system

Abstract

The system according to this embodiment aims to centralize information on volunteer activities and facilitate efficient matching and fundraising. [Solution] The system according to the embodiment comprises a collection unit, a matching unit, and a settlement unit. The collection unit collects information on volunteer activities. The matching unit matches volunteer activities based on the information collected by the collection unit. The settlement unit collects donations using an electronic payment system.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that information on volunteer activities is scattered, making it difficult to achieve efficient matching and fundraising.

[0005] The system according to the embodiment aims to centralize information on volunteer activities and perform efficient matching and fundraising.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a matching unit, and a settlement unit. The collection unit collects information on volunteer activities. The matching unit performs matching of volunteer activities based on the information collected by the collection unit. The settlement unit raises donations through an electronic payment system. [Effects of the Invention]

[0007] The system according to this embodiment can centralize information on volunteer activities and facilitate efficient matching and fundraising. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 activity support system according to an embodiment of the present invention is a system that centralizes information on volunteer activities and matches organizations seeking volunteers with individuals who wish to provide support. This system collects information on volunteer activities, matches volunteer activities based on the collected information, and also has the function of soliciting donations through an electronic payment system. For example, each organization can post information on volunteer recruitment, and users can search for activities they are interested in and apply to participate. In addition, users can post their volunteer activity achievements and experiences, and other users can comment on and "like" these posts. Furthermore, donations can be solicited using an electronic payment system and used to support volunteer activities (food expenses, accommodation expenses, other supplies, etc.). Finally, a marketplace is provided where users can exchange goods with each other, allowing them to provide unwanted goods to other users and receive necessary goods in return. It is expected that this platform will centralize information on volunteer activities, improve participant motivation, and further revitalize volunteer activities through donations and the exchange of goods. As a result, the volunteer activity support system will centralize information on volunteer activities and efficiently perform volunteer activity matching and donation solicitation.

[0029] The volunteer activity support system according to this embodiment comprises a collection unit, a matching unit, and a payment unit. The collection unit collects information on volunteer activities. The information collected includes, but is not limited to, details of volunteer activities, dates and times, and locations. The collection unit includes, for example, a function that allows each organization to post volunteer recruitment information. Next, the matching unit matches volunteer activities based on the information collected by the collection unit. The matching is performed based on, for example, the user's interests, skills, geographical conditions, etc., but is not limited to these. The matching unit includes, for example, a function that allows users to search for activities they are interested in and apply to participate. Furthermore, the payment unit solicits donations using an electronic payment system. The electronic payment system includes, for example, credit cards, electronic money, and bank transfers, but is not limited to these. The payment unit includes, for example, a function that solicits donations using an electronic payment system and utilizes them to support volunteer activities. As a result, the volunteer activity support system according to this embodiment centralizes information on volunteer activities and enables efficient matching of volunteer activities and solicitation of donations.

[0030] The volunteer activity support system includes a public section for publishing volunteer activity records or experiences. The public section publishes volunteer activity records and experiences. The specific content and format of these records and experiences may include, but are not limited to, text, photos, and videos. The public section includes features such as allowing users to post volunteer activity records and experiences, and enabling other users to comment on and "like" those posts. This allows for increased motivation among participants by publishing their volunteer activity records and experiences.

[0031] The volunteer activity support system includes a market department that operates an exchange market. The market department operates a market where users can exchange goods with each other. The specific methods of operation and types of transactions of the exchange market include, but are not limited to, the types of goods and trading rules. The market department has a function that allows users to provide unwanted goods to other users and receive needed goods. This enables the exchange of goods between users and expands support for volunteer activities.

[0032] The volunteer activity support system includes an evaluation unit that provides a volunteer activity evaluation system. The evaluation unit provides a system for evaluating volunteer activities. Specific criteria and methods of the evaluation system include, but are not limited to, evaluation items and evaluation scales. The evaluation unit also includes, for example, a function that allows users to evaluate volunteer activities and share the evaluation results with other users. This enables the evaluation of volunteer activities and improves the quality of those activities.

[0033] The collection section allows organizations to post volunteer recruitment information. The collection section has a function that enables organizations to post volunteer recruitment information. The specific content and format of volunteer recruitment information may include, but are not limited to, the purpose of recruitment, required skills, and details of the activities. For example, organizations can post volunteer recruitment information and make that information available to other users. This allows organizations to post volunteer recruitment information, making information collection more efficient.

[0034] The matching function allows users to search for activities they are interested in and apply to participate. The matching function provides a feature that allows users to search for activities they are interested in and apply to participate. Specific types and criteria for activities of interest include, for example, environmental protection activities and educational support activities, but are not limited to these examples. The matching function allows users to search for activities they are interested in and apply to participate. This enables users to search for activities they are interested in and apply to participate.

[0035] The settlement department can use an electronic payment system to collect donations and use them to support volunteer activities. The settlement department is equipped with the functionality to collect donations using an electronic payment system and use them to support volunteer activities. Specific methods and criteria for collecting donations include, but are not limited to, the purpose of the donation, the target amount, and the collection period. For example, the settlement department can use an electronic payment system to collect donations and use those donations to support volunteer activities (food expenses, accommodation expenses, other supplies, etc.). This allows for the collection of donations using an electronic payment system and their use to support volunteer activities.

[0036] The data collection unit can analyze each organization's past recruitment history and select an appropriate data collection method. The unit has the capability to analyze each organization's past recruitment history and select the optimal data collection method. Specific methods and criteria for analyzing recruitment history include, but are not limited to, past recruitment data and analytical metrics. For example, the unit can analyze successful recruitment methods from the past and collect information using similar methods. It can also select effective data collection methods for specific periods based on past recruitment history. Furthermore, it can propose the optimal data collection method for a particular organization based on past recruitment history. This allows for the selection of the optimal data collection method by analyzing past recruitment history.

[0037] The collection unit can filter volunteer recruitment information based on the organization's current activities or areas of interest. The collection unit has the functionality to filter volunteer recruitment information based on the organization's current activities and areas of interest. Specific filtering criteria and methods include, but are not limited to, evaluation criteria for activity status and methods for identifying areas of interest. For example, the collection unit can check the organization's current activities and prioritize collecting information from actively operating organizations. It can also filter relevant volunteer recruitment information based on the organization's areas of interest. Furthermore, it can combine the organization's activities and areas of interest to collect the most relevant information. This allows for the collection of highly relevant information by filtering information based on the organization's activities and areas of interest.

[0038] The data collection unit can prioritize the collection of highly relevant information based on the organization's geographical location when collecting volunteer recruitment information. The data collection unit has a function to prioritize the collection of highly relevant information by considering the organization's geographical location when collecting volunteer recruitment information. Specific methods for obtaining and using geographical location information include, but are not limited to, GPS data and location services. For example, the data collection unit can prioritize the collection of nearby volunteer recruitment information based on the organization's location. It can also filter relevant volunteer recruitment information based on the organization's activity area. Furthermore, it can collect the most relevant information based on the organization's geographical location. This allows for the priority collection of highly relevant information by considering the organization's geographical location.

[0039] The collection unit can analyze an organization's social media activity and collect relevant information when gathering volunteer recruitment information. The collection unit has the functionality to analyze an organization's social media activity and collect relevant information when gathering volunteer recruitment information. Specific methods and criteria for analyzing social media activity include, but are not limited to, post content and engagement rates. For example, the collection unit can analyze an organization's social media posts and collect relevant volunteer recruitment information. It can also analyze the interests of an organization's followers and collect relevant volunteer recruitment information. Furthermore, it can analyze trends in an organization's social media activity and collect the most relevant information. In this way, relevant information can be collected by analyzing an organization's social media activity.

[0040] The matching unit can improve the accuracy of matching based on the interrelationships of volunteer activities. The matching unit has a function to improve the accuracy of matching by considering the interrelationships of volunteer activities. Specific methods and criteria for considering interrelationships include, but are not limited to, the relevance and cooperative nature of the activities. For example, the matching unit prioritizes matching volunteers whose activities complement each other. It can also adjust the schedules of volunteer activities to avoid overlap. Furthermore, it can prioritize matching volunteers whose activities are located in nearby areas. In this way, considering the interrelationships of volunteer activities improves the accuracy of matching.

[0041] The matching unit can perform matching based on user attribute information during the matching process. The matching unit has a function that considers user attribute information when performing matching. Specific types of attribute information and methods of acquisition include, but are not limited to, age, gender, and occupation. For example, the matching unit can match appropriate volunteer activities based on the user's age and gender. It can also match appropriate volunteer activities based on the user's occupation and skills. Furthermore, it can match appropriate volunteer activities based on the user's interests and preferences. In this way, by considering the user's attribute information, it is possible to match more appropriate volunteer activities.

[0042] The matching unit can perform matching while considering the geographical distribution of volunteer activities. The matching unit has a function that considers the geographical distribution of volunteer activities during the matching process. Specific methods and criteria for considering geographical distribution include, but are not limited to, the area and distance of the activity. For example, the matching unit can prioritize matching volunteer activities close to the user's place of residence. It can also prioritize matching volunteer activities in nearby areas. Furthermore, it can prioritize matching volunteer activities along the user's commute route. In this way, by considering the geographical distribution of volunteer activities, the system can match users with the most suitable volunteer activities.

[0043] The matching unit can improve the accuracy of matching based on relevant literature related to volunteer activities. The matching unit has a function to improve matching accuracy by referring to relevant literature related to volunteer activities during the matching process. Specific types of relevant literature and methods of reference include, but are not limited to, academic papers and reports. For example, the matching unit can refer to relevant literature related to volunteer activities to perform the optimal match. It can also refer to successful volunteer activity case studies to perform the optimal match. Furthermore, it can refer to research data on volunteer activities to perform the optimal match. As a result, the accuracy of matching is improved by referring to relevant literature related to volunteer activities.

[0044] The settlement unit can select the optimal fundraising method based on past fundraising data when soliciting donations. The settlement unit has a function to select the optimal fundraising method by referring to past fundraising data when soliciting donations. The specific types and methods of referencing past fundraising data include, but are not limited to, the amount of donations and the attributes of donors. For example, the settlement unit can analyze successful fundraising campaigns in the past and solicit donations using similar methods. It can also select fundraising methods that are effective at a particular time based on past fundraising data. Furthermore, it can propose the optimal fundraising method for a specific user group based on past fundraising data. In this way, the optimal fundraising method can be selected by referring to past fundraising data.

[0045] The payment unit can adjust the fundraising methods based on user attribute information when soliciting donations. The payment unit has the function to customize fundraising methods based on user attribute information when soliciting donations. Specific methods and criteria for adjusting fundraising methods include, but are not limited to, online donations and event donations. For example, the payment unit can suggest appropriate donation methods based on the user's age and gender. It can also suggest appropriate donation methods based on the user's occupation and income. Furthermore, it can suggest appropriate donation methods based on the user's interests and concerns. By customizing fundraising methods based on user attribute information, more effective fundraising activities can be conducted.

[0046] The payment unit can select the most suitable donation method based on the user's geographical location information when soliciting donations. The payment unit has a function to select the most suitable donation method considering the user's geographical location information when soliciting donations. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location information services. For example, the payment unit can propose a locally focused donation method based on the user's place of residence. It can also propose the most suitable donation method based on the user's activity area. Furthermore, it can propose the most relevant donation method based on the user's geographical location information. This allows for the selection of the most suitable donation method by considering the user's geographical location information.

[0047] The payment department can analyze a user's social media activity and suggest donation methods when soliciting donations. The payment department has a function to analyze a user's social media activity and suggest donation methods when soliciting donations. Specific methods and criteria for analyzing social media activity include, but are not limited to, post content and engagement rates. For example, the payment department can analyze a user's social media posts and suggest relevant donation methods. It can also analyze the interests of a user's followers and suggest relevant donation methods. Furthermore, it can analyze trends in the user's social media activity and suggest the most relevant donation methods. In this way, by analyzing a user's social media activity, the optimal donation method can be suggested.

[0048] The Public Information Section can adjust the level of detail disclosed based on the importance of the volunteer activity at the time of disclosure. The Public Information Section has a function to adjust the level of detail disclosed based on the importance of the volunteer activity at the time of disclosure. Specific methods and criteria for adjusting the level of detail include, but are not limited to, the depth and specificity of the information. For example, the Public Information Section will disclose detailed information for highly important volunteer activities. Conversely, it may disclose concise information for less important volunteer activities. Furthermore, it can adjust the level of detail of the information disclosed according to its importance. This allows for more effective information provision by adjusting the level of detail disclosed based on the importance of the volunteer activity.

[0049] The Public Information Department can select the priority of information disclosure based on the timing of the volunteer activities at the time of disclosure. The Public Information Department has the function of determining the priority of information disclosure based on the timing of the volunteer activities at the time of disclosure. Specific methods and criteria for determining the priority of information disclosure based on the timing of the activities include, but are not limited to, the start date and end date of the activities. The Public Information Department may, for example, prioritize the disclosure of volunteer activities that are being carried out most recently. Longer-term volunteer activities may be disclosed at a later date. Furthermore, the priority of information disclosure can be adjusted according to the timing of the volunteer activities. This allows for more effective information provision by determining the priority of information disclosure based on the timing of the volunteer activities.

[0050] The Market Department can select the optimal operating method by referring to past trading data when operating the market. The Market Department has the functionality to select the optimal operating method by referring to past trading data when operating the market. Specific types of past trading data and methods of reference include, but are not limited to, trading amounts and types of trading. For example, the Market Department can analyze successful past trading methods and operate the market using similar methods. It can also select an effective operating method for a specific period based on past trading data. Furthermore, it can propose the optimal operating method for a specific user group based on past trading data. In this way, the optimal operating method can be selected by referring to past trading data.

[0051] The market department can select the optimal operating method based on the user's geographical location information when operating the market. The market department has a function to select the optimal operating method considering the user's geographical location information when operating the market. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location information services. For example, the market department can propose a locally focused trading method based on the user's place of residence. It can also propose the optimal trading method based on the user's activity area. Furthermore, it can propose the most relevant trading method based on the user's geographical location information. In this way, the optimal operating method can be selected by considering the user's geographical location information.

[0052] The evaluation unit can optimize its evaluation algorithm based on past evaluation data during the evaluation process. The evaluation unit has a function to optimize its evaluation algorithm by referring to past evaluation data during the evaluation process. Specific types of past evaluation data and methods of reference include, but are not limited to, evaluation results and evaluation items. For example, the evaluation unit analyzes past evaluation data and selects the optimal evaluation algorithm. It can also select an effective evaluation method for a specific period based on past evaluation data. Furthermore, it can propose an optimal evaluation method for a specific user group based on past evaluation data. This allows for the optimization of the evaluation algorithm by referring to past evaluation data.

[0053] The evaluation unit can weight evaluation data based on the timing of the volunteer activities during the evaluation process. The evaluation unit has a function to weight evaluation data based on the timing of the volunteer activities during the evaluation process. Specific methods and criteria for weighting evaluation data based on the timing include, but are not limited to, the start and end dates of the activities. For example, the evaluation unit may give more weight to evaluation data for the most recent volunteer activities. It may also disregard evaluation data for long-term volunteer activities. Furthermore, it can adjust the weighting of evaluation data according to the timing of the volunteer activities. This allows for more appropriate evaluations by weighting evaluation data based on the timing of the volunteer activities.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The volunteer activity support system can also include a health management unit that monitors the user's health status. The health management unit collects user health data and evaluates the user's suitability for volunteer activities based on this data. For example, it collects user heart rate, steps, and sleep data and adjusts activities to avoid excessive strain. It can also suggest more physically demanding activities to users in good health. Furthermore, the health management unit can suggest timings for rest and nutrition based on the user's health condition. This allows for the provision of optimal volunteer activities while maintaining the user's health.

[0056] The volunteer activity support system can also include a skills assessment unit that evaluates users' skills. This unit assesses users' skills based on their past volunteer activities and work experience, and suggests appropriate volunteer activities. For example, in educational support activities, it prioritizes matching users with education-related experience or qualifications. Similarly, in environmental protection activities, it can prioritize matching users with knowledge of environmental science. Furthermore, the skills assessment unit can suggest training programs to improve users' skills. This allows for the maximum utilization of users' skills and enables effective volunteer activities.

[0057] The volunteer activity support system can also include an interest analysis unit that analyzes user interests and suggests volunteer activities based on those interests. The interest analysis unit analyzes the user's past activity history and search history to suggest the most suitable volunteer activities. For example, if a user has previously participated in educational support activities, similar activities will be suggested. If a user is interested in environmental protection, environmental protection activities can also be suggested. Furthermore, the interest analysis unit can also suggest new activities based on the user's interests. This allows for the provision of volunteer activities tailored to the user's interests, thereby increasing their motivation to participate.

[0058] The volunteer activity support system can also include a location information utilization unit that leverages the user's geographical location information to suggest the most suitable volunteer activities. This unit analyzes the user's current location and travel history to suggest nearby volunteer activities. For example, if a user is staying in a specific area, it will prioritize suggesting volunteer activities taking place in that area. It can also suggest volunteer activities along the user's commute route. Furthermore, based on the user's travel history, the location information utilization unit can suggest volunteer activities in new areas. This allows the system to provide optimal volunteer activities by utilizing the user's geographical location information.

[0059] The volunteer activity support system can also include a social media analytics department that analyzes users' social media activity and suggests relevant volunteer activities. The social media analytics department analyzes users' posts and engagement rates to suggest the most suitable volunteer activities. For example, if a user frequently posts about environmental protection, it will suggest environmental protection activities. It can also analyze the interests of the user's followers and suggest relevant volunteer activities. Furthermore, the social media analytics department can analyze trends in the user's social media activity and suggest new volunteer activities. This allows the system to analyze users' social media activity and provide the most suitable volunteer activities.

[0060] The volunteer activity support system can also include an evaluation data analysis unit that analyzes the user's past evaluation data and proposes the most suitable volunteer activities. The evaluation data analysis unit analyzes the user's past evaluation results and proposes the most suitable volunteer activities. For example, it can prioritize suggesting activities in which the user has received high ratings in the past. It can also suggest avoiding activities in which the user has received low ratings. Furthermore, the evaluation data analysis unit can also suggest new activities based on the user's evaluation data. This allows the system to provide optimal volunteer activities by utilizing the user's past evaluation data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit gathers information about volunteer activities. This information includes details of the volunteer activity, such as the date, time, and location. The collection unit also has a function that allows each organization to post volunteer recruitment information. Step 2: The matching department matches volunteers with suitable activities based on the information collected by the data collection department. Matching is based on the user's interests, skills, geographical location, etc. The matching department has a function that allows users to search for activities they are interested in and apply to participate. Step 3: The payment department collects donations through an electronic payment system. This system includes credit cards, e-money, and bank transfers. The payment department has the functionality to collect donations using the electronic payment system and utilize them to support volunteer activities.

[0063] (Example of form 2) The volunteer activity support system according to an embodiment of the present invention is a system that centralizes information on volunteer activities and matches organizations seeking volunteers with individuals who wish to provide support. This system collects information on volunteer activities, matches volunteer activities based on the collected information, and also has the function of soliciting donations through an electronic payment system. For example, each organization can post information on volunteer recruitment, and users can search for activities they are interested in and apply to participate. In addition, users can post their volunteer activity achievements and experiences, and other users can comment on and "like" these posts. Furthermore, donations can be solicited using an electronic payment system and used to support volunteer activities (food expenses, accommodation expenses, other supplies, etc.). Finally, a marketplace is provided where users can exchange goods with each other, allowing them to provide unwanted goods to other users and receive necessary goods in return. It is expected that this platform will centralize information on volunteer activities, improve participant motivation, and further revitalize volunteer activities through donations and the exchange of goods. As a result, the volunteer activity support system will centralize information on volunteer activities and efficiently perform volunteer activity matching and donation solicitation.

[0064] The volunteer activity support system according to this embodiment comprises a collection unit, a matching unit, and a payment unit. The collection unit collects information on volunteer activities. The information collected includes, but is not limited to, details of volunteer activities, dates and times, and locations. The collection unit includes, for example, a function that allows each organization to post volunteer recruitment information. Next, the matching unit matches volunteer activities based on the information collected by the collection unit. The matching is performed based on, for example, the user's interests, skills, geographical conditions, etc., but is not limited to these. The matching unit includes, for example, a function that allows users to search for activities they are interested in and apply to participate. Furthermore, the payment unit solicits donations using an electronic payment system. The electronic payment system includes, for example, credit cards, electronic money, and bank transfers, but is not limited to these. The payment unit includes, for example, a function that solicits donations using an electronic payment system and utilizes them to support volunteer activities. As a result, the volunteer activity support system according to this embodiment centralizes information on volunteer activities and enables efficient matching of volunteer activities and solicitation of donations.

[0065] The volunteer activity support system includes a public section for publishing volunteer activity records or experiences. The public section publishes volunteer activity records and experiences. The specific content and format of these records and experiences may include, but are not limited to, text, photos, and videos. The public section includes features such as allowing users to post volunteer activity records and experiences, and enabling other users to comment on and "like" those posts. This allows for increased motivation among participants by publishing their volunteer activity records and experiences.

[0066] The volunteer activity support system includes a market department that operates an exchange market. The market department operates a market where users can exchange goods with each other. The specific methods of operation and types of transactions of the exchange market include, but are not limited to, the types of goods and trading rules. The market department has a function that allows users to provide unwanted goods to other users and receive needed goods. This enables the exchange of goods between users and expands support for volunteer activities.

[0067] The volunteer activity support system includes an evaluation unit that provides a volunteer activity evaluation system. The evaluation unit provides a system for evaluating volunteer activities. Specific criteria and methods of the evaluation system include, but are not limited to, evaluation items and evaluation scales. The evaluation unit also includes, for example, a function that allows users to evaluate volunteer activities and share the evaluation results with other users. This enables the evaluation of volunteer activities and improves the quality of those activities.

[0068] The collection section allows organizations to post volunteer recruitment information. The collection section has a function that enables organizations to post volunteer recruitment information. The specific content and format of volunteer recruitment information may include, but are not limited to, the purpose of recruitment, required skills, and details of the activities. For example, organizations can post volunteer recruitment information and make that information available to other users. This allows organizations to post volunteer recruitment information, making information collection more efficient.

[0069] The matching function allows users to search for activities they are interested in and apply to participate. The matching function provides a feature that allows users to search for activities they are interested in and apply to participate. Specific types and criteria for activities of interest include, for example, environmental protection activities and educational support activities, but are not limited to these examples. The matching function allows users to search for activities they are interested in and apply to participate. This enables users to search for activities they are interested in and apply to participate.

[0070] The settlement department can use an electronic payment system to collect donations and use them to support volunteer activities. The settlement department is equipped with the functionality to collect donations using an electronic payment system and use them to support volunteer activities. Specific methods and criteria for collecting donations include, but are not limited to, the purpose of the donation, the target amount, and the collection period. For example, the settlement department can use an electronic payment system to collect donations and use those donations to support volunteer activities (food expenses, accommodation expenses, other supplies, etc.). This allows for the collection of donations using an electronic payment system and their use to support volunteer activities.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of collecting volunteer recruitment information based on those emotions. The data collection unit has the function of estimating the user's emotions and adjusting the timing of collecting volunteer recruitment information based on those emotions. Specific methods and criteria for estimating emotions include, but are not limited to, emotion analysis algorithms and emotion types. For example, if the user is excited, the data collection unit will immediately collect volunteer recruitment information to maintain the user's interest. If the user is relaxed, it will collect volunteer recruitment information periodically to avoid burdening the user. Furthermore, if the user is stressed, it will delay the collection timing and provide information when the user has calmed down. In this way, by adjusting the collection timing according to the user's emotions, the data collection unit can maintain the user's interest.

[0072] The data collection unit can analyze each organization's past recruitment history and select an appropriate data collection method. The unit has the capability to analyze each organization's past recruitment history and select the optimal data collection method. Specific methods and criteria for analyzing recruitment history include, but are not limited to, past recruitment data and analytical metrics. For example, the unit can analyze successful recruitment methods from the past and collect information using similar methods. It can also select effective data collection methods for specific periods based on past recruitment history. Furthermore, it can propose the optimal data collection method for a particular organization based on past recruitment history. This allows for the selection of the optimal data collection method by analyzing past recruitment history.

[0073] The collection unit can filter volunteer recruitment information based on the organization's current activities or areas of interest. The collection unit has the functionality to filter volunteer recruitment information based on the organization's current activities and areas of interest. Specific filtering criteria and methods include, but are not limited to, evaluation criteria for activity status and methods for identifying areas of interest. For example, the collection unit can check the organization's current activities and prioritize collecting information from actively operating organizations. It can also filter relevant volunteer recruitment information based on the organization's areas of interest. Furthermore, it can combine the organization's activities and areas of interest to collect the most relevant information. This allows for the collection of highly relevant information by filtering information based on the organization's activities and areas of interest.

[0074] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on those emotions. The data collection unit has the function of estimating the user's emotions and determining the priority of information to collect based on those emotions. Specific criteria and methods for determining information priority include, but are not limited to, the importance of the information and the user's level of interest. For example, if the user is excited, the data collection unit will prioritize collecting the latest volunteer recruitment information. If the user is relaxed, it may also prioritize collecting long-term volunteer recruitment information. Furthermore, if the user is stressed, it may prioritize collecting simple, short-term volunteer recruitment information. This allows for the collection of more relevant information by prioritizing information according to the user's emotions.

[0075] The data collection unit can prioritize the collection of highly relevant information based on the organization's geographical location when collecting volunteer recruitment information. The data collection unit has a function to prioritize the collection of highly relevant information by considering the organization's geographical location when collecting volunteer recruitment information. Specific methods for obtaining and using geographical location information include, but are not limited to, GPS data and location services. For example, the data collection unit can prioritize the collection of nearby volunteer recruitment information based on the organization's location. It can also filter relevant volunteer recruitment information based on the organization's activity area. Furthermore, it can collect the most relevant information based on the organization's geographical location. This allows for the priority collection of highly relevant information by considering the organization's geographical location.

[0076] The collection unit can analyze an organization's social media activity and collect relevant information when gathering volunteer recruitment information. The collection unit has the functionality to analyze an organization's social media activity and collect relevant information when gathering volunteer recruitment information. Specific methods and criteria for analyzing social media activity include, but are not limited to, post content and engagement rates. For example, the collection unit can analyze an organization's social media posts and collect relevant volunteer recruitment information. It can also analyze the interests of an organization's followers and collect relevant volunteer recruitment information. Furthermore, it can analyze trends in an organization's social media activity and collect the most relevant information. In this way, relevant information can be collected by analyzing an organization's social media activity.

[0077] The matching unit can estimate the user's emotions and adjust the matching criteria based on those emotions. The matching unit has the function of estimating the user's emotions and adjusting the matching criteria based on those emotions. Specific methods and criteria for adjusting the matching criteria include, but are not limited to, the user's interests, skills, and geographical location. For example, if the user is excited, the matching unit may prioritize matching them with challenging volunteer activities. If the user is relaxed, it may prioritize matching them with relaxing volunteer activities. Furthermore, if the user is stressed, it may prioritize matching them with easy, short-term volunteer activities. This allows for more appropriate matching by adjusting the matching criteria according to the user's emotions.

[0078] The matching unit can improve the accuracy of matching based on the interrelationships of volunteer activities. The matching unit has a function to improve the accuracy of matching by considering the interrelationships of volunteer activities. Specific methods and criteria for considering interrelationships include, but are not limited to, the relevance and cooperative nature of the activities. For example, the matching unit prioritizes matching volunteers whose activities complement each other. It can also adjust the schedules of volunteer activities to avoid overlap. Furthermore, it can prioritize matching volunteers whose activities are located in nearby areas. In this way, considering the interrelationships of volunteer activities improves the accuracy of matching.

[0079] The matching unit can perform matching based on user attribute information during the matching process. The matching unit has a function that considers user attribute information when performing matching. Specific types of attribute information and methods of acquisition include, but are not limited to, age, gender, and occupation. For example, the matching unit can match appropriate volunteer activities based on the user's age and gender. It can also match appropriate volunteer activities based on the user's occupation and skills. Furthermore, it can match appropriate volunteer activities based on the user's interests and preferences. In this way, by considering the user's attribute information, it is possible to match more appropriate volunteer activities.

[0080] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on those emotions. The matching unit has the function of estimating the user's emotions and adjusting the order in which matching results are displayed based on those emotions. Specific methods and criteria for adjusting the display order include, but are not limited to, the user's level of interest and the importance of the activity. For example, if the user is excited, the matching unit can display challenging volunteer activities at the top. If the user is relaxed, it can also display relaxing volunteer activities at the top. Furthermore, if the user is stressed, it can display easy, short-term volunteer activities at the top. This allows the system to provide the user with the most relevant information by adjusting the order in which matching results are displayed according to their emotions.

[0081] The matching unit can perform matching while considering the geographical distribution of volunteer activities. The matching unit has a function that considers the geographical distribution of volunteer activities during the matching process. Specific methods and criteria for considering geographical distribution include, but are not limited to, the area and distance of the activity. For example, the matching unit can prioritize matching volunteer activities close to the user's place of residence. It can also prioritize matching volunteer activities in nearby areas. Furthermore, it can prioritize matching volunteer activities along the user's commute route. In this way, by considering the geographical distribution of volunteer activities, the system can match users with the most suitable volunteer activities.

[0082] The matching unit can improve the accuracy of matching based on relevant literature related to volunteer activities. The matching unit has a function to improve matching accuracy by referring to relevant literature related to volunteer activities during the matching process. Specific types of relevant literature and methods of reference include, but are not limited to, academic papers and reports. For example, the matching unit can refer to relevant literature related to volunteer activities to perform the optimal match. It can also refer to successful volunteer activity case studies to perform the optimal match. Furthermore, it can refer to research data on volunteer activities to perform the optimal match. As a result, the accuracy of matching is improved by referring to relevant literature related to volunteer activities.

[0083] The payment unit can estimate the user's emotions and adjust the fundraising method based on those emotions. The payment unit has the functionality to estimate the user's emotions and adjust the fundraising method based on those emotions. Specific methods and criteria for adjusting the fundraising method include, but are not limited to, the purpose of the fundraising, the target amount, and the fundraising period. For example, if the user is excited, the payment unit can implement an impactful fundraising campaign. If the user is relaxed, it can implement a calmer fundraising campaign. Furthermore, if the user is stressed, it can offer a simple and easy way to donate. This allows for more effective fundraising by adjusting the fundraising method according to the user's emotions.

[0084] The settlement unit can select the optimal fundraising method based on past fundraising data when soliciting donations. The settlement unit has a function to select the optimal fundraising method by referring to past fundraising data when soliciting donations. The specific types and methods of referencing past fundraising data include, but are not limited to, the amount of donations and the attributes of donors. For example, the settlement unit can analyze successful fundraising campaigns in the past and solicit donations using similar methods. It can also select fundraising methods that are effective at a particular time based on past fundraising data. Furthermore, it can propose the optimal fundraising method for a specific user group based on past fundraising data. In this way, the optimal fundraising method can be selected by referring to past fundraising data.

[0085] The payment unit can adjust the fundraising methods based on user attribute information when soliciting donations. The payment unit has the function to customize fundraising methods based on user attribute information when soliciting donations. Specific methods and criteria for adjusting fundraising methods include, but are not limited to, online donations and event donations. For example, the payment unit can suggest appropriate donation methods based on the user's age and gender. It can also suggest appropriate donation methods based on the user's occupation and income. Furthermore, it can suggest appropriate donation methods based on the user's interests and concerns. By customizing fundraising methods based on user attribute information, more effective fundraising activities can be conducted.

[0086] The payment unit can estimate the user's emotions and determine the priority of donations based on those emotions. The payment unit has the function of estimating the user's emotions and determining the priority of donations based on those emotions. Specific criteria and methods for determining donation priority include, but are not limited to, the purpose and urgency of the donation. For example, if the user is excited, the payment unit may prioritize highly urgent donations. If the user is relaxed, it may also prioritize long-term donations. Furthermore, if the user is stressed, it may prioritize easy and convenient donations. This allows for more effective fundraising by prioritizing donations according to the user's emotions.

[0087] The payment unit can select the most suitable donation method based on the user's geographical location information when soliciting donations. The payment unit has a function to select the most suitable donation method considering the user's geographical location information when soliciting donations. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location information services. For example, the payment unit can propose a locally focused donation method based on the user's place of residence. It can also propose the most suitable donation method based on the user's activity area. Furthermore, it can propose the most relevant donation method based on the user's geographical location information. This allows for the selection of the most suitable donation method by considering the user's geographical location information.

[0088] The payment department can analyze a user's social media activity and suggest donation methods when soliciting donations. The payment department has a function to analyze a user's social media activity and suggest donation methods when soliciting donations. Specific methods and criteria for analyzing social media activity include, but are not limited to, post content and engagement rates. For example, the payment department can analyze a user's social media posts and suggest relevant donation methods. It can also analyze the interests of a user's followers and suggest relevant donation methods. Furthermore, it can analyze trends in the user's social media activity and suggest the most relevant donation methods. In this way, by analyzing a user's social media activity, the optimal donation method can be suggested.

[0089] The publishing function can estimate the user's emotions and adjust the way the published content is presented based on those emotions. The publishing function has the capability to estimate the user's emotions and adjust the way the published content is presented based on those emotions. Specific methods and criteria for adjusting the presentation include, but are not limited to, the tone of the text and the use of images. For example, if the user is excited, the publishing function may use an impactful presentation. If the user is relaxed, it may use a calm presentation. Furthermore, if the user is stressed, it may use a concise and easy-to-understand presentation. This allows for more effective information delivery by adjusting the presentation of published content according to the user's emotions.

[0090] The Public Information Section can adjust the level of detail disclosed based on the importance of the volunteer activity at the time of disclosure. The Public Information Section has a function to adjust the level of detail disclosed based on the importance of the volunteer activity at the time of disclosure. Specific methods and criteria for adjusting the level of detail include, but are not limited to, the depth and specificity of the information. For example, the Public Information Section will disclose detailed information for highly important volunteer activities. Conversely, it may disclose concise information for less important volunteer activities. Furthermore, it can adjust the level of detail of the information disclosed according to its importance. This allows for more effective information provision by adjusting the level of detail disclosed based on the importance of the volunteer activity.

[0091] The publishing function can estimate the user's emotions and adjust the length of the content it publishes based on those emotions. Specific methods and criteria for adjusting content length include, but are not limited to, sentence length and amount of information. For example, if the user is excited, the publishing function will publish short, concise content. If the user is relaxed, it may publish longer content with more detailed explanations. Furthermore, if the user is stressed, it may publish concise and easy-to-understand content. This allows for more effective information delivery by adjusting the length of published content according to the user's emotions.

[0092] The Public Information Department can select the priority of information disclosure based on the timing of the volunteer activities at the time of disclosure. The Public Information Department has the function of determining the priority of information disclosure based on the timing of the volunteer activities at the time of disclosure. Specific methods and criteria for determining the priority of information disclosure based on the timing of the activities include, but are not limited to, the start date and end date of the activities. The Public Information Department may, for example, prioritize the disclosure of volunteer activities that are being carried out most recently. Longer-term volunteer activities may be disclosed at a later date. Furthermore, the priority of information disclosure can be adjusted according to the timing of the volunteer activities. This allows for more effective information provision by determining the priority of information disclosure based on the timing of the volunteer activities.

[0093] The market department can estimate user sentiment and adjust market operations based on that estimation. The market department has the capability to estimate user sentiment and adjust market operations based on that estimation. Specific methods and criteria for adjusting operations include, but are not limited to, trading rules and fee settings. For example, if users are excited, the market department can implement campaigns to encourage active trading. If users are relaxed, it can provide a calm trading environment. Furthermore, if users are stressed, it can offer simple and easy trading methods. This allows for more effective trading by adjusting market operations according to user sentiment.

[0094] The Market Department can select the optimal operating method by referring to past trading data when operating the market. The Market Department has the functionality to select the optimal operating method by referring to past trading data when operating the market. Specific types of past trading data and methods of reference include, but are not limited to, trading amounts and types of trading. For example, the Market Department can analyze successful past trading methods and operate the market using similar methods. It can also select an effective operating method for a specific period based on past trading data. Furthermore, it can propose the optimal operating method for a specific user group based on past trading data. In this way, the optimal operating method can be selected by referring to past trading data.

[0095] The market unit can estimate user sentiment and prioritize markets based on that sentiment. The market unit has the functionality to estimate user sentiment and prioritize markets based on that sentiment. Specific criteria and methods for determining market priorities include, but are not limited to, the importance of a trade and the user's level of interest. For example, if a user is excited, the market unit may prioritize displaying urgent trades. If a user is relaxed, it may also prioritize displaying long-term trades. Furthermore, if a user is stressed, it may prioritize displaying easy and convenient trades. This allows for more effective trading by prioritizing markets according to user sentiment.

[0096] The market department can select the optimal operating method based on the user's geographical location information when operating the market. The market department has a function to select the optimal operating method considering the user's geographical location information when operating the market. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location information services. For example, the market department can propose a locally focused trading method based on the user's place of residence. It can also propose the optimal trading method based on the user's activity area. Furthermore, it can propose the most relevant trading method based on the user's geographical location information. In this way, the optimal operating method can be selected by considering the user's geographical location information.

[0097] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those estimated emotions. The evaluation unit has the function of estimating the user's emotions and adjusting the evaluation criteria based on those estimated emotions. Specific methods and criteria for adjusting the evaluation criteria include, but are not limited to, evaluation items and evaluation scales. For example, the evaluation unit may apply strict evaluation criteria if the user is excited, or lenient criteria if the user is relaxed, or simple and easy-to-understand criteria if the user is stressed. This allows for more appropriate evaluations by adjusting the evaluation criteria according to the user's emotions.

[0098] The evaluation unit can optimize its evaluation algorithm based on past evaluation data during the evaluation process. The evaluation unit has a function to optimize its evaluation algorithm by referring to past evaluation data during the evaluation process. Specific types of past evaluation data and methods of reference include, but are not limited to, evaluation results and evaluation items. For example, the evaluation unit analyzes past evaluation data and selects the optimal evaluation algorithm. It can also select an effective evaluation method for a specific period based on past evaluation data. Furthermore, it can propose an optimal evaluation method for a specific user group based on past evaluation data. This allows for the optimization of the evaluation algorithm by referring to past evaluation data.

[0099] The evaluation unit can estimate the user's emotions and adjust the frequency of evaluations based on those emotions. The evaluation unit has the function of estimating the user's emotions and adjusting the frequency of evaluations based on those emotions. Specific methods and criteria for adjusting the frequency of evaluations include, but are not limited to, the timing and interval of evaluations. For example, the evaluation unit may perform evaluations frequently if the user is excited, or at a moderate frequency if the user is relaxed, or even reduce the frequency of evaluations if the user is stressed. This allows for more appropriate evaluations by adjusting the frequency of evaluations according to the user's emotions.

[0100] The evaluation unit can weight evaluation data based on the timing of the volunteer activities during the evaluation process. The evaluation unit has a function to weight evaluation data based on the timing of the volunteer activities during the evaluation process. Specific methods and criteria for weighting evaluation data based on the timing include, but are not limited to, the start and end dates of the activities. For example, the evaluation unit may give more weight to evaluation data for the most recent volunteer activities. It may also disregard evaluation data for long-term volunteer activities. Furthermore, it can adjust the weighting of evaluation data according to the timing of the volunteer activities. This allows for more appropriate evaluations by weighting evaluation data based on the timing of the volunteer activities. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, matching unit, settlement unit, publication unit, market unit, and evaluation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information on volunteer activities. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches volunteer activities based on the collected information. The settlement unit is implemented, for example, by the control unit 46A of the smart device 14 and solicits donations using an electronic payment system. The publication unit is implemented, for example, by the control unit 46A of the smart device 14 and publishes the results and experiences of volunteer activities. The market unit is implemented, for example, by the control unit 46A of the smart device 14 and operates a market where users can exchange goods. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an evaluation system for volunteer activities. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, matching unit, settlement unit, publication unit, market unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information on volunteer activities. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches volunteer activities based on the collected information. The settlement unit is implemented, for example, by the control unit 46A of the smart glasses 214 and solicits donations using an electronic payment system. The publication unit is implemented, for example, by the control unit 46A of the smart glasses 214 and publishes the results and experiences of volunteer activities. The market unit is implemented, for example, by the control unit 46A of the smart glasses 214 and operates a market where users can exchange goods. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an evaluation system for volunteer activities. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, matching unit, settlement unit, publication unit, market unit, and evaluation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information on volunteer activities. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches volunteer activities based on the collected information. The settlement unit is implemented by, for example, the control unit 46A of the headset terminal 314 and solicits donations using an electronic payment system. The publication unit is implemented by, for example, the control unit 46A of the headset terminal 314 and publishes the results and experiences of volunteer activities. The market unit is implemented by, for example, the control unit 46A of the headset terminal 314 and operates a market where users can exchange goods. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides an evaluation system for volunteer activities. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, matching unit, settlement unit, publication unit, market unit, and evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information on volunteer activities. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches volunteer activities based on the collected information. The settlement unit is implemented by, for example, the control unit 46A of the robot 414 and solicits donations using an electronic payment system. The publication unit is implemented by, for example, the control unit 46A of the robot 414 and publishes the results and experiences of volunteer activities. The market unit is implemented by, for example, the control unit 46A of the robot 414 and operates a market where users can exchange goods. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides an evaluation system for volunteer activities.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The volunteer activity support system can also include a health management unit that monitors the user's health status. The health management unit collects user health data and evaluates the user's suitability for volunteer activities based on this data. For example, it collects user heart rate, steps, and sleep data and adjusts activities to avoid excessive strain. It can also suggest more physically demanding activities to users in good health. Furthermore, the health management unit can suggest timings for rest and nutrition based on the user's health condition. This allows for the provision of optimal volunteer activities while maintaining the user's health.

[0103] The volunteer activity support system can also include a skills assessment unit that evaluates users' skills. This unit assesses users' skills based on their past volunteer activities and work experience, and suggests appropriate volunteer activities. For example, in educational support activities, it prioritizes matching users with education-related experience or qualifications. Similarly, in environmental protection activities, it can prioritize matching users with knowledge of environmental science. Furthermore, the skills assessment unit can suggest training programs to improve users' skills. This allows for the maximum utilization of users' skills and enables effective volunteer activities.

[0104] The volunteer activity support system can also include a feedback unit that estimates the user's emotions and provides feedback on the volunteer activity based on those estimated emotions. The feedback unit analyzes the user's emotions and improves motivation by providing positive feedback. For example, if the user feels satisfied, it provides feedback that emphasizes that emotion. If the user is dissatisfied, it can identify the cause and suggest solutions. Furthermore, the feedback unit can also suggest future volunteer activities based on the user's emotions. This allows for feedback tailored to the user's emotions, thereby improving satisfaction with volunteer activities.

[0105] The volunteer activity support system may also include a scheduling unit that estimates the user's emotions and adjusts the volunteer activity schedule based on those emotions. The scheduling unit analyzes the user's emotions and proposes an optimal schedule. For example, if the user is feeling stressed, it will prioritize relaxing activities in the schedule. Conversely, if the user is excited, it may suggest challenging activities. Furthermore, the scheduling unit can also suggest breaks and refreshment times according to the user's emotions. This allows for a schedule tailored to the user's emotions, maximizing the effectiveness of volunteer activities.

[0106] The volunteer activity support system may also include a reward adjustment unit that estimates the user's emotions and adjusts the reward for volunteer activities based on those emotions. The reward adjustment unit analyzes the user's emotions and provides appropriate rewards. For example, if the user is feeling satisfied, it may provide additional rewards to reinforce that feeling. If the user is feeling dissatisfied, it may identify the cause and adjust the reward to improve motivation. Furthermore, the reward adjustment unit can also suggest rewards for the next volunteer activity based on the user's emotions. This allows for the provision of rewards that match the user's emotions and improves the satisfaction level of volunteer activities.

[0107] The volunteer activity support system can also include an interest analysis unit that analyzes user interests and suggests volunteer activities based on those interests. The interest analysis unit analyzes the user's past activity history and search history to suggest the most suitable volunteer activities. For example, if a user has previously participated in educational support activities, similar activities will be suggested. If a user is interested in environmental protection, environmental protection activities can also be suggested. Furthermore, the interest analysis unit can also suggest new activities based on the user's interests. This allows for the provision of volunteer activities tailored to the user's interests, thereby increasing their motivation to participate.

[0108] The volunteer activity support system can also include a location information utilization unit that leverages the user's geographical location information to suggest the most suitable volunteer activities. This unit analyzes the user's current location and travel history to suggest nearby volunteer activities. For example, if a user is staying in a specific area, it will prioritize suggesting volunteer activities taking place in that area. It can also suggest volunteer activities along the user's commute route. Furthermore, based on the user's travel history, the location information utilization unit can suggest volunteer activities in new areas. This allows the system to provide optimal volunteer activities by utilizing the user's geographical location information.

[0109] The volunteer activity support system can also include a social media analytics department that analyzes users' social media activity and suggests relevant volunteer activities. The social media analytics department analyzes users' posts and engagement rates to suggest the most suitable volunteer activities. For example, if a user frequently posts about environmental protection, it will suggest environmental protection activities. It can also analyze the interests of the user's followers and suggest relevant volunteer activities. Furthermore, the social media analytics department can analyze trends in the user's social media activity and suggest new volunteer activities. This allows the system to analyze users' social media activity and provide the most suitable volunteer activities.

[0110] The volunteer activity support system can also include an evaluation data analysis unit that analyzes the user's past evaluation data and proposes the most suitable volunteer activities. The evaluation data analysis unit analyzes the user's past evaluation results and proposes the most suitable volunteer activities. For example, it can prioritize suggesting activities in which the user has received high ratings in the past. It can also suggest avoiding activities in which the user has received low ratings. Furthermore, the evaluation data analysis unit can also suggest new activities based on the user's evaluation data. This allows the system to provide optimal volunteer activities by utilizing the user's past evaluation data.

[0111] The volunteer activity support system can also include a feedback unit that estimates the user's emotions and provides feedback on the volunteer activity based on those estimated emotions. The feedback unit analyzes the user's emotions and improves motivation by providing positive feedback. For example, if the user feels satisfied, it provides feedback that emphasizes that emotion. If the user is dissatisfied, it can identify the cause and suggest solutions. Furthermore, the feedback unit can also suggest future volunteer activities based on the user's emotions. This allows for feedback tailored to the user's emotions, thereby improving satisfaction with volunteer activities.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit gathers information about volunteer activities. This information includes details of the volunteer activity, such as the date, time, and location. The collection unit also has a function that allows each organization to post volunteer recruitment information. Step 2: The matching department matches volunteers with suitable activities based on the information collected by the data collection department. Matching is based on the user's interests, skills, geographical location, etc. The matching department has a function that allows users to search for activities they are interested in and apply to participate. Step 3: The payment department collects donations through an electronic payment system. This system includes credit cards, e-money, and bank transfers. The payment department has the functionality to collect donations using the electronic payment system and utilize them to support volunteer activities.

[0114] 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.

[0115] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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. 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.

[0132] 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.

[0133] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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. 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.

[0148] 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.

[0149] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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. 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.

[0165] 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.

[0166] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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. A collection department that collects information on volunteer activities, A matching unit that matches volunteers based on the information collected by the aforementioned collection unit, It includes a payment department that collects donations using an electronic payment system. A system characterized by the following features.

2. It has a public section where volunteer activity records or experiences are made public. The system according to feature 1.

3. It has a market department that operates the exchange market. The system according to feature 1.

4. It has an evaluation department that provides an evaluation system for volunteer activities. The system according to feature 1.

5. The aforementioned collection unit is Each organization can post volunteer recruitment information. The system according to feature 1.

6. The matching unit is Users can search for activities they are interested in and register to participate. The system according to feature 1.

7. The aforementioned settlement unit, We will collect donations using an electronic payment system and use them to support volunteer activities. The system according to feature 1.

8. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of collecting volunteer recruitment information based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned collection unit is We will analyze the past recruitment history of each organization and select the appropriate collection method. The system according to feature 1.

Images