system

Abstract

The system according to this embodiment aims to quickly identify and assign the most suitable personnel based on their skills, preferences, and participation period. [Solution] The system according to the embodiment comprises a profile management unit, a matching unit, a support unit, and an experience accumulation unit. The profile management unit registers and manages volunteers' skills, preferences, participation period, and desired schedule. The matching unit identifies the most suitable personnel from the project details and volunteer profiles based on the information managed by the profile management unit. The support unit assists the personnel identified by the matching unit in registering the profiles of new participants. The experience accumulation unit records each participant's activity record and provides feedback for skill improvement based on the information registered by the support unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies have a problem that it is difficult to arrange according to the suitability, hobbies, and participation period of volunteers, and it is difficult to quickly find appropriate personnel for a case.

[0005] The system according to the embodiment aims to quickly identify and arrange optimal personnel according to the skills, hobbies, and participation period of volunteers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a profile management unit, a matching unit, a support unit, and an experience accumulation unit. The profile management unit registers and manages volunteers' skills, preferences, participation period, and desired schedule. The matching unit identifies the most suitable personnel based on the project details and volunteer profiles, using the information managed by the profile management unit. The support unit assists the personnel identified by the matching unit in registering the profiles of new participants. The experience accumulation unit records each participant's activity record and provides feedback for skill improvement, using the information registered by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly identify and assign the most suitable personnel based on the volunteer's skills, preferences, and participation period. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The volunteer matching system according to an embodiment of the present invention is a system that realizes optimal placement of volunteers according to their aptitude, preferences, and participation period. This system includes a profile management system that registers and manages volunteers' skills, preferences, participation period, and desired schedule, and accumulates their activity experience; an automatic matching engine that quickly identifies the most suitable personnel according to the participation period based on the project details and the volunteer's profile; a new participant support function that assists new participants in registering their profiles and flexibly matches them based on the period and desired schedule; and an experience accumulation and short-term response function that records each participant's activity record, provides feedback for skill improvement, and provides placement suitable for short-term projects. For example, it proposes the most suitable projects based on the volunteer's profile and supports them in improving their skills and careers by participating in projects. By using an AI system, volunteer matching can be automated, enabling the identification of personnel quickly and accurately. This makes it possible for volunteer organizations and local communities to efficiently utilize personnel and maximize the effectiveness of their activities. Furthermore, by utilizing AI to collect and analyze data necessary for volunteer activities, flexible volunteer matching can be realized. This makes it possible to realize more effective volunteer activities and volunteer activities with a greater social impact. With the spread of AI, people will be able to concentrate on more advanced and complex tasks, leading to more efficient work performance. This allows the volunteer matching system to achieve optimal personnel placement based on volunteers' aptitudes, preferences, and participation period, maximizing resources through efficient matching. Furthermore, it is expected to promote volunteer growth through accumulated experience.

[0029] The volunteer matching system according to this embodiment comprises a profile management unit, a matching unit, a support unit, and an experience accumulation unit. The profile management unit registers and manages volunteers' skills, preferences, participation period, and desired schedule. For example, the profile management unit registers the technical skills and communication skills that volunteers possess. The profile management unit can also register the volunteers' areas of interest and favorite activities. Furthermore, the profile management unit can register the volunteer's participation period in units such as days, weeks, and months. For example, the profile management unit can register the volunteer's desired schedule as a specific date, day of the week, and time slot. The matching unit identifies the most suitable personnel from the project details and volunteer profiles based on the information managed by the profile management unit. For example, the matching unit uses AI to match the project details with the volunteer's skills and preferences. The matching unit can also quickly identify the most suitable personnel according to the participation period. For example, the matching unit uses real-time search and prioritization to quickly identify personnel. The support unit assists new participants in registering their profiles for personnel identified by the matching unit. The support department guides new participants through the input process when they register their profiles. The support department can also flexibly match volunteers based on duration and preferred dates. For example, the support department adjusts and prioritizes schedules to match volunteers according to their preferences. The experience accumulation department records each participant's activity record based on the information registered by the support department and provides feedback for skill improvement. For example, the experience accumulation department records the number of times volunteers have participated and the results they have achieved. The experience accumulation department can also provide feedback for skill improvement. For example, the experience accumulation department evaluates volunteer skill improvement based on evaluation criteria and provides feedback. This enables the volunteer matching system according to the embodiment to optimally allocate personnel based on volunteers' skills, preferences, participation period, and preferred dates.

[0030] The Profile Management Department registers and manages volunteers' skills, preferences, participation period, and preferred schedules. Specifically, it allows for detailed registration of volunteers' technical and communication skills. This includes professional skills such as programming, design, marketing, and education, as well as soft skills such as leadership, teamwork, and problem-solving abilities. The Profile Management Department also allows volunteers to register their areas of interest and preferred activities. For example, it records in detail areas of interest such as environmental protection, educational support, community activities, and disaster relief. Furthermore, the Profile Management Department allows volunteers to register their participation period in units of days, weeks, or months. This allows for flexible registration according to volunteers' preferences, such as participating in short-term projects or committing to long-term activities. The Profile Management Department also allows volunteers to register their preferred schedules by specific dates, days of the week, and time slots. For example, it allows for detailed schedule management of volunteers who can only participate on weekday evenings or weekends, or who can only participate during specific periods. In this way, the Profile Management Department centrally manages detailed volunteer information, providing a foundation for a smooth subsequent matching process.

[0031] The Matching Department identifies the most suitable candidates based on project details and volunteer profiles, using information managed by the Profile Management Department. Specifically, it uses AI to match project details with volunteers' skills and preferences. The AI ​​analyzes the detailed content of the project using natural language processing technology and compares it with volunteer profile information. For example, if a project requires programming skills, the AI ​​identifies individuals with the relevant skills from the volunteer skill sets. It also considers the volunteer's preferences and areas of interest to make the best match. Furthermore, the Matching Department can quickly identify the most suitable candidates based on the duration of participation. For example, it prioritizes matching volunteers who can work for a short period to short-term projects, and volunteers who can commit for a long period to long-term projects. The Matching Department uses real-time search and prioritization to quickly identify candidates. For example, for urgent projects, it prioritizes searching for and matching volunteers who can respond immediately. In this way, the Matching Department can efficiently and effectively identify the most suitable candidates and contribute to the success of projects.

[0032] The Support Department assists new participants in registering their profiles, based on the information identified by the Matching Department. Specifically, they guide new participants through the input process when registering their profiles. For example, they explain how to sequentially enter the necessary information through online forms or applications and provide checking functions to prevent input errors. The Support Department can also flexibly match volunteers based on their preferred duration and schedule. For example, they propose the most suitable assignments and adjust schedules to fit the volunteer's availability. The Support Department adjusts and prioritizes schedules to match volunteers according to their preferences. For example, if multiple assignments overlap during a specific period, they select and adjust the most suitable assignment based on the volunteer's preferences and priorities. In this way, the Support Department helps volunteers start their activities smoothly and streamlines the matching process. Furthermore, the Support Department provides support for any problems or questions volunteers may encounter during their activities, helping them to continue their work.

[0033] The Experience Accumulation Department records each participant's activity record and provides feedback for skill improvement based on information registered by the Support Department. Specifically, it meticulously records the number of times volunteers have participated and the results they have achieved. For example, it records the content, duration, and deliverables of projects they have participated in and saves this as a volunteer activity history. The Experience Accumulation Department can also provide feedback for skill improvement. For example, it evaluates volunteers' skill improvement based on evaluation criteria and provides specific feedback. This includes success factors and areas for improvement in projects, as well as advice for future skill development. Furthermore, the Experience Accumulation Department can provide information useful for future matching based on volunteer activity records. For example, it can build a database to prioritize matching volunteers with specific skills and experience, supporting efficient matching. In this way, the Experience Accumulation Department can support the growth of volunteers and enhance the overall effectiveness of the system. In addition, the Experience Accumulation Department can establish reward and award systems based on volunteer activity records. For example, it can provide certificates of commendation and benefits to volunteers who have achieved certain results, thereby increasing their motivation. In this way, the Experience Accumulation Department can improve volunteer motivation and encourage continued participation.

[0034] The Experience Accumulation Department includes a Short-Term Response Department that provides suitable placements for short-term projects. The Short-Term Response Department assigns appropriate volunteers to projects lasting less than a week or those requiring urgent response. Based on volunteer skills, preferences, and participation period, the Short-Term Response Department can quickly identify the most suitable personnel for short-term projects. For example, it uses AI to match the content of short-term projects with volunteer profiles. Furthermore, the Short-Term Response Department can also assign volunteers based on their past activity records. For instance, it refers to volunteers' past activity records to identify volunteers with the skills and experience suitable for short-term projects. This enables suitable placements even for short-term projects.

[0035] The Profile Management Department can accumulate volunteer activity experience. For example, it records projects volunteers have participated in and skills they have acquired. Furthermore, the Profile Management Department can use volunteer activity records to facilitate more appropriate matching. For instance, it can refer to a volunteer's past activity experience and suggest suitable projects. This accumulation of volunteer activity experience enables more accurate matching.

[0036] The matching department can quickly identify the most suitable candidates based on their participation period. For example, it uses real-time search and prioritization to identify the best candidates for each duration. Furthermore, the matching department can also use AI to quickly identify the best candidates based on their participation period. For instance, it uses an AI model to identify the best candidates based on their participation period. This enables efficient matching by quickly identifying the most suitable candidates based on their participation period.

[0037] The support department can flexibly match volunteers based on duration and preferred dates. For example, the support department can adjust schedules and prioritize tasks to match volunteers according to their preferences. Furthermore, the support department can use AI to perform flexible matching based on duration and preferred dates. For instance, the support department can use an AI model to perform flexible matching based on duration and preferred dates. This flexible matching based on duration and preferred dates allows for matching volunteers according to their preferences.

[0038] The experience accumulation unit can provide feedback on skill improvement. For example, the experience accumulation unit evaluates volunteers' skill improvement based on evaluation criteria and provides feedback. Furthermore, the experience accumulation unit can also provide feedback on skill improvement using AI. For example, the experience accumulation unit can use an AI model to evaluate volunteers' skill improvement and provide feedback. This facilitates the growth of volunteers by providing feedback on skill improvement.

[0039] The profile management system can provide optimal profile input support by referring to a volunteer's past activity history during profile management. For example, the profile management system can automatically input relevant skills and experience based on the types of activities a volunteer has participated in in the past. It can also automatically complete input fields based on information previously entered by the volunteer. Furthermore, the profile management system can suggest frequently used keywords from the volunteer's past activity history. This enables optimal profile input support by referring to the volunteer's past activity history.

[0040] The profile management system can adjust the level of detail in input fields according to the volunteer's skill level during profile management. For example, it can display only basic input fields for novice volunteers, omitting detailed input. It can also add fields for more detailed skills and experience for advanced volunteers. Furthermore, it can offer an option to display detailed input fields as needed for intermediate volunteers. This allows for more appropriate profile completion by adjusting the level of detail in input fields according to the volunteer's skill level.

[0041] The profile management system can prioritize displaying relevant activity information by considering the volunteer's geographical location during profile management. For example, it can prioritize displaying nearby activity information based on the volunteer's current location. It can also refer to the volunteer's past location information to display activity information in frequently visited areas. Furthermore, based on the volunteer's location information, the profile management system can suggest activity information that takes into account transportation methods and travel time. This allows for the priority display of relevant activity information by considering the volunteer's geographical location.

[0042] The profile management system can analyze volunteers' social media activity during profile management and automatically supplement relevant skills and preferences. For example, it can extract relevant skills and interests from volunteers' social media posts and reflect them in their profiles. It can also analyze volunteers' social media following and liking history to estimate their preferences. Furthermore, it can suggest appropriate skills and experience based on the frequency and content of volunteers' social media activity. This allows for the automatic supplementation of relevant skills and preferences by analyzing volunteers' social media activity.

[0043] The matching department can prioritize matching based on the urgency of each project. For example, it will prioritize matching high-urgency projects. It can also flexibly match less urgent projects to volunteers according to their schedules. Furthermore, it can appropriately match moderately urgent projects to volunteers based on their skills and experience. This allows for faster matching by prioritizing projects based on their urgency.

[0044] The matching unit can apply the most suitable matching algorithm by referring to the volunteer's past matching history during the matching process. For example, the matching unit can prioritize proposing projects with a high success rate based on the volunteer's past matching history. It can also analyze the volunteer's past matching history and propose projects that match their appropriate skills and experience. Furthermore, the matching unit can select the most suitable matching algorithm based on the volunteer's past matching history. This makes it possible to apply the most suitable matching algorithm by referring to the volunteer's past matching history.

[0045] The matching unit can identify the most suitable volunteers by considering the geographical distribution of the projects during the matching process. For example, the matching unit prioritizes matching volunteers who are close to the location of the project. Furthermore, the matching unit can identify volunteers who take into account transportation methods and travel time, considering the geographical distribution of the projects. In addition, the matching unit can identify the most suitable volunteers based on the geographical distribution of the projects. This makes it possible to identify the most suitable volunteers by considering the geographical distribution of the projects.

[0046] The matching unit can improve the accuracy of matching by referring to relevant literature for the project during the matching process. For example, the matching unit can refer to relevant literature for the project to identify volunteers with the necessary skills and experience. Furthermore, the matching unit can apply the optimal matching algorithm based on the relevant literature. In addition, the matching unit can analyze the relevant literature for the project to further improve the accuracy of matching. This makes it possible to improve the accuracy of matching by referring to relevant literature for the project.

[0047] The support department can select the most suitable support method by referring to the past activity history of new participants during support sessions. For example, the support department can propose an appropriate support method based on the new participant's past activity history. Furthermore, the support department can select a support method with the necessary skills and experience based on the new participant's past activity history. In addition, the support department can analyze the new participant's past activity history and provide the most suitable support method. This makes it possible to select the most suitable support method by referring to the new participant's past activity history.

[0048] The support department can customize the support provided to new participants according to their skill level. For example, the support department can provide basic support to beginner participants, and more detailed support to advanced participants. Furthermore, the support department can offer the option of providing more detailed support to intermediate participants as needed. This allows for more appropriate support by customizing the support content according to the skill level of each new participant.

[0049] The support department can select the most suitable support method for new participants by considering their geographical location. For example, the support department can prioritize providing support methods in nearby areas based on the new participant's current location. It can also refer to the new participant's past location information and provide support methods for frequently visited areas. Furthermore, the support department can propose support methods that take into account transportation and travel time based on the new participant's location information. This makes it possible to select the most suitable support method by considering the new participant's geographical location.

[0050] The support department can analyze the social media activity of new participants to supplement the support provided. For example, the support department can extract relevant skills and interests from the new participant's social media posts and reflect them in the support content. The support department can also analyze the new participant's social media following and liking history to estimate their preferences. Furthermore, the support department can propose appropriate support content based on the frequency and content of the new participant's social media activity. In this way, analyzing the new participant's social media activity makes it possible to supplement the support content.

[0051] The experience accumulation unit can provide optimal feedback by referring to the volunteer's past activity record during the experience accumulation process. For example, the experience accumulation unit can provide appropriate feedback based on the volunteer's past activity record. Furthermore, the experience accumulation unit can provide feedback on the necessary skills and experience based on the volunteer's past activity record. In addition, the experience accumulation unit can analyze the volunteer's past activity record to provide optimal feedback. This makes it possible to provide optimal feedback by referring to the volunteer's past activity record.

[0052] The experience accumulation unit can customize the feedback content according to the volunteer's skill level during the experience accumulation process. For example, it provides basic feedback to novice volunteers. It can also provide detailed feedback to advanced volunteers. Furthermore, it can offer the option to provide detailed feedback to intermediate volunteers as needed. This allows for more appropriate feedback by customizing the feedback content according to the volunteer's skill level.

[0053] The experience accumulation unit can provide optimal feedback by considering the volunteer's geographical location information during the experience accumulation process. For example, it can prioritize providing feedback methods for nearby areas based on the volunteer's current location. It can also refer to the volunteer's past location information and provide feedback methods for frequently visited areas. Furthermore, based on the volunteer's location information, the experience accumulation unit can propose feedback methods that take into account transportation methods and travel time. This makes it possible to provide optimal feedback by considering the volunteer's geographical location information.

[0054] The experience accumulation unit can analyze volunteers' social media activities during the experience accumulation process to supplement feedback. For example, it can extract relevant skills and interests from volunteers' social media posts and reflect them in the feedback. It can also analyze volunteers' social media following and liking history to estimate their preferences. Furthermore, it can suggest appropriate feedback based on the frequency and content of volunteers' social media activity. In this way, analyzing volunteers' social media activities makes it possible to supplement feedback.

[0055] The Short-Term Response Department can select the optimal placement method by referring to a volunteer's past short-term assignment history during short-term assignments. For example, the Short-Term Response Department can propose an appropriate placement method based on a volunteer's past short-term assignment history. Furthermore, the Short-Term Response Department can select a placement method with the necessary skills and experience based on a volunteer's past short-term assignment history. In addition, the Short-Term Response Department can analyze a volunteer's past short-term assignment history and provide the optimal placement method. This makes it possible to select the optimal placement method by referring to a volunteer's past short-term assignment history.

[0056] The Short-Term Support Department can customize volunteer assignments according to their skill level during short-term assignments. For example, it can provide basic assignments to novice volunteers, and more detailed assignments to more experienced volunteers. Furthermore, it can offer the option of providing more detailed assignments to intermediate-level volunteers as needed. This allows for more appropriate short-term assignments by customizing assignments according to volunteer skill levels.

[0057] The short-term response department can select the optimal deployment method for volunteers during short-term deployments, taking into account their geographical location. For example, it can prioritize providing nearby short-term assignments based on the volunteer's current location. It can also refer to the volunteer's past location information and provide short-term assignments in areas they frequently visit. Furthermore, based on the volunteer's location information, the short-term response department can propose short-term assignments that take into account transportation methods and travel time. This makes it possible to select the optimal deployment method by considering the volunteer's geographical location.

[0058] The Short-Term Support Department can analyze volunteers' social media activity during short-term assignments to supplement placement decisions. For example, it can extract relevant skills and interests from volunteers' social media posts and reflect them in placement decisions. It can also analyze volunteers' social media following and liking history to estimate their preferences. Furthermore, it can propose appropriate placements based on the frequency and content of volunteers' social media activity. This allows for the supplementation of placement decisions by analyzing volunteers' social media activity.

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

[0060] The volunteer matching system can make optimal matches by taking into account the health status of volunteers. For example, the profile management department can register volunteers' health information, and the matching department can propose assignments that match their health status. Volunteers in good health can be offered assignments that require physical strength, while volunteers with unstable health status can be offered light work or remote support assignments. Furthermore, the system can adjust volunteer activity hours based on their health status. This enables optimal matching according to the health status of volunteers.

[0061] The volunteer matching system can make optimal matches by taking into account volunteers' hobbies and skills. For example, the profile management department can register volunteers' hobbies and skills, and the matching department can propose projects based on this information. For example, a volunteer whose hobby is cooking can be offered a cooking class support project, and a volunteer whose skill is handicrafts can be offered a handicrafts class support project. Furthermore, it can also propose projects that help volunteers improve their skills based on their hobbies and skills. This makes it possible to make optimal matches according to the volunteers' hobbies and skills.

[0062] The volunteer matching system can make optimal matches by taking into account the family structure of volunteers. For example, the profile management department can register the family structure of volunteers, and the matching department can propose assignments that suit that family structure. For example, volunteers with young children can be offered assignments that they can participate in with their children, and volunteers providing care can be offered assignments that they can participate in between caregiving duties. Furthermore, the system can adjust the volunteer's activity time based on their family structure. This makes it possible to make optimal matches that suit the family structure of volunteers.

[0063] The volunteer matching system can make optimal matches by taking into account volunteers' educational and professional backgrounds. For example, the profile management department can register volunteers' educational and professional backgrounds, and the matching department can propose projects based on this information. Volunteers with high educational backgrounds can be offered education-related projects, while volunteers with extensive professional experience can be offered projects that utilize their specialized knowledge. Furthermore, projects that help volunteers advance their careers can also be proposed based on their educational and professional backgrounds. This enables optimal matching according to the educational and professional backgrounds of volunteers.

[0064] The volunteer matching system can make optimal matches by taking into account volunteers' language skills. For example, the profile management department can register volunteers' language skills, and the matching department can propose assignments based on this information. Volunteers who are multilingual can be offered international exchange or interpretation assignments, while volunteers fluent in a specific language can be offered assignments that utilize that language. Furthermore, assignments that help volunteers improve their skills can also be proposed based on their language skills. This enables optimal matching according to the volunteer's language skills.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The Profile Management Department registers and manages volunteers' skills, preferences, participation period, and desired schedules. For example, volunteers' technical and communication skills, areas of interest and favorite activities, and participation period are registered in units of days, weeks, or months, while their desired schedules are registered as specific dates, days of the week, and time slots. Step 2: The matching department identifies the most suitable candidates based on the project details and volunteer profiles, using information managed by the profile management department. For example, AI is used to match project details with volunteer skills and preferences, quickly identifying the most suitable candidates based on the duration of participation. Step 3: The support team assists new participants in registering their profiles, based on the information provided by the matching team. For example, they guide new participants through the input process and flexibly match them based on their desired duration and schedule. Step 4: The Experience Accumulation Department records each participant's activity record and provides feedback for skill improvement based on the information registered by the Support Department. For example, it records the number of times volunteers participate and the results achieved, evaluates skill improvement based on evaluation criteria, and provides feedback.

[0067] (Example of form 2) The volunteer matching system according to an embodiment of the present invention is a system that realizes optimal placement of volunteers according to their aptitude, preferences, and participation period. This system includes a profile management system that registers and manages volunteers' skills, preferences, participation period, and desired schedule, and accumulates their activity experience; an automatic matching engine that quickly identifies the most suitable personnel according to the participation period based on the project details and the volunteer's profile; a new participant support function that assists new participants in registering their profiles and flexibly matches them based on the period and desired schedule; and an experience accumulation and short-term response function that records each participant's activity record, provides feedback for skill improvement, and provides placement suitable for short-term projects. For example, it proposes the most suitable projects based on the volunteer's profile and supports them in improving their skills and careers by participating in projects. By using an AI system, volunteer matching can be automated, enabling the identification of personnel quickly and accurately. This makes it possible for volunteer organizations and local communities to efficiently utilize personnel and maximize the effectiveness of their activities. Furthermore, by utilizing AI to collect and analyze data necessary for volunteer activities, flexible volunteer matching can be realized. This makes it possible to realize more effective volunteer activities and volunteer activities with a greater social impact. With the spread of AI, people will be able to concentrate on more advanced and complex tasks, leading to more efficient work performance. This allows the volunteer matching system to achieve optimal personnel placement based on volunteers' aptitudes, preferences, and participation period, maximizing resources through efficient matching. Furthermore, it is expected to promote volunteer growth through accumulated experience.

[0068] The volunteer matching system according to this embodiment comprises a profile management unit, a matching unit, a support unit, and an experience accumulation unit. The profile management unit registers and manages volunteers' skills, preferences, participation period, and desired schedule. For example, the profile management unit registers the technical skills and communication skills that volunteers possess. The profile management unit can also register the volunteers' areas of interest and favorite activities. Furthermore, the profile management unit can register the volunteer's participation period in units such as days, weeks, and months. For example, the profile management unit can register the volunteer's desired schedule as a specific date, day of the week, and time slot. The matching unit identifies the most suitable personnel from the project details and volunteer profiles based on the information managed by the profile management unit. For example, the matching unit uses AI to match the project details with the volunteer's skills and preferences. The matching unit can also quickly identify the most suitable personnel according to the participation period. For example, the matching unit uses real-time search and prioritization to quickly identify personnel. The support unit assists new participants in registering their profiles for personnel identified by the matching unit. The support department guides new participants through the input process when they register their profiles. The support department can also flexibly match volunteers based on duration and preferred dates. For example, the support department adjusts and prioritizes schedules to match volunteers according to their preferences. The experience accumulation department records each participant's activity record based on the information registered by the support department and provides feedback for skill improvement. For example, the experience accumulation department records the number of times volunteers have participated and the results they have achieved. The experience accumulation department can also provide feedback for skill improvement. For example, the experience accumulation department evaluates volunteer skill improvement based on evaluation criteria and provides feedback. This enables the volunteer matching system according to the embodiment to optimally allocate personnel based on volunteers' skills, preferences, participation period, and preferred dates.

[0069] The Profile Management Department registers and manages volunteers' skills, preferences, participation period, and preferred schedules. Specifically, it allows for detailed registration of volunteers' technical and communication skills. This includes professional skills such as programming, design, marketing, and education, as well as soft skills such as leadership, teamwork, and problem-solving abilities. The Profile Management Department also allows volunteers to register their areas of interest and preferred activities. For example, it records in detail areas of interest such as environmental protection, educational support, community activities, and disaster relief. Furthermore, the Profile Management Department allows volunteers to register their participation period in units of days, weeks, or months. This allows for flexible registration according to volunteers' preferences, such as participating in short-term projects or committing to long-term activities. The Profile Management Department also allows volunteers to register their preferred schedules by specific dates, days of the week, and time slots. For example, it allows for detailed schedule management of volunteers who can only participate on weekday evenings or weekends, or who can only participate during specific periods. In this way, the Profile Management Department centrally manages detailed volunteer information, providing a foundation for a smooth subsequent matching process.

[0070] The Matching Department identifies the most suitable candidates based on project details and volunteer profiles, using information managed by the Profile Management Department. Specifically, it uses AI to match project details with volunteers' skills and preferences. The AI ​​analyzes the detailed content of the project using natural language processing technology and compares it with volunteer profile information. For example, if a project requires programming skills, the AI ​​identifies individuals with the relevant skills from the volunteer skill sets. It also considers the volunteer's preferences and areas of interest to make the best match. Furthermore, the Matching Department can quickly identify the most suitable candidates based on the duration of participation. For example, it prioritizes matching volunteers who can work for a short period to short-term projects, and volunteers who can commit for a long period to long-term projects. The Matching Department uses real-time search and prioritization to quickly identify candidates. For example, for urgent projects, it prioritizes searching for and matching volunteers who can respond immediately. In this way, the Matching Department can efficiently and effectively identify the most suitable candidates and contribute to the success of projects.

[0071] The Support Department assists new participants in registering their profiles, based on the information identified by the Matching Department. Specifically, they guide new participants through the input process when registering their profiles. For example, they explain how to sequentially enter the necessary information through online forms or applications and provide checking functions to prevent input errors. The Support Department can also flexibly match volunteers based on their preferred duration and schedule. For example, they propose the most suitable assignments and adjust schedules to fit the volunteer's availability. The Support Department adjusts and prioritizes schedules to match volunteers according to their preferences. For example, if multiple assignments overlap during a specific period, they select and adjust the most suitable assignment based on the volunteer's preferences and priorities. In this way, the Support Department helps volunteers start their activities smoothly and streamlines the matching process. Furthermore, the Support Department provides support for any problems or questions volunteers may encounter during their activities, helping them to continue their work.

[0072] The Experience Accumulation Department records each participant's activity record and provides feedback for skill improvement based on information registered by the Support Department. Specifically, it meticulously records the number of times volunteers have participated and the results they have achieved. For example, it records the content, duration, and deliverables of projects they have participated in and saves this as a volunteer activity history. The Experience Accumulation Department can also provide feedback for skill improvement. For example, it evaluates volunteers' skill improvement based on evaluation criteria and provides specific feedback. This includes success factors and areas for improvement in projects, as well as advice for future skill development. Furthermore, the Experience Accumulation Department can provide information useful for future matching based on volunteer activity records. For example, it can build a database to prioritize matching volunteers with specific skills and experience, supporting efficient matching. In this way, the Experience Accumulation Department can support the growth of volunteers and enhance the overall effectiveness of the system. In addition, the Experience Accumulation Department can establish reward and award systems based on volunteer activity records. For example, it can provide certificates of commendation and benefits to volunteers who have achieved certain results, thereby increasing their motivation. In this way, the Experience Accumulation Department can improve volunteer motivation and encourage continued participation.

[0073] The Experience Accumulation Department includes a Short-Term Response Department that provides suitable placements for short-term projects. The Short-Term Response Department assigns appropriate volunteers to projects lasting less than a week or those requiring urgent response. Based on volunteer skills, preferences, and participation period, the Short-Term Response Department can quickly identify the most suitable personnel for short-term projects. For example, it uses AI to match the content of short-term projects with volunteer profiles. Furthermore, the Short-Term Response Department can also assign volunteers based on their past activity records. For instance, it refers to volunteers' past activity records to identify volunteers with the skills and experience suitable for short-term projects. This enables suitable placements even for short-term projects.

[0074] The Profile Management Department can accumulate volunteer activity experience. For example, it records projects volunteers have participated in and skills they have acquired. Furthermore, the Profile Management Department can use volunteer activity records to facilitate more appropriate matching. For instance, it can refer to a volunteer's past activity experience and suggest suitable projects. This accumulation of volunteer activity experience enables more accurate matching.

[0075] The matching department can quickly identify the most suitable candidates based on their participation period. For example, it uses real-time search and prioritization to identify the best candidates for each duration. Furthermore, the matching department can also use AI to quickly identify the best candidates based on their participation period. For instance, it uses an AI model to identify the best candidates based on their participation period. This enables efficient matching by quickly identifying the most suitable candidates based on their participation period.

[0076] The support department can flexibly match volunteers based on duration and preferred dates. For example, the support department can adjust schedules and prioritize tasks to match volunteers according to their preferences. Furthermore, the support department can use AI to perform flexible matching based on duration and preferred dates. For instance, the support department can use an AI model to perform flexible matching based on duration and preferred dates. This flexible matching based on duration and preferred dates allows for matching volunteers according to their preferences.

[0077] The experience accumulation unit can provide feedback on skill improvement. For example, the experience accumulation unit evaluates volunteers' skill improvement based on evaluation criteria and provides feedback. Furthermore, the experience accumulation unit can also provide feedback on skill improvement using AI. For example, the experience accumulation unit can use an AI model to evaluate volunteers' skill improvement and provide feedback. This facilitates the growth of volunteers by providing feedback on skill improvement.

[0078] The profile management unit can estimate the user's emotions and adjust the profile input method based on the estimated emotions. For example, if the user is stressed, the profile management unit can provide a simple interface and minimize the input steps. If the user is relaxed, the profile management unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the profile management unit can prioritize voice input to allow for quick profile input. This allows for more appropriate profile input by adjusting the profile input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The profile management system can provide optimal profile input support by referring to a volunteer's past activity history during profile management. For example, the profile management system can automatically input relevant skills and experience based on the types of activities a volunteer has participated in in the past. It can also automatically complete input fields based on information previously entered by the volunteer. Furthermore, the profile management system can suggest frequently used keywords from the volunteer's past activity history. This enables optimal profile input support by referring to the volunteer's past activity history.

[0080] The profile management system can adjust the level of detail in input fields according to the volunteer's skill level during profile management. For example, it can display only basic input fields for novice volunteers, omitting detailed input. It can also add fields for more detailed skills and experience for advanced volunteers. Furthermore, it can offer an option to display detailed input fields as needed for intermediate volunteers. This allows for more appropriate profile completion by adjusting the level of detail in input fields according to the volunteer's skill level.

[0081] The profile management unit can estimate the user's emotions and adjust how the profile is displayed based on those emotions. For example, if the user is stressed, the profile management unit can provide a calming color scheme interface to reduce visual stress. If the user is enjoying themselves, the profile management unit can provide a bright color scheme interface to make the input process more enjoyable. Furthermore, if the user is tired, the profile management unit can provide a simple and highly visible interface to facilitate the input process. This allows for a more appropriate profile display by adjusting how the profile is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The profile management system can prioritize displaying relevant activity information by considering the volunteer's geographical location during profile management. For example, it can prioritize displaying nearby activity information based on the volunteer's current location. It can also refer to the volunteer's past location information to display activity information in frequently visited areas. Furthermore, based on the volunteer's location information, the profile management system can suggest activity information that takes into account transportation methods and travel time. This allows for the priority display of relevant activity information by considering the volunteer's geographical location.

[0083] The profile management system can analyze volunteers' social media activity during profile management and automatically supplement relevant skills and preferences. For example, it can extract relevant skills and interests from volunteers' social media posts and reflect them in their profiles. It can also analyze volunteers' social media following and liking history to estimate their preferences. Furthermore, it can suggest appropriate skills and experience based on the frequency and content of volunteers' social media activity. This allows for the automatic supplementation of relevant skills and preferences by analyzing volunteers' social media activity.

[0084] The matching unit can estimate the user's emotions and adjust the matching criteria based on those emotions. For example, if the user is stressed, the matching unit will prioritize matching them with easy tasks. Conversely, if the user is relaxed, the matching unit can suggest more challenging tasks. Furthermore, if the user is in a hurry, the matching unit can prioritize matching them with tasks that can be handled quickly. By adjusting the matching criteria according to the user's emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The matching department can prioritize matching based on the urgency of each project. For example, it will prioritize matching high-urgency projects. It can also flexibly match less urgent projects to volunteers according to their schedules. Furthermore, it can appropriately match moderately urgent projects to volunteers based on their skills and experience. This allows for faster matching by prioritizing projects based on their urgency.

[0086] The matching unit can apply the most suitable matching algorithm by referring to the volunteer's past matching history during the matching process. For example, the matching unit can prioritize proposing projects with a high success rate based on the volunteer's past matching history. It can also analyze the volunteer's past matching history and propose projects that match their appropriate skills and experience. Furthermore, the matching unit can select the most suitable matching algorithm based on the volunteer's past matching history. This makes it possible to apply the most suitable matching algorithm by referring to the volunteer's past matching history.

[0087] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated emotions. For example, if the user is nervous, the matching unit can provide a simple and highly visible display method. If the user is relaxed, the matching unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the matching unit can provide a concise display method. By adjusting the display method of the matching results according to the user's emotions, it becomes possible to display more appropriate matching results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The matching unit can identify the most suitable volunteers by considering the geographical distribution of the projects during the matching process. For example, the matching unit prioritizes matching volunteers who are close to the location of the project. Furthermore, the matching unit can identify volunteers who take into account transportation methods and travel time, considering the geographical distribution of the projects. In addition, the matching unit can identify the most suitable volunteers based on the geographical distribution of the projects. This makes it possible to identify the most suitable volunteers by considering the geographical distribution of the projects.

[0089] The matching unit can improve the accuracy of matching by referring to relevant literature for the project during the matching process. For example, the matching unit can refer to relevant literature for the project to identify volunteers with the necessary skills and experience. Furthermore, the matching unit can apply the optimal matching algorithm based on the relevant literature. In addition, the matching unit can analyze the relevant literature for the project to further improve the accuracy of matching. This makes it possible to improve the accuracy of matching by referring to relevant literature for the project.

[0090] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is stressed, the support unit can provide simple support methods and minimize the steps involved. If the user is relaxed, the support unit can also provide detailed support options and suggest customizable support methods. Furthermore, if the user is in a hurry, the support unit can provide support methods that allow for a quick response. This allows for more appropriate support by adjusting the support method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The support department can select the most suitable support method by referring to the past activity history of new participants during support sessions. For example, the support department can propose an appropriate support method based on the new participant's past activity history. Furthermore, the support department can select a support method with the necessary skills and experience based on the new participant's past activity history. In addition, the support department can analyze the new participant's past activity history and provide the most suitable support method. This makes it possible to select the most suitable support method by referring to the new participant's past activity history.

[0092] The support department can customize the support provided to new participants according to their skill level. For example, the support department can provide basic support to beginner participants, and more detailed support to advanced participants. Furthermore, the support department can offer the option of providing more detailed support to intermediate participants as needed. This allows for more appropriate support by customizing the support content according to the skill level of each new participant.

[0093] The support unit can estimate the user's emotions and prioritize support based on those emotions. For example, if the user is stressed, the support unit can provide a quick response. If the user is relaxed, the support unit can also provide more detailed support options. Furthermore, if the user is in a hurry, the support unit can prioritize providing a quick response. This allows for faster support by prioritizing support according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The support department can select the most suitable support method for new participants by considering their geographical location. For example, the support department can prioritize providing support methods in nearby areas based on the new participant's current location. It can also refer to the new participant's past location information and provide support methods for frequently visited areas. Furthermore, the support department can propose support methods that take into account transportation and travel time based on the new participant's location information. This makes it possible to select the most suitable support method by considering the new participant's geographical location.

[0095] The support department can analyze the social media activity of new participants to supplement the support provided. For example, the support department can extract relevant skills and interests from the new participant's social media posts and reflect them in the support content. The support department can also analyze the new participant's social media following and liking history to estimate their preferences. Furthermore, the support department can propose appropriate support content based on the frequency and content of the new participant's social media activity. In this way, analyzing the new participant's social media activity makes it possible to supplement the support content.

[0096] The experience accumulator can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is stressed, the experience accumulator can provide a simple feedback method and minimize the steps involved. If the user is relaxed, the experience accumulator can also provide detailed feedback options and suggest a customizable feedback method. Furthermore, if the user is in a hurry, the experience accumulator can provide a feedback method that allows for a quick response. This allows for more appropriate feedback by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The experience accumulation unit can provide optimal feedback by referring to the volunteer's past activity record during the experience accumulation process. For example, the experience accumulation unit can provide appropriate feedback based on the volunteer's past activity record. Furthermore, the experience accumulation unit can provide feedback on the necessary skills and experience based on the volunteer's past activity record. In addition, the experience accumulation unit can analyze the volunteer's past activity record to provide optimal feedback. This makes it possible to provide optimal feedback by referring to the volunteer's past activity record.

[0098] The experience accumulation unit can customize the feedback content according to the volunteer's skill level during the experience accumulation process. For example, it provides basic feedback to novice volunteers. It can also provide detailed feedback to advanced volunteers. Furthermore, it can offer the option to provide detailed feedback to intermediate volunteers as needed. This allows for more appropriate feedback by customizing the feedback content according to the volunteer's skill level.

[0099] The experience accumulator can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is nervous, the experience accumulator can provide a quick response method. It can also provide more detailed feedback options if the user is relaxed. Furthermore, if the user is in a hurry, the experience accumulator can prioritize a quick response method. This allows for faster feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The experience accumulation unit can provide optimal feedback by considering the volunteer's geographical location information during the experience accumulation process. For example, it can prioritize providing feedback methods for nearby areas based on the volunteer's current location. It can also refer to the volunteer's past location information and provide feedback methods for frequently visited areas. Furthermore, based on the volunteer's location information, the experience accumulation unit can propose feedback methods that take into account transportation methods and travel time. This makes it possible to provide optimal feedback by considering the volunteer's geographical location information.

[0101] The experience accumulation unit can analyze volunteers' social media activities during the experience accumulation process to supplement feedback. For example, it can extract relevant skills and interests from volunteers' social media posts and reflect them in the feedback. It can also analyze volunteers' social media following and liking history to estimate their preferences. Furthermore, it can suggest appropriate feedback based on the frequency and content of volunteers' social media activity. In this way, analyzing volunteers' social media activities makes it possible to supplement feedback.

[0102] The short-term response unit can estimate the user's emotions and adjust the allocation of short-term tasks based on those emotions. For example, if the user is stressed, the short-term response unit will prioritize assigning easy short-term tasks. Conversely, if the user is relaxed, the short-term response unit can suggest challenging short-term tasks. Furthermore, if the user is in a hurry, the short-term response unit can prioritize assigning short-term tasks that can be handled quickly. This allows for more appropriate short-term task allocation by adjusting the allocation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The Short-Term Response Department can select the optimal placement method by referring to a volunteer's past short-term assignment history during short-term assignments. For example, the Short-Term Response Department can propose an appropriate placement method based on a volunteer's past short-term assignment history. Furthermore, the Short-Term Response Department can select a placement method with the necessary skills and experience based on a volunteer's past short-term assignment history. In addition, the Short-Term Response Department can analyze a volunteer's past short-term assignment history and provide the optimal placement method. This makes it possible to select the optimal placement method by referring to a volunteer's past short-term assignment history.

[0104] The Short-Term Support Department can customize volunteer assignments according to their skill level during short-term assignments. For example, it can provide basic assignments to novice volunteers, and more detailed assignments to more experienced volunteers. Furthermore, it can offer the option of providing more detailed assignments to intermediate-level volunteers as needed. This allows for more appropriate short-term assignments by customizing assignments according to volunteer skill levels.

[0105] The short-term response unit can estimate the user's emotions and prioritize short-term tasks based on those emotions. For example, if the user is stressed, the short-term response unit will prioritize providing short-term tasks that can be handled quickly. If the user is relaxed, the short-term response unit can also provide detailed short-term task options. Furthermore, if the user is in a hurry, the short-term response unit can also prioritize providing short-term tasks that can be handled quickly. This allows for faster allocation of short-term tasks by prioritizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The short-term response department can select the optimal deployment method for volunteers during short-term deployments, taking into account their geographical location. For example, it can prioritize providing nearby short-term assignments based on the volunteer's current location. It can also refer to the volunteer's past location information and provide short-term assignments in areas they frequently visit. Furthermore, based on the volunteer's location information, the short-term response department can propose short-term assignments that take into account transportation methods and travel time. This makes it possible to select the optimal deployment method by considering the volunteer's geographical location.

[0107] The Short-Term Support Department can analyze volunteers' social media activity during short-term assignments to supplement placement decisions. For example, it can extract relevant skills and interests from volunteers' social media posts and reflect them in placement decisions. It can also analyze volunteers' social media following and liking history to estimate their preferences. Furthermore, it can propose appropriate placements based on the frequency and content of volunteers' social media activity. This allows for the supplementation of placement decisions by analyzing volunteers' social media activity.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The volunteer matching system can make optimal matches by taking into account the health status of volunteers. For example, the profile management department can register volunteers' health information, and the matching department can propose assignments that match their health status. Volunteers in good health can be offered assignments that require physical strength, while volunteers with unstable health status can be offered light work or remote support assignments. Furthermore, the system can adjust volunteer activity hours based on their health status. This enables optimal matching according to the health status of volunteers.

[0110] The volunteer matching system can make optimal matches by taking into account volunteers' hobbies and skills. For example, the profile management department can register volunteers' hobbies and skills, and the matching department can propose projects based on this information. For example, a volunteer whose hobby is cooking can be offered a cooking class support project, and a volunteer whose skill is handicrafts can be offered a handicrafts class support project. Furthermore, it can also propose projects that help volunteers improve their skills based on their hobbies and skills. This makes it possible to make optimal matches according to the volunteers' hobbies and skills.

[0111] The volunteer matching system can make optimal matches by taking into account the family structure of volunteers. For example, the profile management department can register the family structure of volunteers, and the matching department can propose assignments that suit that family structure. For example, volunteers with young children can be offered assignments that they can participate in with their children, and volunteers providing care can be offered assignments that they can participate in between caregiving duties. Furthermore, the system can adjust the volunteer's activity time based on their family structure. This makes it possible to make optimal matches that suit the family structure of volunteers.

[0112] The volunteer matching system can make optimal matches by taking into account volunteers' educational and professional backgrounds. For example, the profile management department can register volunteers' educational and professional backgrounds, and the matching department can propose projects based on this information. Volunteers with high educational backgrounds can be offered education-related projects, while volunteers with extensive professional experience can be offered projects that utilize their specialized knowledge. Furthermore, projects that help volunteers advance their careers can also be proposed based on their educational and professional backgrounds. This enables optimal matching according to the educational and professional backgrounds of volunteers.

[0113] The volunteer matching system can make optimal matches by taking into account volunteers' language skills. For example, the profile management department can register volunteers' language skills, and the matching department can propose assignments based on this information. Volunteers who are multilingual can be offered international exchange or interpretation assignments, while volunteers fluent in a specific language can be offered assignments that utilize that language. Furthermore, assignments that help volunteers improve their skills can also be proposed based on their language skills. This enables optimal matching according to the volunteer's language skills.

[0114] The volunteer matching system can estimate volunteers' emotions and make optimal matches based on those estimates. For example, if a volunteer is feeling stressed, the matching system can suggest relaxing assignments; if a volunteer is relaxed, it can suggest challenging assignments. Furthermore, if a volunteer is in a hurry, it can suggest assignments that can be handled quickly. This enables optimal matching tailored to the volunteer's emotional state.

[0115] The volunteer matching system can estimate a volunteer's emotions and adjust the profile input method based on those estimates. For example, if a volunteer is stressed, the profile management section can provide a simple interface and minimize the input steps. If the volunteer is relaxed, it can offer detailed input options and suggest a customizable input method. Furthermore, if a volunteer is in a hurry, it can prioritize voice input to allow for quick profile completion. This enables optimal profile input tailored to the volunteer's emotions.

[0116] The volunteer matching system can estimate the volunteer's emotions and adjust how the matching results are displayed based on those estimates. For example, if a volunteer is nervous, the matching unit can provide a simple and easy-to-read display; if a volunteer is relaxed, it can provide a display with more detailed information. Furthermore, if a volunteer is in a hurry, it can provide a concise display. This allows for the display of optimal matching results tailored to the volunteer's emotions.

[0117] The volunteer matching system can estimate a volunteer's emotions and adjust the feedback method based on those estimates. For example, if a volunteer is stressed, the experience accumulating unit can provide a simple feedback method with minimal steps. If the volunteer is relaxed, it can offer detailed feedback options and suggest a customizable feedback method. Furthermore, if a volunteer is in a hurry, it can provide a feedback method that allows for a quick response. This enables optimal feedback tailored to the volunteer's emotions.

[0118] The volunteer matching system can estimate volunteers' emotions and adjust how short-term assignments are placed based on those estimates. For example, the short-term response department can prioritize assigning easy short-term assignments to volunteers who are stressed, and suggest challenging assignments to volunteers who are relaxed. Furthermore, if a volunteer is in a hurry, it can prioritize assigning short-term assignments that can be handled quickly. This makes it possible to assign short-term assignments optimally according to the volunteer's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The Profile Management Department registers and manages volunteers' skills, preferences, participation period, and desired schedules. For example, volunteers' technical and communication skills, areas of interest and favorite activities, and participation period are registered in units of days, weeks, or months, while their desired schedules are registered as specific dates, days of the week, and time slots. Step 2: The matching department identifies the most suitable candidates based on the project details and volunteer profiles, using information managed by the profile management department. For example, AI is used to match project details with volunteer skills and preferences, quickly identifying the most suitable candidates based on the duration of participation. Step 3: The support team assists new participants in registering their profiles, based on the information provided by the matching team. For example, they guide new participants through the input process and flexibly match them based on their desired duration and schedule. Step 4: The Experience Accumulation Department records each participant's activity record and provides feedback for skill improvement based on the information registered by the Support Department. For example, it records the number of times volunteers participate and the results achieved, evaluates skill improvement based on evaluation criteria, and provides feedback.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the profile management unit, matching unit, support unit, and experience accumulation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the profile management unit is implemented by the control unit 46A of the smart device 14 and registers and manages volunteers' skills, preferences, participation period, and desired schedule. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the most suitable personnel from the project details and volunteer profiles. The support unit is implemented by the control unit 46A of the smart device 14 and assists in registering the profiles of new participants. The experience accumulation unit is implemented by the identification processing unit 290 of the data processing unit 12 and records each participant's activity record and provides feedback for skill improvement. The short-term response unit is implemented by the control unit 46A of the smart device 14 and quickly identifies the most suitable personnel for short-term projects. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the profile management unit, matching unit, support unit, and experience accumulation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the profile management unit is implemented by the control unit 46A of the smart glasses 214 and registers and manages volunteers' skills, preferences, participation period, and desired schedule. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the most suitable personnel from the project details and volunteer profiles. The support unit is implemented by the control unit 46A of the smart glasses 214 and assists in registering the profiles of new participants. The experience accumulation unit is implemented by the identification processing unit 290 of the data processing unit 12 and records each participant's activity record and provides feedback for skill improvement. The short-term response unit is implemented by the control unit 46A of the smart glasses 214 and quickly identifies the most suitable personnel for short-term projects. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the profile management unit, matching unit, support unit, and experience accumulation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the profile management unit is implemented by the control unit 46A of the headset terminal 314 and registers and manages volunteers' skills, preferences, participation period, and desired schedule. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the most suitable personnel from the project details and volunteer profiles. The support unit is implemented by the control unit 46A of the headset terminal 314 and assists in registering the profiles of new participants. The experience accumulation unit is implemented by the identification processing unit 290 of the data processing unit 12 and records each participant's activity record and provides feedback for skill improvement. The short-term response unit is implemented by the control unit 46A of the headset terminal 314 and quickly identifies the most suitable personnel for short-term projects. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the profile management unit, matching unit, support unit, and experience accumulation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the profile management unit is implemented by the control unit 46A of the robot 414 and registers and manages volunteers' skills, preferences, participation period, and desired schedule. The matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies the most suitable personnel from the project details and volunteer profiles. The support unit is implemented by, for example, the control unit 46A of the robot 414 and assists in registering the profiles of new participants. The experience accumulation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and records each participant's activity record and provides feedback for skill improvement. The short-term response unit is implemented by, for example, the control unit 46A of the robot 414 and quickly identifies the most suitable personnel for short-term projects. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The profile management department registers and manages volunteers' skills, preferences, participation period, and desired dates. Based on the information managed by the aforementioned Profile Management Department, the Matching Department identifies the most suitable personnel from the project details and volunteer profiles. A support unit assists new participants in registering their profiles for individuals identified by the aforementioned matching unit. The system includes an experience accumulation unit that records each participant's activity record and provides feedback for skill improvement based on information registered by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned experience accumulation unit, We have a short-term response department that provides suitable staffing for short-term projects. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned profile management department, Accumulate experience in volunteer activities The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Quickly identify the most suitable personnel based on their participation period. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is We provide flexible matching based on the duration and desired schedule. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned experience accumulation unit, Provide feedback on skill improvement. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned profile management department, It estimates the user's emotions and adjusts how they fill out their profile based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned profile management department, When managing profiles, the system provides optimal profile input support by referencing the volunteer's past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned profile management department, When managing profiles, adjust the level of detail in input fields according to the volunteer's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned profile management department, It estimates the user's emotions and adjusts how profiles are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned profile management department, When managing profiles, the system prioritizes displaying relevant activity information by considering the volunteer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned profile management department, When managing profiles, the system analyzes volunteers' social media activity and automatically fills in relevant skills and interests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is During the matching process, the priority of matching is determined based on the urgency of the project. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is During the matching process, the system references the volunteer's past matching history to apply the most suitable matching algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is The system estimates the user's emotions and adjusts how matching results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is During the matching process, we identify the most suitable volunteers by considering the geographical distribution of the projects. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature for each project. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is When providing support, refer to the new participant's past activity history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is During support sessions, customize the support content according to the skill level of new participants. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is When providing support, the most suitable support method will be selected considering the geographical location information of new participants. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is During support, analyze the social media activity of new participants to supplement the support provided. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned experience accumulation unit, It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned experience accumulation unit, When accumulating experience, we provide optimal feedback by referring to the volunteer's past activity record. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned experience accumulation unit, As volunteers gain experience, the feedback content will be customized according to their skill level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned experience accumulation unit, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned experience accumulation unit, When accumulating experience, provide optimal feedback while considering the geographical location of volunteers. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned experience accumulation unit, As experience accumulates, analyze volunteers' social media activities to supplement the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned short-term response unit is The system estimates user sentiment and adjusts the allocation of short-term projects based on the estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned short-term response unit is When responding to short-term situations, the optimal deployment method is selected by referring to the volunteers' past short-term assignment history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned short-term response unit is For short-term deployments, customize volunteer assignments according to their skill levels. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned short-term response unit is It estimates user sentiment and prioritizes short-term projects based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned short-term response unit is When responding to short-term situations, the optimal deployment method will be selected considering the geographical location of the volunteers. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned short-term response unit is During short-term deployments, analyze volunteers' social media activity to supplement deployment decisions. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The profile management department registers and manages volunteers' skills, preferences, participation period, and desired dates. Based on the information managed by the aforementioned Profile Management Department, the Matching Department identifies the most suitable personnel from the project details and volunteer profiles. A support unit assists new participants in registering their profiles for individuals identified by the aforementioned matching unit. The system includes an experience accumulation unit that records each participant's activity record and provides feedback for skill improvement based on information registered by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned experience accumulation unit, We have a short-term response department that provides suitable staffing for short-term projects. The system according to feature 1.

3. The aforementioned profile management department, Accumulate experience in volunteer activities The system according to feature 1.

4. The matching unit is Quickly identify the most suitable personnel based on their participation period. The system according to feature 1.

5. The aforementioned support unit is We provide flexible matching based on the duration and desired schedule. The system according to feature 1.

6. The aforementioned experience accumulation unit, Provide feedback on skill improvement. The system according to feature 1.

7. The aforementioned profile management department, It estimates the user's emotions and adjusts how they fill out their profile based on those emotions. The system according to feature 1.

8. The aforementioned profile management department, When managing profiles, the system provides optimal profile input support by referencing the volunteer's past activity history. The system according to feature 1.

9. The aforementioned profile management department, When managing profiles, adjust the level of detail in input fields according to the volunteer's skill level. The system according to feature 1.

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