system

Abstract

The system according to the embodiment aims to analyze user information and suggest optimal volunteer activities and organizations. [Solution] A system according to an embodiment includes a collection unit, a suggestion unit, and a learning unit. The collection unit collects user information. The suggestion unit analyzes the information collected by the collection unit and suggests appropriate volunteer activities and organizations. The learning unit collects user feedback and learns from it.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty suggesting optimal volunteer activities and organizations based on a user's interests and skills, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user information and suggest optimal volunteer activities and organizations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a suggestion unit, and a learning unit. The collection unit collects user information. The suggestion unit analyzes the information collected by the collection unit and suggests appropriate volunteer activities and organizations. The learning unit collects user feedback and learns from it. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user information and suggest optimal volunteer activities and organizations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the volunteer matching system of the present invention, users enter information such as their interests, skills, desired activities, and past volunteer experience when registering. Based on this information, AI then suggests the most suitable volunteer activities and organizations. Users select from the proposed options and apply to participate. Once a match is made, they can begin the activity. The AI ​​learns from user feedback and provides more appropriate matches. This system also aims to maximize the use of data from the volunteer matching system to connect organizations and local governments, fostering community building by openly sharing information such as diverse and multigenerational individuals and past volunteer history. This will realize the world's largest open volunteer platform. For example, users enter information such as their interests, skills, desired activities, and past volunteer experience. This information is collected by a collection unit. Next, a proposal unit analyzes the information collected by the collection unit and suggests the most suitable volunteer activities and organizations. The user then applies to participate from the proposed options, and a match is made. After the activity, the user provides feedback, which is collected and learned by a learning unit. This allows the AI ​​to provide more appropriate matches. Furthermore, the data management unit manages data shared by each organization and local government, such as "activity content," "required skills," and "activity results." Based on this data, the community formation unit promotes community formation. In this way, the entire system works together to enhance community formation while providing users with the most suitable volunteer activities. This allows the volunteer matching system to efficiently collect, analyze, suggest, and learn from user information and provide the most suitable volunteer activities. Furthermore, the data shared by each organization and local government can be efficiently managed to promote community formation.

[0029] A volunteer matching system according to an embodiment includes a collection unit, a suggestion unit, and a learning unit. The collection unit collects information such as a user's "interests," "skills," "activities the user wants to try," and "past volunteer experience" when the user registers. The collection unit collects information from the user, for example, in the form of a questionnaire. The collection unit can also automatically collect a history of volunteer activities the user has participated in in the past. For example, the collection unit can collect information such as the types and duration of volunteer activities the user has participated in in the past and the skills the user has acquired. The suggestion unit analyzes the information collected by the collection unit and suggests optimal volunteer activities and organizations. The suggestion unit makes optimal suggestions based on the user's interests and skills, for example, using data mining technology. The suggestion unit can also make suggestions based on the user's past activity history and feedback using a machine learning algorithm. For example, the suggestion unit can analyze feedback on volunteer activities the user has participated in in the past and suggest similar activities. The learning unit collects and learns from user feedback. For example, the learning unit collects feedback provided by the user on proposed volunteer activities. The learning unit can also update the machine learning algorithm based on the user's feedback to improve the accuracy of suggestions. For example, if a user gives a high rating to a proposed activity, the learning unit can increase the number of suggestions related to that activity. This allows the volunteer matching system according to the embodiment to efficiently collect, analyze, suggest, and learn from user information, and provide optimal volunteer activities.

[0030] The volunteer matching system further includes a data management unit that manages data shared by each organization and local government. The data management unit manages data such as "activity details," "required skills," and "activity results" shared by each organization and local government. The data management unit efficiently manages this data using, for example, a database. The data management unit can also perform access control to ensure data security. For example, the data management unit authenticates each organization or local government when accessing data to prevent unauthorized access. Furthermore, the data management unit can regularly back up data to ensure data integrity. For example, the data management unit backs up data daily to prepare for data loss or corruption. In this way, the data management unit can efficiently manage data shared by each organization and local government and ensure data security and integrity.

[0031] The volunteer matching system further includes a community formation unit that promotes community formation based on the data managed by the data management unit. The community formation unit promotes community formation based on the data managed by the data management unit. The community formation unit provides a place where users can interact with each other through, for example, an online forum. The community formation unit can also hold events to provide opportunities for users to interact directly. For example, the community formation unit can regularly hold seminars and workshops on volunteer activities to provide a place where users can share information. Furthermore, the community formation unit can link with a social networking site to provide a platform where users can share the results of their activities. For example, the community formation unit encourages users to post photos and impressions of their volunteer activities on the social networking site and share them with other users. In this way, the community formation unit can efficiently promote community formation based on the data and deepen interactions between users.

[0032] The collection unit can collect the user's "interests," "skills," "activities they would like to try," "past volunteer experience," and other related information. For example, the collection unit prompts the user to enter information such as "interests," "skills," "activities they would like to try," and "past volunteer experience" in the form of a questionnaire when registering. The collection unit can also automatically collect a history of volunteer activities the user has participated in in the past. For example, the collection unit can collect information such as the types and duration of volunteer activities the user has participated in in the past, and the skills they have acquired. Furthermore, the collection unit can analyze the user's social media activities and collect related information. For example, if the user posts frequently about "environmental protection" on social media, the collection unit can collect information about volunteer activities related to environmental protection. In this way, the collection unit can collect more detailed information about the user and make more appropriate suggestions.

[0033] The suggestion unit can suggest optimal volunteer activities and organizations based on the collected information. For example, the suggestion unit uses data mining technology to make optimal suggestions based on the user's interests and skills. The suggestion unit can also use machine learning algorithms to make suggestions based on the user's past activity history and feedback. For example, the suggestion unit can analyze feedback on volunteer activities the user has participated in in the past and suggest similar activities. Furthermore, the suggestion unit can filter information based on the user's current living situation and areas of interest to make optimal suggestions. For example, if the user inputs that they are busy as their current living situation, the suggestion unit will prioritize suggesting information about volunteer activities that can be completed in a short amount of time. This enables the suggestion unit to make optimal suggestions based on the collected information.

[0034] The learning unit can collect and learn from user feedback. For example, the learning unit collects feedback provided by users on proposed volunteer activities. The learning unit can also update the machine learning algorithm based on user feedback to improve the accuracy of suggestions. For example, if a user gives a high rating to a proposed activity, the learning unit can increase the number of suggestions related to that activity. Furthermore, the learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, the learning unit preferentially selects positive feedback as learning data. In this way, the learning unit learns user feedback and improves the accuracy of the system.

[0035] The collection unit can analyze the user's past volunteer experiences and select the optimal information collection method. For example, the collection unit analyzes the types of volunteer activities the user has participated in in the past and prioritizes collecting information about similar activities. The collection unit can also analyze the time periods in which the user has preferred to participate in activities in the past and collect information tailored to those time periods. Furthermore, the collection unit can analyze feedback on activities the user has participated in in the past and collect information about activities that have received a lot of positive feedback. For example, the collection unit can select the optimal information collection method based on the history of volunteer activities the user has participated in in the past. This allows the collection unit to collect more appropriate information by analyzing the user's past volunteer experiences.

[0036] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user inputs that they are busy as their current living situation, the collection unit prioritizes collecting information about volunteer activities that can be completed in a short amount of time. Furthermore, if the user selects environmental protection as their area of ​​interest, the collection unit can also collect information about volunteer activities related to environmental protection. Furthermore, if the user inputs that they are working remotely as their current living situation, the collection unit can also collect information about volunteer activities that can be completed online. For example, the collection unit can filter and collect optimal information based on the user's living situation and areas of interest. This allows the collection unit to filter information based on the user's living situation and areas of interest, thereby enabling more appropriate information collection.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user's current location is in an urban area, the collection unit can prioritize collecting information about volunteer activities held in the urban area. Furthermore, when the user's current location is in a suburban area, the collection unit can also prioritize collecting information about volunteer activities held in the suburban area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting information about volunteer activities that the user can participate in at their travel destination. For example, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. This allows the collection unit to collect more relevant information by taking into account the user's geographical location information.

[0038] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, if the user posts many times on social media about "environmental protection," the collection unit can collect information about volunteer activities related to environmental protection. Furthermore, if the user posts many times on social media about "education," the collection unit can also collect information about volunteer activities related to education. Furthermore, if the user posts many times on social media about "animal protection," the collection unit can also collect information about volunteer activities related to animal protection. For example, the collection unit can analyze the user's social media activities and collect related information. This allows the collection unit to collect more relevant information by analyzing the user's social media activities.

[0039] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the volunteer activity. For example, the suggestion unit provides detailed information for volunteer activities with high importance. The suggestion unit can also provide concise information for volunteer activities with low importance. Furthermore, the suggestion unit can also provide information with an appropriate level of detail for volunteer activities with medium importance. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the volunteer activity. This allows the suggestion unit to make a more appropriate proposal by adjusting the level of detail of the proposal based on the importance of the volunteer activity.

[0040] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the volunteer activity. For example, the suggestion unit can apply a suggestion algorithm specialized for environmental protection to a volunteer activity related to environmental protection. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for education to a volunteer activity related to education. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for animal protection to a volunteer activity related to animal protection. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the volunteer activity. This enables the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the category of the volunteer activity.

[0041] When making a proposal, the proposal unit can determine the priority of the proposals based on the timing of the implementation of the volunteer activities. For example, the proposal unit gives priority to volunteer activities that will be implemented soon. The proposal unit can also postpone the proposal of volunteer activities that will be implemented over the long term. Furthermore, the proposal unit can also propose volunteer activities that will be implemented periodically at an appropriate timing. For example, the proposal unit can determine the priority of the proposals based on the timing of the implementation of the volunteer activities. This enables the proposal unit to make more appropriate proposals by determining the priority of the proposals based on the timing of the implementation of the volunteer activities.

[0042] When suggesting volunteer activities, the suggestion unit can adjust the order of suggestions based on the relevance of the volunteer activities. For example, the suggestion unit first suggests volunteer activities that are most relevant to the user's interests and skills. The suggestion unit can also prioritize suggesting highly relevant activities based on the user's past volunteer experience. Furthermore, the suggestion unit can prioritize suggesting volunteer activities that are most suitable for the user's current living situation. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the volunteer activities. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the volunteer activities.

[0043] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, analyzes past learning data and selects the most effective learning algorithm. The learning unit can also optimize the parameters of the learning algorithm from the past learning data. Furthermore, the learning unit can develop a new learning algorithm based on the past learning data. For example, the learning unit can optimize the learning algorithm by referring to the past learning data. This makes it possible for the learning unit to optimize the learning algorithm by referring to the past learning data.

[0044] During learning, the learning unit can improve the accuracy of the learning based on the level of detail of the user's feedback. For example, the learning unit preferentially learns data of users who provide detailed feedback. The learning unit can also supplementarily learn data of users who provide concise feedback. Furthermore, the learning unit can adjust parameters of the learning algorithm according to the level of detail of the feedback. For example, the learning unit can improve the accuracy of the learning algorithm based on data of users who provide detailed feedback. This allows the learning unit to improve the accuracy of learning based on the level of detail of the feedback, thereby enabling more appropriate learning.

[0045] During learning, the learning unit can weight the learning data based on the time of feedback submission. For example, the learning unit prioritizes weighting of recent feedback as learning data. The learning unit can also weight past feedback as complementary learning data. Furthermore, the learning unit can adjust parameters of the learning algorithm depending on the time of feedback submission. For example, the learning unit can weight the learning data based on the time of feedback submission. This allows the learning unit to weight the learning data based on the time of feedback submission, thereby enabling more appropriate learning.

[0046] During learning, the learning unit can determine the priority of learning data based on the relevance of the feedback. For example, the learning unit prioritizes learning feedback that is most relevant to the user's interests and skills. The learning unit can also prioritize learning highly relevant feedback based on the user's past volunteer experience. Furthermore, the learning unit can also prioritize learning feedback that is most suitable for the user's current living situation. For example, the learning unit can determine the priority of learning data based on the relevance of the feedback. This allows the learning unit to prioritize learning data based on the relevance of the feedback, thereby enabling more appropriate learning.

[0047] When managing data, the data management unit can refer to past data management history to select the optimal management method. For example, the data management unit can refer to data management methods that have been successful in the past and apply similar methods. The data management unit can also avoid data management methods that have failed in the past and apply improved methods. Furthermore, the data management unit can analyze past data management history to select the optimal management method. For example, the data management unit can refer to past data management history to select the optimal management method. This makes it possible for the data management unit to select the optimal data management method by referring to past data management history.

[0048] The data management unit can adjust the level of detail of management based on the importance of the data when managing data. For example, the data management unit applies a detailed management method to data of high importance. The data management unit can also apply a simple management method to data of low importance. Furthermore, the data management unit can also apply a management method of appropriate detail to data of medium importance. For example, the data management unit can adjust the level of detail of management based on the importance of the data. This allows the data management unit to adjust the level of detail of management based on the importance of the data, thereby enabling more appropriate data management.

[0049] During data management, the data management unit can adjust the management priority based on the time of data submission. For example, the data management unit prioritizes management of recently submitted data. The data management unit can also manage data submitted in the past in a complementary manner. Furthermore, the data management unit can adjust the management priority according to the time of data submission. For example, the data management unit can adjust the management priority based on the time of data submission. This allows the data management unit to adjust the management priority based on the time of data submission, thereby enabling more appropriate data management.

[0050] During data management, the data management unit can adjust the management order based on the relevance of the data. For example, the data management unit prioritizes management of data that is most relevant to the user's interests and skills. The data management unit can also prioritize management of highly relevant data based on the user's past volunteer experience. Furthermore, the data management unit can also prioritize management of data that is most suitable for the user's current living situation. For example, the data management unit can adjust the management order based on the relevance of the data. This allows the data management unit to adjust the management order based on the relevance of the data, thereby enabling more appropriate data management.

[0051] When forming a community, the community formation unit can select an optimal formation method by referring to past community formation history. For example, the community formation unit can refer to a community formation method that was successful in the past and apply a similar method. The community formation unit can also avoid a community formation method that failed in the past and apply an improved method. Furthermore, the community formation unit can analyze past community formation history and select an optimal formation method. For example, the community formation unit can select an optimal formation method by referring to past community formation history. This makes it possible for the community formation unit to select an optimal community formation method by referring to past community formation history.

[0052] The community formation unit can adjust the level of detail of the formation based on the importance of the community when forming the community. For example, the community formation unit applies a detailed formation method to a community with high importance. The community formation unit can also apply a simple formation method to a community with low importance. Furthermore, the community formation unit can also apply a formation method with an appropriate level of detail to a community with medium importance. For example, the community formation unit can adjust the level of detail of the formation based on the importance of the community. In this way, the community formation unit can adjust the level of detail of the formation based on the importance of the community, thereby enabling more appropriate community formation.

[0053] When forming a community, the community formation unit can adjust the formation priority based on the time of community formation. For example, the community formation unit can prioritize the formation of a community that was formed most recently. The community formation unit can also postpone the formation of a community that will be formed over the long term. Furthermore, the community formation unit can also form communities that are formed periodically at an appropriate timing. For example, the community formation unit can adjust the formation priority based on the time of community formation. This allows the community formation unit to adjust the formation priority based on the time of community formation, thereby enabling more appropriate community formation.

[0054] The community formation unit can adjust the order of community formation based on the relevance of the communities when forming communities. For example, the community formation unit first forms a community that is most relevant to the user's interests and skills. The community formation unit can also prioritize forming highly relevant communities based on the user's past volunteer experience. Furthermore, the community formation unit can also prioritize forming a community that is most suitable for the user's current living situation. For example, the community formation unit can adjust the order of community formation based on the relevance of the communities. This allows the community formation unit to form more appropriate communities by adjusting the order of community formation based on the relevance of the communities.

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

[0056] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user's current location is in an urban area, the collection unit can prioritize collecting information about volunteer activities held in urban areas. Also, if the user's current location is in a suburban area, the collection unit can prioritize collecting information about volunteer activities held in the suburban area. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about volunteer activities that the user can participate in at their travel destination. This allows the collection unit to collect more relevant information by taking into account the user's geographical location information.

[0057] When managing data, the data management unit can refer to past data management history to select the optimal management method. For example, it can refer to data management methods that have been successful in the past and apply similar methods. It can also avoid data management methods that have failed in the past and apply improved methods. It can also analyze past data management history to select the optimal management method. This allows the data management unit to select the optimal data management method by referring to past data management history.

[0058] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the volunteer activity. For example, detailed information can be provided for volunteer activities with high importance. Brief information can also be provided for volunteer activities with low importance. Furthermore, information with an appropriate level of detail can be provided for volunteer activities with medium importance. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the volunteer activity, thereby enabling more appropriate proposals.

[0059] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of the volunteer activity. For example, volunteer activities that will be carried out in the near future can be proposed as a priority. Volunteer activities that will be carried out over the long term can also be proposed later. Furthermore, volunteer activities that will be carried out periodically can be proposed at an appropriate time. In this way, the proposal unit can determine the priority of the proposal based on the timing of the volunteer activity, thereby enabling more appropriate proposals.

[0060] During learning, the learning unit can weight the learning data based on the time of feedback submission. For example, recent feedback can be weighted preferentially as learning data. Also, past feedback can be weighted as complementary learning data. Furthermore, the parameters of the learning algorithm can be adjusted depending on the time of feedback submission. In this way, the learning unit can weight the learning data based on the time of feedback submission, thereby enabling more appropriate learning.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: When a user registers, the collection unit collects information such as "interests," "skills," "activities they would like to try," and "past volunteer experience." The collection unit may collect information from the user, for example, in the form of a questionnaire. The collection unit may also automatically collect a history of volunteer activities the user has participated in in the past. For example, the collection unit may collect information such as the types and duration of volunteer activities the user has participated in in the past, and the skills they have acquired. Step 2: The suggestion unit analyzes the information collected by the collection unit and suggests optimal volunteer activities and organizations. For example, the suggestion unit uses data mining technology to make optimal suggestions based on the user's interests and skills. The suggestion unit can also use machine learning algorithms to make suggestions based on the user's past activity history and feedback. For example, the suggestion unit can analyze feedback on volunteer activities the user has participated in in the past and suggest similar activities. Step 3: The learning unit collects and learns from user feedback. For example, the learning unit collects feedback provided by users on suggested volunteer activities. The learning unit can also update the machine learning algorithm based on user feedback to improve the accuracy of suggestions. For example, if a user gives a high rating to a suggested activity, the learning unit can increase the number of suggestions related to that activity.

[0063] (Example 2) In an embodiment of the volunteer matching system of the present invention, users enter information such as their interests, skills, desired activities, and past volunteer experience when registering. Based on this information, AI then suggests the most suitable volunteer activities and organizations. Users select from the proposed options and apply to participate. Once a match is made, they can begin the activity. The AI ​​learns from user feedback and provides more appropriate matches. This system also aims to maximize the use of data from the volunteer matching system to connect organizations and local governments, fostering community building by openly sharing information such as diverse and multigenerational individuals and past volunteer history. This will realize the world's largest open volunteer platform. For example, users enter information such as their interests, skills, desired activities, and past volunteer experience. This information is collected by a collection unit. Next, a proposal unit analyzes the information collected by the collection unit and suggests the most suitable volunteer activities and organizations. The user then applies to participate from the proposed options, and a match is made. After the activity, the user provides feedback, which is collected and learned by a learning unit. This allows the AI ​​to provide more appropriate matches. Furthermore, the data management unit manages data shared by each organization and local government, such as "activity content," "required skills," and "activity results." Based on this data, the community formation unit promotes community formation. In this way, the entire system works together to enhance community formation while providing users with the most suitable volunteer activities. This allows the volunteer matching system to efficiently collect, analyze, suggest, and learn from user information and provide the most suitable volunteer activities. Furthermore, the data shared by each organization and local government can be efficiently managed to promote community formation.

[0064] A volunteer matching system according to an embodiment includes a collection unit, a suggestion unit, and a learning unit. The collection unit collects information such as a user's "interests," "skills," "activities the user wants to try," and "past volunteer experience" when the user registers. The collection unit collects information from the user, for example, in the form of a questionnaire. The collection unit can also automatically collect a history of volunteer activities the user has participated in in the past. For example, the collection unit can collect information such as the types and duration of volunteer activities the user has participated in in the past and the skills the user has acquired. The suggestion unit analyzes the information collected by the collection unit and suggests optimal volunteer activities and organizations. The suggestion unit makes optimal suggestions based on the user's interests and skills, for example, using data mining technology. The suggestion unit can also make suggestions based on the user's past activity history and feedback using a machine learning algorithm. For example, the suggestion unit can analyze feedback on volunteer activities the user has participated in in the past and suggest similar activities. The learning unit collects and learns from user feedback. For example, the learning unit collects feedback provided by the user on proposed volunteer activities. The learning unit can also update the machine learning algorithm based on the user's feedback to improve the accuracy of suggestions. For example, if a user gives a high rating to a proposed activity, the learning unit can increase the number of suggestions related to that activity. This allows the volunteer matching system according to the embodiment to efficiently collect, analyze, suggest, and learn from user information, and provide optimal volunteer activities.

[0065] The volunteer matching system further includes a data management unit that manages data shared by each organization and local government. The data management unit manages data such as "activity details," "required skills," and "activity results" shared by each organization and local government. The data management unit efficiently manages this data using, for example, a database. The data management unit can also perform access control to ensure data security. For example, the data management unit authenticates each organization or local government when accessing data to prevent unauthorized access. Furthermore, the data management unit can regularly back up data to ensure data integrity. For example, the data management unit backs up data daily to prepare for data loss or corruption. In this way, the data management unit can efficiently manage data shared by each organization and local government and ensure data security and integrity.

[0066] The volunteer matching system further includes a community formation unit that promotes community formation based on the data managed by the data management unit. The community formation unit promotes community formation based on the data managed by the data management unit. The community formation unit provides a place where users can interact with each other through, for example, an online forum. The community formation unit can also hold events to provide opportunities for users to interact directly. For example, the community formation unit can regularly hold seminars and workshops on volunteer activities to provide a place where users can share information. Furthermore, the community formation unit can link with a social networking site to provide a platform where users can share the results of their activities. For example, the community formation unit encourages users to post photos and impressions of their volunteer activities on the social networking site and share them with other users. In this way, the community formation unit can efficiently promote community formation based on the data and deepen interactions between users.

[0067] The collection unit can collect the user's "interests," "skills," "activities they would like to try," "past volunteer experience," and other related information. For example, the collection unit prompts the user to enter information such as "interests," "skills," "activities they would like to try," and "past volunteer experience" in the form of a questionnaire when registering. The collection unit can also automatically collect a history of volunteer activities the user has participated in in the past. For example, the collection unit can collect information such as the types and duration of volunteer activities the user has participated in in the past, and the skills they have acquired. Furthermore, the collection unit can analyze the user's social media activities and collect related information. For example, if the user posts frequently about "environmental protection" on social media, the collection unit can collect information about volunteer activities related to environmental protection. In this way, the collection unit can collect more detailed information about the user and make more appropriate suggestions.

[0068] The suggestion unit can suggest optimal volunteer activities and organizations based on the collected information. For example, the suggestion unit uses data mining technology to make optimal suggestions based on the user's interests and skills. The suggestion unit can also use machine learning algorithms to make suggestions based on the user's past activity history and feedback. For example, the suggestion unit can analyze feedback on volunteer activities the user has participated in in the past and suggest similar activities. Furthermore, the suggestion unit can filter information based on the user's current living situation and areas of interest to make optimal suggestions. For example, if the user inputs that they are busy as their current living situation, the suggestion unit will prioritize suggesting information about volunteer activities that can be completed in a short amount of time. This enables the suggestion unit to make optimal suggestions based on the collected information.

[0069] The learning unit can collect and learn from user feedback. For example, the learning unit collects feedback provided by users on proposed volunteer activities. The learning unit can also update the machine learning algorithm based on user feedback to improve the accuracy of suggestions. For example, if a user gives a high rating to a proposed activity, the learning unit can increase the number of suggestions related to that activity. Furthermore, the learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, the learning unit preferentially selects positive feedback as learning data. In this way, the learning unit learns user feedback and improves the accuracy of the system.

[0070] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit delays the timing of collection so that the user can input information in a relaxed state. Furthermore, if the user is excited, the collection unit can collect information immediately and make suggestions before the user's interest cools. Furthermore, if the user is tired, the collection unit can adjust the timing of collection and collect information after the user has rested. For example, if the user is relaxed, the collection unit can increase the frequency of information collection. This allows the collection unit to adjust the timing of information collection according to the user's emotions, thereby enabling more appropriate information collection.

[0071] The collection unit can analyze the user's past volunteer experiences and select the optimal information collection method. For example, the collection unit analyzes the types of volunteer activities the user has participated in in the past and prioritizes collecting information about similar activities. The collection unit can also analyze the time periods in which the user has preferred to participate in activities in the past and collect information tailored to those time periods. Furthermore, the collection unit can analyze feedback on activities the user has participated in in the past and collect information about activities that have received a lot of positive feedback. For example, the collection unit can select the optimal information collection method based on the history of volunteer activities the user has participated in in the past. This allows the collection unit to collect more appropriate information by analyzing the user's past volunteer experiences.

[0072] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user inputs that they are busy as their current living situation, the collection unit prioritizes collecting information about volunteer activities that can be completed in a short amount of time. Furthermore, if the user selects environmental protection as their area of ​​interest, the collection unit can also collect information about volunteer activities related to environmental protection. Furthermore, if the user inputs that they are working remotely as their current living situation, the collection unit can also collect information about volunteer activities that can be completed online. For example, the collection unit can filter and collect optimal information based on the user's living situation and areas of interest. This allows the collection unit to filter information based on the user's living situation and areas of interest, thereby enabling more appropriate information collection.

[0073] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information about relaxing volunteer activities. Furthermore, if the user is excited, the collection unit can also prioritize collecting information about challenging volunteer activities. Furthermore, if the user is tired, the collection unit can also prioritize collecting information about less burdensome volunteer activities. For example, the collection unit can determine the priority of information to be collected based on the user's emotions. This allows the collection unit to prioritize information according to the user's emotions, thereby enabling more appropriate information collection.

[0074] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user's current location is in an urban area, the collection unit can prioritize collecting information about volunteer activities held in the urban area. Furthermore, when the user's current location is in a suburban area, the collection unit can also prioritize collecting information about volunteer activities held in the suburban area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting information about volunteer activities that the user can participate in at their travel destination. For example, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. This allows the collection unit to collect more relevant information by taking into account the user's geographical location information.

[0075] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, if the user posts many times on social media about "environmental protection," the collection unit can collect information about volunteer activities related to environmental protection. Furthermore, if the user posts many times on social media about "education," the collection unit can also collect information about volunteer activities related to education. Furthermore, if the user posts many times on social media about "animal protection," the collection unit can also collect information about volunteer activities related to animal protection. For example, the collection unit can analyze the user's social media activities and collect related information. This allows the collection unit to collect more relevant information by analyzing the user's social media activities.

[0076] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit can make suggestions that include detailed explanations. Furthermore, when the user is in a hurry, the suggestion unit can make suggestions that are concise and to the point. Furthermore, when the user is excited, the suggestion unit can make visually appealing suggestions. For example, the suggestion unit can adjust the way in which suggestions are expressed based on the user's emotions. This allows the suggestion unit to make more appropriate suggestions by adjusting the way in which suggestions are expressed in accordance with the user's emotions.

[0077] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the volunteer activity. For example, the suggestion unit provides detailed information for volunteer activities with high importance. The suggestion unit can also provide concise information for volunteer activities with low importance. Furthermore, the suggestion unit can also provide information with an appropriate level of detail for volunteer activities with medium importance. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the volunteer activity. This allows the suggestion unit to make a more appropriate proposal by adjusting the level of detail of the proposal based on the importance of the volunteer activity.

[0078] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the volunteer activity. For example, the suggestion unit can apply a suggestion algorithm specialized for environmental protection to a volunteer activity related to environmental protection. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for education to a volunteer activity related to education. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for animal protection to a volunteer activity related to animal protection. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the volunteer activity. This enables the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the category of the volunteer activity.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can make a visually stimulating suggestion. For example, the suggestion unit can adjust the length of the suggestion based on the user's emotions. This allows the suggestion unit to make more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions.

[0080] When making a proposal, the proposal unit can determine the priority of the proposals based on the timing of the implementation of the volunteer activities. For example, the proposal unit gives priority to volunteer activities that will be implemented soon. The proposal unit can also postpone the proposal of volunteer activities that will be implemented over the long term. Furthermore, the proposal unit can also propose volunteer activities that will be implemented periodically at an appropriate timing. For example, the proposal unit can determine the priority of the proposals based on the timing of the implementation of the volunteer activities. This enables the proposal unit to make more appropriate proposals by determining the priority of the proposals based on the timing of the implementation of the volunteer activities.

[0081] When suggesting volunteer activities, the suggestion unit can adjust the order of suggestions based on the relevance of the volunteer activities. For example, the suggestion unit first suggests volunteer activities that are most relevant to the user's interests and skills. The suggestion unit can also prioritize suggesting highly relevant activities based on the user's past volunteer experience. Furthermore, the suggestion unit can prioritize suggesting volunteer activities that are most suitable for the user's current living situation. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the volunteer activities. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the volunteer activities.

[0082] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, the learning unit can preferentially select positive feedback as learning data. Furthermore, if the user is feeling stressed, the learning unit can also preferentially select negative feedback as learning data. Furthermore, if the user is excited, the learning unit can also select feedback with large emotional fluctuations as learning data. For example, the learning unit can select learning data based on the user's emotions. This allows the learning unit to select learning data according to the user's emotions, thereby enabling more appropriate learning.

[0083] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, analyzes past learning data and selects the most effective learning algorithm. The learning unit can also optimize the parameters of the learning algorithm from the past learning data. Furthermore, the learning unit can develop a new learning algorithm based on the past learning data. For example, the learning unit can optimize the learning algorithm by referring to the past learning data. This makes it possible for the learning unit to optimize the learning algorithm by referring to the past learning data.

[0084] During learning, the learning unit can improve the accuracy of the learning based on the level of detail of the user's feedback. For example, the learning unit preferentially learns data of users who provide detailed feedback. The learning unit can also supplementarily learn data of users who provide concise feedback. Furthermore, the learning unit can adjust parameters of the learning algorithm according to the level of detail of the feedback. For example, the learning unit can improve the accuracy of the learning algorithm based on data of users who provide detailed feedback. This allows the learning unit to improve the accuracy of learning based on the level of detail of the feedback, thereby enabling more appropriate learning.

[0085] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user's emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is feeling stressed. Furthermore, the learning unit can also adjust the frequency of learning appropriately when the user is excited. For example, the learning unit can adjust the frequency of learning based on the user's emotions. This allows the learning unit to adjust the frequency of learning according to the user's emotions, thereby enabling more appropriate learning.

[0086] During learning, the learning unit can weight the learning data based on the time of feedback submission. For example, the learning unit prioritizes weighting of recent feedback as learning data. The learning unit can also weight past feedback as complementary learning data. Furthermore, the learning unit can adjust parameters of the learning algorithm depending on the time of feedback submission. For example, the learning unit can weight the learning data based on the time of feedback submission. This allows the learning unit to weight the learning data based on the time of feedback submission, thereby enabling more appropriate learning.

[0087] During learning, the learning unit can determine the priority of learning data based on the relevance of the feedback. For example, the learning unit prioritizes learning feedback that is most relevant to the user's interests and skills. The learning unit can also prioritize learning highly relevant feedback based on the user's past volunteer experience. Furthermore, the learning unit can also prioritize learning feedback that is most suitable for the user's current living situation. For example, the learning unit can determine the priority of learning data based on the relevance of the feedback. This allows the learning unit to prioritize learning data based on the relevance of the feedback, thereby enabling more appropriate learning.

[0088] The data management unit can estimate the emotions of each organization or local government and adjust the data management method based on the estimated emotions. For example, if the organization has positive emotions, the data management unit can actively share data. Furthermore, if the organization has negative emotions, the data management unit can also carefully share data. Furthermore, if the organization has neutral emotions, the data management unit can also moderately share data. For example, the data management unit can adjust the data management method based on the emotions of each organization or local government. This allows the data management unit to adjust the data management method based on the emotions of each organization or local government, enabling more appropriate data management.

[0089] When managing data, the data management unit can refer to past data management history to select the optimal management method. For example, the data management unit can refer to data management methods that have been successful in the past and apply similar methods. The data management unit can also avoid data management methods that have failed in the past and apply improved methods. Furthermore, the data management unit can analyze past data management history to select the optimal management method. For example, the data management unit can refer to past data management history to select the optimal management method. This makes it possible for the data management unit to select the optimal data management method by referring to past data management history.

[0090] The data management unit can adjust the level of detail of management based on the importance of the data when managing data. For example, the data management unit applies a detailed management method to data of high importance. The data management unit can also apply a simple management method to data of low importance. Furthermore, the data management unit can also apply a management method of appropriate detail to data of medium importance. For example, the data management unit can adjust the level of detail of management based on the importance of the data. This allows the data management unit to adjust the level of detail of management based on the importance of the data, thereby enabling more appropriate data management.

[0091] The data management unit can estimate the emotions of each organization or local government and determine the priority of data based on the estimated emotions. For example, if an organization has positive emotions, the data management unit prioritizes management of that organization's data. In addition, if an organization has negative emotions, the data management unit can also manage that organization's data later. Furthermore, if an organization has neutral emotions, the data management unit can also manage that organization's data moderately. For example, the data management unit can determine the priority of data based on the emotions of each organization or local government. This allows the data management unit to prioritize data based on the emotions of each organization or local government, enabling more appropriate data management.

[0092] During data management, the data management unit can adjust the management priority based on the time of data submission. For example, the data management unit prioritizes management of recently submitted data. The data management unit can also manage data submitted in the past in a complementary manner. Furthermore, the data management unit can adjust the management priority according to the time of data submission. For example, the data management unit can adjust the management priority based on the time of data submission. This allows the data management unit to adjust the management priority based on the time of data submission, thereby enabling more appropriate data management.

[0093] During data management, the data management unit can adjust the management order based on the relevance of the data. For example, the data management unit prioritizes management of data that is most relevant to the user's interests and skills. The data management unit can also prioritize management of highly relevant data based on the user's past volunteer experience. Furthermore, the data management unit can also prioritize management of data that is most suitable for the user's current living situation. For example, the data management unit can adjust the management order based on the relevance of the data. This allows the data management unit to adjust the management order based on the relevance of the data, thereby enabling more appropriate data management.

[0094] The community formation unit can estimate the user's emotions and adjust the community formation method based on the estimated user's emotions. For example, if the user is relaxed, the community formation unit can proceed with community formation at a leisurely pace. Furthermore, if the user is excited, the community formation unit can also proceed with community formation proactively. Furthermore, if the user is feeling stressed, the community formation unit can proceed with community formation carefully. For example, the community formation unit can adjust the community formation method based on the user's emotions. In this way, the community formation unit can form a more appropriate community by adjusting the community formation method based on the user's emotions.

[0095] When forming a community, the community formation unit can select an optimal formation method by referring to past community formation history. For example, the community formation unit can refer to a community formation method that was successful in the past and apply a similar method. The community formation unit can also avoid a community formation method that failed in the past and apply an improved method. Furthermore, the community formation unit can analyze past community formation history and select an optimal formation method. For example, the community formation unit can select an optimal formation method by referring to past community formation history. This makes it possible for the community formation unit to select an optimal community formation method by referring to past community formation history.

[0096] The community formation unit can adjust the level of detail of the formation based on the importance of the community when forming the community. For example, the community formation unit applies a detailed formation method to a community with high importance. The community formation unit can also apply a simple formation method to a community with low importance. Furthermore, the community formation unit can also apply a formation method with an appropriate level of detail to a community with medium importance. For example, the community formation unit can adjust the level of detail of the formation based on the importance of the community. In this way, the community formation unit can adjust the level of detail of the formation based on the importance of the community, thereby enabling more appropriate community formation.

[0097] The community formation unit can estimate the user's emotions and determine the priority of community formation based on the estimated user's emotions. For example, the community formation unit can increase the priority of community formation when the user is relaxed. The community formation unit can also increase the priority of community formation when the user is excited. Furthermore, the community formation unit can also lower the priority of community formation when the user is feeling stressed. For example, the community formation unit can determine the priority of community formation based on the user's emotions. This allows the community formation unit to determine the priority of community formation based on the user's emotions, thereby enabling more appropriate community formation.

[0098] When forming a community, the community formation unit can adjust the formation priority based on the time of community formation. For example, the community formation unit can prioritize the formation of a community that was formed most recently. The community formation unit can also postpone the formation of a community that will be formed over the long term. Furthermore, the community formation unit can also form communities that are formed periodically at an appropriate timing. For example, the community formation unit can adjust the formation priority based on the time of community formation. This allows the community formation unit to adjust the formation priority based on the time of community formation, thereby enabling more appropriate community formation.

[0099] The community formation unit can adjust the order of community formation based on the relevance of the communities when forming communities. For example, the community formation unit first forms a community that is most relevant to the user's interests and skills. The community formation unit can also prioritize forming highly relevant communities based on the user's past volunteer experience. Furthermore, the community formation unit can also prioritize forming a community that is most suitable for the user's current living situation. For example, the community formation unit can adjust the order of community formation based on the relevance of the communities. This allows the community formation unit to form more appropriate communities by adjusting the order of community formation based on the relevance of the communities. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, suggestion unit, learning unit, data management unit, and community building unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the reception device 38 of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit, implemented by the specific processing unit 290 of the data processing device 12, suggests optimal volunteer activities and organizations based on the collected information. The learning unit, implemented by the specific processing unit 290 of the data processing device 12, collects user feedback and improves the accuracy of the suggestions. The data management unit manages data shared by organizations and local governments using the database 24 of the data processing device 12. The community building unit promotes interaction between users using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, learning unit, data management unit, and community building unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal volunteer activities and organizations based on the collected information. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve the accuracy of the suggestions. The data management unit manages data shared by organizations and local governments using the database 24 of the data processing device 12. The community building unit promotes interaction between users using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, suggestion unit, learning unit, data management unit, and community building unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the headset-type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal volunteer activities and organizations based on the collected information. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve the accuracy of the suggestions. The data management unit manages data shared by organizations and local governments using the database 24 of the data processing device 12. The community building unit promotes interaction between users using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, suggestion unit, learning unit, data management unit, and community building unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal volunteer activities and organizations based on the collected information. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve the accuracy of the suggestions. The data management unit manages data shared by various organizations and local governments using the database 24 of the data processing device 12. The community building unit promotes interaction between users using the speaker 240 of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide a suggestion with detailed explanations. If the user is in a hurry, the suggestion unit can provide a concise and to-the-point suggestion. Furthermore, if the user is excited, the suggestion unit can provide a visually appealing suggestion. This allows the suggestion unit to adjust the way suggestions are expressed according to the user's emotions, thereby enabling more appropriate suggestions.

[0102] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user's current location is in an urban area, the collection unit can prioritize collecting information about volunteer activities held in urban areas. Also, if the user's current location is in a suburban area, the collection unit can prioritize collecting information about volunteer activities held in the suburban area. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about volunteer activities that the user can participate in at their travel destination. This allows the collection unit to collect more relevant information by taking into account the user's geographical location information.

[0103] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, positive feedback can be preferentially selected as learning data. Also, if the user is stressed, negative feedback can be preferentially selected as learning data. Furthermore, if the user is excited, feedback with large emotional fluctuations can be selected as learning data. In this way, the learning unit can select learning data according to the user's emotions, enabling more appropriate learning.

[0104] When managing data, the data management unit can refer to past data management history to select the optimal management method. For example, it can refer to data management methods that have been successful in the past and apply similar methods. It can also avoid data management methods that have failed in the past and apply improved methods. It can also analyze past data management history to select the optimal management method. This allows the data management unit to select the optimal data management method by referring to past data management history.

[0105] The community formation unit can estimate the user's emotions and adjust the community formation method based on the estimated user's emotions. For example, if the user is relaxed, the community formation can proceed at a leisurely pace. If the user is excited, the community formation can proceed aggressively. Furthermore, if the user is feeling stressed, the community formation can proceed cautiously. In this way, the community formation unit can adjust the community formation method based on the user's emotions, thereby enabling more appropriate community formation.

[0106] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the volunteer activity. For example, detailed information can be provided for volunteer activities with high importance. Brief information can also be provided for volunteer activities with low importance. Furthermore, information with an appropriate level of detail can be provided for volunteer activities with medium importance. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the volunteer activity, thereby enabling more appropriate proposals.

[0107] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection timing can be delayed so that the user can enter information in a relaxed state. Also, if the user is excited, the collection timing can be adjusted so that the user can quickly collect information and make suggestions before their interest cools. Furthermore, if the user is tired, the collection timing can be adjusted so that the information is collected after the user has rested. In this way, the collection unit can adjust the timing of information collection according to the user's emotions, thereby enabling more appropriate information collection.

[0108] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of the volunteer activity. For example, volunteer activities that will be carried out in the near future can be proposed as a priority. Volunteer activities that will be carried out over the long term can also be proposed later. Furthermore, volunteer activities that will be carried out periodically can be proposed at an appropriate time. In this way, the proposal unit can determine the priority of the proposal based on the timing of the volunteer activity, thereby enabling more appropriate proposals.

[0109] During learning, the learning unit can weight the learning data based on the time of feedback submission. For example, recent feedback can be weighted preferentially as learning data. Also, past feedback can be weighted as complementary learning data. Furthermore, the parameters of the learning algorithm can be adjusted depending on the time of feedback submission. In this way, the learning unit can weight the learning data based on the time of feedback submission, thereby enabling more appropriate learning.

[0110] The data management unit can estimate the sentiment of each organization or local government and prioritize data based on the estimated sentiment. For example, if an organization has positive sentiment, the data of that organization can be managed preferentially. On the other hand, if an organization has negative sentiment, the data of that organization can be managed later. Furthermore, if an organization has neutral sentiment, the data of that organization can be managed moderately. This allows the data management unit to prioritize data based on the sentiment of each organization or local government, enabling more appropriate data management.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: When a user registers, the collection unit collects information such as "interests," "skills," "activities they would like to try," and "past volunteer experience." The collection unit may collect information from the user, for example, in the form of a questionnaire. The collection unit may also automatically collect a history of volunteer activities the user has participated in in the past. For example, the collection unit may collect information such as the types and duration of volunteer activities the user has participated in in the past, and the skills they have acquired. Step 2: The suggestion unit analyzes the information collected by the collection unit and suggests optimal volunteer activities and organizations. For example, the suggestion unit uses data mining technology to make optimal suggestions based on the user's interests and skills. The suggestion unit can also use machine learning algorithms to make suggestions based on the user's past activity history and feedback. For example, the suggestion unit can analyze feedback on volunteer activities the user has participated in in the past and suggest similar activities. Step 3: The learning unit collects and learns from user feedback. For example, the learning unit collects feedback provided by users on suggested volunteer activities. The learning unit can also update the machine learning algorithm based on user feedback to improve the accuracy of suggestions. For example, if a user gives a high rating to a suggested activity, the learning unit can increase the number of suggestions related to that activity.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; a suggestion unit that analyzes the information collected by the collection unit and suggests appropriate volunteer activities and organizations; A learning unit that collects and learns from user feedback. A system characterized by:

2. It will also have a data management department that manages data shared by various organizations and local governments. The system of claim 1 .

3. The system further includes a community formation section that promotes community formation based on the data managed by the data management section.

3. The system of claim 2.

4. The proposal unit Based on the collected information, we suggest the most suitable volunteer activities and organizations. The system of claim 1 .

5. The learning unit Collect and learn from user feedback The system of claim 1 .

6. The collecting unit Estimates user emotions and adjusts information collection timing based on the estimated user emotions. The system of claim 1 .

7. The collecting unit Analyze users' past volunteer experiences and select the most appropriate information collection method The system of claim 1 .

8. The collecting unit When collecting information, filter it based on the user's current life situation and areas of interest. The system of claim 1 .

9. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. The system of claim 1 .

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