system

Abstract

The system according to the embodiment aims to receive reports on volunteer activities, analyze them, and provide evaluation results. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives reports on volunteer activities. The analysis unit analyzes the content of the reports received by the reception unit. The provision unit provides the evaluation results analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately report and evaluate volunteer activities, and there is room for improvement.

[0005] The system according to the embodiment aims to receive reports on volunteer activities, analyze them, and provide evaluation results. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a report on a volunteer activity. The analysis unit analyzes the report received by the reception unit. The provision unit provides the evaluation results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can receive reports of volunteer activities, analyze them, and provide evaluation results. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A volunteer activity evaluation system according to an embodiment of the present invention accepts and analyzes reports on volunteer activities and provides evaluation results. The volunteer activity evaluation system rewards and evaluates volunteers by awarding points, honor, and status that can be converted into cash for their volunteer activities. This increases the number of volunteers participating and promotes social contribution. For example, a volunteer activity evaluation system may collaborate with organizations that perform volunteer activities and provide a points budget. The volunteer activity evaluation system then provides a mechanism for evaluating the results of volunteer activities and awarding rewards. This system uses AI to score the volunteers' achievements, which are difficult to evaluate. For example, in a volunteer activity evaluation system, an organization that performs volunteer activities registers the details of their activities in the system. Volunteers then perform the activities and report their achievements to the system. The volunteer activity evaluation system uses AI to evaluate the reported achievements and award points, honor, and status. This improves motivation for volunteer activities and revitalizes them. For example, when a volunteer performs a local cleanup activity, the volunteer reports the details of the activity to the system. The volunteer activity evaluation system uses AI to evaluate the reported details and award points. The points are provided in a form that can be converted into cash, allowing the volunteer to receive rewards. In addition, honor and status are conferred, improving the social standing of volunteer activities. This system will increase the number of volunteer participants and promote social contribution. For example, volunteer activities will be revitalized in a variety of fields, such as caring for the elderly and supporting children's education. This will provide solutions to social issues such as aging and widening disparities. The volunteer activity evaluation system will increase the number of volunteer participants and promote social contribution. For example, volunteer activities will be revitalized in a variety of fields, such as caring for the elderly and supporting children's education. This will provide solutions to social issues such as aging and widening disparities.

[0029] A volunteer activity evaluation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives reports on volunteer activities. Reports include, but are not limited to, text, images, and videos. The reception unit provides, for example, a form in which volunteers input details of their activities in text. The reception unit can also provide a function for uploading images and videos. For example, volunteers upload images and videos of their activities. The reception unit can also receive reports using voice input. For example, volunteers report details of their activities by voice. The analysis unit analyzes the report received by the reception unit. The analysis can be performed using, for example, data mining or statistical analysis, but is not limited to, these examples. For example, the analysis unit can analyze the text data of the report using natural language processing technology. The analysis unit can also analyze the content of images and videos using image recognition technology. For example, the image recognition technology can be used to analyze the activity and reflect the results in the evaluation. The analysis unit can also analyze voice data using voice analysis technology. For example, the activity content is analyzed using voice analysis technology and reflected in the evaluation. The providing unit provides the evaluation results analyzed by the analysis unit. The evaluation results include, for example, scores, ranks, feedback, etc., but are not limited to these examples. For example, the providing unit assigns a score to the volunteer based on the analysis results. The providing unit can also determine the volunteer's rank based on the analysis results. Furthermore, the providing unit can provide feedback to the volunteer based on the analysis results. For example, the providing unit provides evaluation comments on the volunteer's activity content. As a result, the volunteer activity evaluation system according to the embodiment can improve motivation for volunteer activities and revitalize activities by accepting and analyzing reports of volunteer activities and providing evaluation results.

[0030] The providing unit can grant points that can be exchanged for cash based on the evaluation results. The exchangeable points can include, for example, an amount per point and an exchange procedure, but are not limited to these examples. The providing unit, for example, grants points to volunteers based on the evaluation results. For example, if a volunteer receives a high evaluation, more points are awarded. The providing unit can also manage how the points can be exchanged for cash. For example, the providing unit can provide a procedure for exchanging the points for bank transfer or electronic money. Furthermore, the providing unit can also provide how the points can be used. For example, the providing unit can make the points available for shopping at affiliated stores. In this way, by granting exchangeable points based on the evaluation results, it is possible to provide a reward for volunteer activities.

[0031] The provision unit can confer honor or status based on the evaluation results. Examples of honor and status include, but are not limited to, titles, positions, and awards. For example, the provision unit can confer titles to volunteers based on the evaluation results. For example, if a volunteer receives a high evaluation, the provision unit can confer the title of "outstanding volunteer." The provision unit can also confer titles to volunteers based on the evaluation results. For example, if a volunteer demonstrates leadership, the provision unit can confer the title of "volunteer leader." Furthermore, the provision unit can commend volunteers based on the evaluation results. For example, the provision unit can evaluate the volunteer's activities and award them with a certificate of commendation. In this way, by conferring honor and status based on the evaluation results, it is possible to improve the social reputation of volunteer activities.

[0032] The analysis unit can analyze data on the duration of the activity, the extent of the influence, and the number of participants. The data to be analyzed includes, but is not limited to, the duration of the activity, the extent of the influence, and the number of participants. For example, the analysis unit analyzes data on the duration of the activity. For example, the analysis unit collects and analyzes data on the time spent by volunteers on the activity. The analysis unit can also analyze data on the extent of the influence. For example, the analysis unit collects and analyzes data on the impact of the volunteer activity on the entire region. Furthermore, the analysis unit can analyze data on the number of participants. For example, the analysis unit collects and analyzes data on the number of people who participated in the volunteer activity. This makes it possible to evaluate volunteer activities in detail by analyzing data such as the duration of the activity, the extent of the influence, and the number of participants.

[0033] The reception unit can register the details of volunteer activities in the system. Registration includes, for example, an input form and a method of saving in a database, but is not limited to these examples. The reception unit, for example, provides a form for volunteers to input the details of their activities. For example, the volunteer enters the details of their activities in text and registers them in the system. The reception unit can also provide a function for uploading images and videos. For example, the reception unit registers images and videos taken by volunteers of their activities in the system. Furthermore, the reception unit can also register the details of their activities using voice input. For example, the volunteer reports the details of their activities by voice and registers them in the system. In this way, registering the details of volunteer activities in the system makes it easier to record and manage the activities.

[0034] The providing unit can manage the method of converting points into cash. Examples of the conversion method include, but are not limited to, bank transfer, electronic money, etc. The providing unit, for example, provides a procedure for converting points into cash by bank transfer. For example, the points earned by the volunteer are transferred to a bank account. The providing unit can also provide a procedure for converting points into electronic money. For example, the points earned by the volunteer are converted into electronic money and made available for use. Furthermore, the providing unit can also provide a function for converting points into cash online. For example, the volunteer completes the conversion procedure on the system and converts the points into cash. In this way, by managing the method of converting points into cash, rewards for volunteer activities can be provided efficiently.

[0035] The provision unit can manage the awarding of honors and status. The awarding of honors and status includes, but is not limited to, award ceremonies and the issuance of certificates. For example, the provision unit holds an award ceremony for volunteers. For example, the volunteer's activities are evaluated and an award is presented at the award ceremony. The provision unit can also issue certificates. For example, a certificate of "Excellent Volunteer" is issued to a volunteer. Furthermore, the provision unit can manage data related to the awarding of honors and status. For example, the volunteer's activity details and evaluation results are stored in a database, and the award history is managed. In this way, by managing the awarding of honors and status, social recognition of volunteer activities can be efficiently provided.

[0036] The reception unit can analyze the volunteer's past activity history and select the optimal reporting method. Past activity history includes, but is not limited to, activity records and participation history. For example, if the volunteer has previously reported using text, the reception unit recommends text input. For example, if the volunteer has previously reported using voice, the reception unit recommends voice input. Furthermore, if the volunteer has previously reported using images, the reception unit can also recommend uploading images. For example, if the volunteer has previously reported by uploading images, the reception unit provides an image upload function. This allows the efficiency of reporting to be improved by selecting the optimal reporting method based on the volunteer's past activity history.

[0037] When receiving a report, the reception unit can filter the report based on the volunteer's current activity status and areas of interest. Examples of filtering include, but are not limited to, keyword matching and category classification. For example, if the volunteer is currently performing cleaning activities, the reception unit can only accept reports related to cleaning activities. For example, if the volunteer is interested in educational support, the reception unit can only accept reports related to educational support. Furthermore, if the volunteer is performing multiple activities, the reception unit can also filter the report based on the most relevant activity. For example, if the volunteer is performing multiple activities, the reception unit can filter the report based on the current activity status. In this way, by filtering the reports based on the volunteer's current activity status and areas of interest, it is possible to efficiently accept highly relevant reports.

[0038] When accepting a report, the acceptance unit can select the optimal acceptance means depending on the input method of the volunteer. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the volunteer selects voice input, the acceptance unit accepts the report using voice recognition. For example, if the volunteer selects text input, the acceptance unit provides a text field to accept the report. Furthermore, if the volunteer selects image input, the acceptance unit can also accept the report by providing an image upload function. For example, if the volunteer reports by uploading an image, the acceptance unit provides the image upload function. This allows the acceptance of reports to be carried out smoothly by selecting the optimal acceptance means depending on the input method of the volunteer.

[0039] When receiving reports, the reception unit can prioritize receiving highly relevant reports based on the geographical location information of the volunteer. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the volunteer is active in a specific area, the reception unit prioritizes receiving reports related to that area. For example, if the volunteer is traveling, the reception unit prioritizes receiving reports closest to the volunteer's current location. Furthermore, if the volunteer is participating in a specific event, the reception unit can also prioritize receiving reports related to that event. For example, if the volunteer is participating in a specific event, the reception unit prioritizes receiving reports related to that event. This enables efficient report reception by prioritizing highly relevant reports based on the geographical location information of the volunteer.

[0040] When receiving a report, the reception unit can analyze the social media activity of the volunteer and receive relevant reports. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The reception unit, for example, receives reports based on the activity content shared by the volunteer on social media. For example, the reception unit analyzes the content of the volunteer's posts on social media and prioritizes receiving relevant reports. The reception unit can also receive relevant reports by taking into account the activity of the volunteer's friends on social media. For example, the reception unit receives reports based on the activity content shared by the volunteer's friends on social media. In this way, by receiving relevant reports based on the volunteer's social media activity, the relevance of the reports can be increased.

[0041] When receiving a report, the reception unit can customize the reception method by reflecting the volunteer's past feedback. Feedback includes, for example, survey results and comments, but is not limited to these examples. For example, the reception unit can preferentially provide a reporting method that the volunteer has used favorably in the past. For example, the report reception procedure can be optimized based on the volunteer's past feedback. The reception unit can also adjust the reception method to avoid reporting methods that the volunteer has been dissatisfied with in the past. For example, the reception unit adjusts the reception method to avoid reporting methods that the volunteer has been dissatisfied with in the past. In this way, customizing the reception method based on the volunteer's past feedback allows for more appropriate report reception.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the activity. Criteria for evaluating the importance include, but are not limited to, the scope of impact and the number of participants. For example, the analysis unit performs a detailed analysis for activities with high importance. For example, the analysis unit performs a simplified analysis for activities with low importance. The analysis unit can also perform an analysis with a moderate level of detail for activities with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail for activities with medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the activity, it is possible to provide appropriate analysis results.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the activity. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit applies an algorithm that analyzes the amount and type of garbage to cleaning activities. For example, for educational support activities, it applies an algorithm that analyzes the learning outcomes of participants. The analysis unit can also apply an algorithm that analyzes the care time and the quality of the service provided to nursing care activities. For example, for nursing care activities, it applies an algorithm that analyzes the care time and the quality of the service provided. In this way, by applying different analysis algorithms depending on the category of the activity, the accuracy of the analysis results is improved.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the volunteer's past analysis results. Past analysis results include, for example, past evaluation data and analysis reports, but are not limited to these examples. The analysis unit, for example, corrects the current analysis result based on the volunteer's past analysis results. For example, the analysis unit uses the volunteer's past analysis results as learning data to improve the analysis algorithm. The analysis unit can also refer to the volunteer's past analysis results to improve the analysis accuracy for similar activities. For example, the analysis unit refers to the volunteer's past analysis results to improve the analysis accuracy for similar activities. In this way, the accuracy of the current analysis result is improved by referring to the volunteer's past analysis results.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the activity was performed. Evaluation criteria for the time when the activity was performed include, but are not limited to, for example, the start date and end date of the activity. For example, the analysis unit prioritizes the analysis of recently performed activities. For example, the analysis unit prioritizes the analysis of activities related to important events. The analysis unit can also prioritize the analysis of activities that are performed periodically. For example, the analysis unit prioritizes the analysis of activities that are performed periodically. In this way, by determining the priority of analysis based on the time when the activity was performed, the latest activities can be quickly evaluated.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of activities. Criteria for evaluating the relevance include, but are not limited to, common themes and related keywords. For example, the analysis unit analyzes activities in the same category together. For example, it prioritizes the analysis of highly relevant activities. The analysis unit can also postpone the analysis of less relevant activities. For example, it postpones the analysis of less relevant activities. In this way, by adjusting the order of analysis based on the relevance of activities, it is possible to prioritize the evaluation of highly relevant activities.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the volunteer's level of expertise. Criteria for evaluating the level of expertise include, but are not limited to, qualifications and years of experience. For example, the analysis unit provides analysis results that use a lot of technical terminology to a volunteer with high level of expertise. For example, it provides analysis results that are explained in simple language to a volunteer with low level of expertise. The analysis unit can also adjust the use of appropriate technical terminology according to the volunteer's level of expertise. For example, it adjusts the use of appropriate technical terminology according to the volunteer's level of expertise. This makes it easier to understand the analysis results by adjusting the use of technical terminology in the analysis according to the volunteer's level of expertise.

[0048] When providing the reward, the providing unit can select the optimal reward method by analyzing the volunteer's past activities. Criteria for selecting the optimal reward method include, but are not limited to, the content of the volunteer's past activities and the effectiveness of the reward. For example, the providing unit preferentially provides rewards that the volunteer has preferred in the past. For example, the providing unit selects the optimal reward method based on the content of the volunteer's past activities. The providing unit can also adjust the reward method by referring to the volunteer's past feedback. For example, the providing unit adjusts the reward method by referring to the volunteer's past feedback. In this way, the effectiveness of the reward can be maximized by selecting the optimal reward method based on the volunteer's past activities.

[0049] The providing unit can customize the reward means based on the volunteer's current living situation when providing the reward. Methods of collecting living situation include, but are not limited to, income and family structure. For example, if the volunteer is a student, the providing unit can provide a reward that is useful for their studies. For example, if the volunteer is elderly, the providing unit can provide a health-conscious reward. Furthermore, if the volunteer is working, the providing unit can also provide a reward that is useful for work. For example, if the volunteer is working, the providing unit can provide a reward that is useful for work. In this way, customizing the reward means based on the volunteer's current living situation allows the volunteer to receive the reward more appropriately.

[0050] The providing unit can improve the reward method by reflecting the volunteer's feedback when providing the reward. Criteria for improving the reward method include, but are not limited to, monetary rewards and material rewards. The providing unit can, for example, increase the number of types of rewards based on the volunteer's feedback. For example, improve the method for receiving the reward based on the volunteer's feedback. The providing unit can also adjust the timing of providing the reward based on the volunteer's feedback. For example, adjust the timing of providing the reward based on the volunteer's feedback. In this way, by improving the reward method based on the volunteer's feedback, the reward can be received more appropriately.

[0051] The providing unit can select the optimal reward method by taking into consideration the geographical location information of the volunteer when providing the reward. Methods for collecting geographical location information include, but are not limited to, GPS data and location information services. For example, if the volunteer is active in a specific area, the providing unit can provide a reward related to that area. For example, if the volunteer is traveling, the providing unit can provide a reward closest to the current location. Furthermore, if the volunteer is participating in a specific event, the providing unit can provide a reward related to that event. For example, if the volunteer is participating in a specific event, the providing unit can provide a reward related to that event. This allows the effectiveness of the reward to be maximized by selecting the optimal reward method based on the geographical location information of the volunteer.

[0052] When providing the rewards, the providing unit can analyze the social media activity of the volunteer and suggest reward methods. Methods for analyzing social media activity include, but are not limited to, the content of posts and the number of followers. The providing unit can, for example, suggest rewards based on the activity content shared by the volunteer on social media. For example, the content of the volunteer's social media posts can be analyzed to suggest related rewards. The providing unit can also suggest related rewards based on the activity of the volunteer's friends on social media. For example, the rewards can be suggested based on the activity content shared by the volunteer's friends on social media. In this way, by suggesting reward methods based on the volunteer's social media activity, the relevance of the rewards can be increased.

[0053] When providing rewards, the providing unit can customize the reward method by reflecting the volunteer's past feedback. Customization criteria for the reward method include, but are not limited to, monetary rewards and material rewards. For example, the providing unit can increase the types of rewards based on the volunteer's past feedback. For example, the reward receiving method can be improved based on the volunteer's past feedback. The providing unit can also adjust the timing of reward provision based on the volunteer's past feedback. For example, the timing of reward provision can be adjusted based on the volunteer's past feedback. In this way, customizing the reward method based on the volunteer's past feedback allows for more appropriate reward receipt.

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

[0055] The analysis unit can evaluate the environmental impact of volunteer activities based on the reported content of the activities. For example, it can analyze the reported content of cleanup activities to evaluate the amount and type of garbage collected. It can also analyze the reported content of tree planting activities to evaluate the number and type of trees planted. It can also analyze the reported content of energy saving activities to evaluate the amount of energy saved. This makes it possible to specifically evaluate the contribution of volunteer activities to the environment.

[0056] The provision department can provide educational opportunities to volunteers based on the evaluation results. For example, highly rated volunteers can be offered the opportunity to participate in specialized training or workshops. It can also provide online courses to improve skills for volunteers with specific skills. It can also provide support for obtaining qualifications related to volunteer activities. This can support the skill development and career development of volunteers.

[0057] The provision department can provide health management services to volunteers based on the evaluation results. For example, free health checkups and fitness programs can be provided to volunteers who receive high evaluations. Counseling services for stress management can also be provided. Furthermore, nutritional guidance and meal plans for maintaining health can be provided. This can support the health of volunteers and increase the sustainability of their activities.

[0058] The analysis unit can evaluate the social impact of volunteer activities based on the reported content of the activities. For example, it can analyze the reported content of educational support activities to evaluate the learning outcomes of the children who received support. It can also analyze the reported content of local events to evaluate the satisfaction of participants and the degree of revitalization of the local area. It can also analyze the reported content of nursing care activities to evaluate the degree of improvement in the quality of life of elderly people who received care. This makes it possible to specifically evaluate the social contribution of volunteer activities.

[0059] The provision unit can provide travel and leisure opportunities to volunteers based on the evaluation results. For example, free travel vouchers or leisure tickets can be provided to volunteers who receive high evaluations. In addition, invitations to local tourist facilities and events can be provided for specific activities. Furthermore, parties and social events can be held to celebrate the results of volunteer activities. This can increase volunteer motivation and make the activity more enjoyable.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit accepts reports of volunteer activities. Reports can include text, images, videos, etc. The reception unit provides a form for volunteers to enter details of their activities in text, and also provides a function for uploading images and videos. In addition, reports can be accepted using voice input. Step 2: The analysis unit analyzes the report content received by the reception unit. The analysis is performed using data mining, statistical analysis, natural language processing technology, image recognition technology, voice analysis technology, etc. For example, text data is analyzed using natural language processing technology, image and video content is analyzed using image recognition technology, and voice data is analyzed using voice analysis technology. Step 3: The providing unit provides the evaluation results analyzed by the analyzing unit. The evaluation results include scores, ranks, feedback, etc. For example, the providing unit assigns scores to volunteers based on the analysis results, determines their ranks, and provides feedback.

[0062] (Example 2) A volunteer activity evaluation system according to an embodiment of the present invention accepts and analyzes reports on volunteer activities and provides evaluation results. The volunteer activity evaluation system rewards and evaluates volunteers by awarding points, honor, and status that can be converted into cash for their volunteer activities. This increases the number of volunteers participating and promotes social contribution. For example, a volunteer activity evaluation system may collaborate with organizations that perform volunteer activities and provide a points budget. The volunteer activity evaluation system then provides a mechanism for evaluating the results of volunteer activities and awarding rewards. This system uses AI to score the volunteers' achievements, which are difficult to evaluate. For example, in a volunteer activity evaluation system, an organization that performs volunteer activities registers the details of their activities in the system. Volunteers then perform the activities and report their achievements to the system. The volunteer activity evaluation system uses AI to evaluate the reported achievements and award points, honor, and status. This improves motivation for volunteer activities and revitalizes them. For example, when a volunteer performs a local cleanup activity, the volunteer reports the details of the activity to the system. The volunteer activity evaluation system uses AI to evaluate the reported details and award points. The points are provided in a form that can be converted into cash, allowing the volunteer to receive rewards. In addition, honor and status are conferred, improving the social standing of volunteer activities. This system will increase the number of volunteer participants and promote social contribution. For example, volunteer activities will be revitalized in a variety of fields, such as caring for the elderly and supporting children's education. This will provide solutions to social issues such as aging and widening disparities. The volunteer activity evaluation system will increase the number of volunteer participants and promote social contribution. For example, volunteer activities will be revitalized in a variety of fields, such as caring for the elderly and supporting children's education. This will provide solutions to social issues such as aging and widening disparities.

[0063] A volunteer activity evaluation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives reports on volunteer activities. Reports include, but are not limited to, text, images, and videos. The reception unit provides, for example, a form in which volunteers input details of their activities in text. The reception unit can also provide a function for uploading images and videos. For example, volunteers upload images and videos of their activities. The reception unit can also receive reports using voice input. For example, volunteers report details of their activities by voice. The analysis unit analyzes the report received by the reception unit. The analysis can be performed using, for example, data mining or statistical analysis, but is not limited to, these examples. For example, the analysis unit can analyze the text data of the report using natural language processing technology. The analysis unit can also analyze the content of images and videos using image recognition technology. For example, the image recognition technology can be used to analyze the activity and reflect the results in the evaluation. The analysis unit can also analyze voice data using voice analysis technology. For example, the activity content is analyzed using voice analysis technology and reflected in the evaluation. The providing unit provides the evaluation results analyzed by the analysis unit. The evaluation results include, for example, scores, ranks, feedback, etc., but are not limited to these examples. For example, the providing unit assigns a score to the volunteer based on the analysis results. The providing unit can also determine the volunteer's rank based on the analysis results. Furthermore, the providing unit can provide feedback to the volunteer based on the analysis results. For example, the providing unit provides evaluation comments on the volunteer's activity content. As a result, the volunteer activity evaluation system according to the embodiment can improve motivation for volunteer activities and revitalize activities by accepting and analyzing reports of volunteer activities and providing evaluation results.

[0064] The providing unit can grant points that can be exchanged for cash based on the evaluation results. The exchangeable points can include, for example, an amount per point and an exchange procedure, but are not limited to these examples. The providing unit, for example, grants points to volunteers based on the evaluation results. For example, if a volunteer receives a high evaluation, more points are awarded. The providing unit can also manage how the points can be exchanged for cash. For example, the providing unit can provide a procedure for exchanging the points for bank transfer or electronic money. Furthermore, the providing unit can also provide how the points can be used. For example, the providing unit can make the points available for shopping at affiliated stores. In this way, by granting exchangeable points based on the evaluation results, it is possible to provide a reward for volunteer activities.

[0065] The provision unit can confer honor or status based on the evaluation results. Examples of honor and status include, but are not limited to, titles, positions, and awards. For example, the provision unit can confer titles to volunteers based on the evaluation results. For example, if a volunteer receives a high evaluation, the provision unit can confer the title of "outstanding volunteer." The provision unit can also confer titles to volunteers based on the evaluation results. For example, if a volunteer demonstrates leadership, the provision unit can confer the title of "volunteer leader." Furthermore, the provision unit can commend volunteers based on the evaluation results. For example, the provision unit can evaluate the volunteer's activities and award them with a certificate of commendation. In this way, by conferring honor and status based on the evaluation results, it is possible to improve the social reputation of volunteer activities.

[0066] The analysis unit can analyze data on the duration of the activity, the extent of the influence, and the number of participants. The data to be analyzed includes, but is not limited to, the duration of the activity, the extent of the influence, and the number of participants. For example, the analysis unit analyzes data on the duration of the activity. For example, the analysis unit collects and analyzes data on the time spent by volunteers on the activity. The analysis unit can also analyze data on the extent of the influence. For example, the analysis unit collects and analyzes data on the impact of the volunteer activity on the entire region. Furthermore, the analysis unit can analyze data on the number of participants. For example, the analysis unit collects and analyzes data on the number of people who participated in the volunteer activity. This makes it possible to evaluate volunteer activities in detail by analyzing data such as the duration of the activity, the extent of the influence, and the number of participants.

[0067] The reception unit can register the details of volunteer activities in the system. Registration includes, for example, an input form and a method of saving in a database, but is not limited to these examples. The reception unit, for example, provides a form for volunteers to input the details of their activities. For example, the volunteer enters the details of their activities in text and registers them in the system. The reception unit can also provide a function for uploading images and videos. For example, the reception unit registers images and videos taken by volunteers of their activities in the system. Furthermore, the reception unit can also register the details of their activities using voice input. For example, the volunteer reports the details of their activities by voice and registers them in the system. In this way, registering the details of volunteer activities in the system makes it easier to record and manage the activities.

[0068] The providing unit can manage the method of converting points into cash. Examples of the conversion method include, but are not limited to, bank transfer, electronic money, etc. The providing unit, for example, provides a procedure for converting points into cash by bank transfer. For example, the points earned by the volunteer are transferred to a bank account. The providing unit can also provide a procedure for converting points into electronic money. For example, the points earned by the volunteer are converted into electronic money and made available for use. Furthermore, the providing unit can also provide a function for converting points into cash online. For example, the volunteer completes the conversion procedure on the system and converts the points into cash. In this way, by managing the method of converting points into cash, rewards for volunteer activities can be provided efficiently.

[0069] The provision unit can manage the awarding of honors and status. The awarding of honors and status includes, but is not limited to, award ceremonies and the issuance of certificates. For example, the provision unit holds an award ceremony for volunteers. For example, the volunteer's activities are evaluated and an award is presented at the award ceremony. The provision unit can also issue certificates. For example, a certificate of "Excellent Volunteer" is issued to a volunteer. Furthermore, the provision unit can manage data related to the awarding of honors and status. For example, the volunteer's activity details and evaluation results are stored in a database, and the award history is managed. In this way, by managing the awarding of honors and status, social recognition of volunteer activities can be efficiently provided.

[0070] The reception unit can estimate the emotion of the volunteer and adjust the timing of receiving the report based on the estimated emotion of the volunteer. Methods for estimating emotion include, but are not limited to, facial expression recognition and text analysis. For example, if the volunteer is tired, the reception unit postpones receiving the report until the next day. For example, the reception unit can estimate the volunteer's level of fatigue using facial expression recognition technology and adjust the timing of receiving the report. Furthermore, if the volunteer is excited, the reception unit can immediately receive the report. For example, the reception unit can estimate the volunteer's level of excitement using text analysis technology and adjust the timing of receiving the report. Furthermore, if the volunteer is relaxed, the reception unit can continue receiving the report as usual. For example, the reception unit can estimate the volunteer's level of relaxation using voice analysis technology and adjust the timing of receiving the report. This can reduce the burden on the volunteer by adjusting the timing of receiving the report according to the volunteer's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The reception unit can analyze the volunteer's past activity history and select the optimal reporting method. Past activity history includes, but is not limited to, activity records and participation history. For example, if the volunteer has previously reported using text, the reception unit recommends text input. For example, if the volunteer has previously reported using voice, the reception unit recommends voice input. Furthermore, if the volunteer has previously reported using images, the reception unit can also recommend uploading images. For example, if the volunteer has previously reported by uploading images, the reception unit provides an image upload function. This allows the efficiency of reporting to be improved by selecting the optimal reporting method based on the volunteer's past activity history.

[0072] When receiving a report, the reception unit can filter the report based on the volunteer's current activity status and areas of interest. Examples of filtering include, but are not limited to, keyword matching and category classification. For example, if the volunteer is currently performing cleaning activities, the reception unit can only accept reports related to cleaning activities. For example, if the volunteer is interested in educational support, the reception unit can only accept reports related to educational support. Furthermore, if the volunteer is performing multiple activities, the reception unit can also filter the report based on the most relevant activity. For example, if the volunteer is performing multiple activities, the reception unit can filter the report based on the current activity status. In this way, by filtering the reports based on the volunteer's current activity status and areas of interest, it is possible to efficiently accept highly relevant reports.

[0073] When accepting a report, the acceptance unit can select the optimal acceptance means depending on the input method of the volunteer. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the volunteer selects voice input, the acceptance unit accepts the report using voice recognition. For example, if the volunteer selects text input, the acceptance unit provides a text field to accept the report. Furthermore, if the volunteer selects image input, the acceptance unit can also accept the report by providing an image upload function. For example, if the volunteer reports by uploading an image, the acceptance unit provides the image upload function. This allows the acceptance of reports to be carried out smoothly by selecting the optimal acceptance means depending on the input method of the volunteer.

[0074] The reception unit can estimate the emotions of the volunteers and determine the priority of reports to be received based on the estimated emotions of the volunteers. The prioritization can be based on, but is not limited to, criteria such as importance and urgency. For example, if the volunteer is stressed, the reception unit postpones reports of low importance. For example, if the volunteer is relaxed, the reception unit prioritizes reports of high importance. Furthermore, if the volunteer is excited, the reception unit can immediately accept reports of high urgency. For example, if the volunteer is excited, the reception unit prioritizes reports of high urgency. Thus, by determining the priority of reports according to the emotions of the volunteers, important reports can be prioritized. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When receiving reports, the reception unit can prioritize receiving highly relevant reports based on the geographical location information of the volunteer. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the volunteer is active in a specific area, the reception unit prioritizes receiving reports related to that area. For example, if the volunteer is traveling, the reception unit prioritizes receiving reports closest to the volunteer's current location. Furthermore, if the volunteer is participating in a specific event, the reception unit can also prioritize receiving reports related to that event. For example, if the volunteer is participating in a specific event, the reception unit prioritizes receiving reports related to that event. This enables efficient report reception by prioritizing highly relevant reports based on the geographical location information of the volunteer.

[0076] When receiving a report, the reception unit can analyze the social media activity of the volunteer and receive relevant reports. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The reception unit, for example, receives reports based on the activity content shared by the volunteer on social media. For example, the reception unit analyzes the content of the volunteer's posts on social media and prioritizes receiving relevant reports. The reception unit can also receive relevant reports by taking into account the activity of the volunteer's friends on social media. For example, the reception unit receives reports based on the activity content shared by the volunteer's friends on social media. In this way, by receiving relevant reports based on the volunteer's social media activity, the relevance of the reports can be increased.

[0077] When receiving a report, the reception unit can customize the reception method by reflecting the volunteer's past feedback. Feedback includes, for example, survey results and comments, but is not limited to these examples. For example, the reception unit can preferentially provide a reporting method that the volunteer has used favorably in the past. For example, the report reception procedure can be optimized based on the volunteer's past feedback. The reception unit can also adjust the reception method to avoid reporting methods that the volunteer has been dissatisfied with in the past. For example, the reception unit adjusts the reception method to avoid reporting methods that the volunteer has been dissatisfied with in the past. In this way, customizing the reception method based on the volunteer's past feedback allows for more appropriate report reception.

[0078] The analysis unit can estimate the volunteer's emotion and adjust the presentation method of the analysis based on the estimated emotion of the volunteer. Presentation methods include, but are not limited to, graph display and text display. For example, if the volunteer is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the volunteer is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the volunteer is excited, the analysis unit can also provide a visually stimulating analysis result. For example, if the volunteer is excited, the analysis unit provides a visually stimulating analysis result. This makes it easier to understand the analysis result by adjusting the presentation method of the analysis according to the volunteer's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the activity. Criteria for evaluating the importance include, but are not limited to, the scope of impact and the number of participants. For example, the analysis unit performs a detailed analysis for activities with high importance. For example, the analysis unit performs a simplified analysis for activities with low importance. The analysis unit can also perform an analysis with a moderate level of detail for activities with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail for activities with medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the activity, it is possible to provide appropriate analysis results.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the activity. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit applies an algorithm that analyzes the amount and type of garbage to cleaning activities. For example, for educational support activities, it applies an algorithm that analyzes the learning outcomes of participants. The analysis unit can also apply an algorithm that analyzes the care time and the quality of the service provided to nursing care activities. For example, for nursing care activities, it applies an algorithm that analyzes the care time and the quality of the service provided. In this way, by applying different analysis algorithms depending on the category of the activity, the accuracy of the analysis results is improved.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the volunteer's past analysis results. Past analysis results include, for example, past evaluation data and analysis reports, but are not limited to these examples. The analysis unit, for example, corrects the current analysis result based on the volunteer's past analysis results. For example, the analysis unit uses the volunteer's past analysis results as learning data to improve the analysis algorithm. The analysis unit can also refer to the volunteer's past analysis results to improve the analysis accuracy for similar activities. For example, the analysis unit refers to the volunteer's past analysis results to improve the analysis accuracy for similar activities. In this way, the accuracy of the current analysis result is improved by referring to the volunteer's past analysis results.

[0082] The analysis unit can estimate the volunteer's emotion and adjust the length of the analysis based on the estimated emotion of the volunteer. Examples of the length of the analysis include, but are not limited to, the analysis time and data volume. For example, if the volunteer is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the volunteer is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the volunteer is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the volunteer's emotion, the analysis result becomes easier to understand. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the time when the activity was performed. Evaluation criteria for the time when the activity was performed include, but are not limited to, for example, the start date and end date of the activity. For example, the analysis unit prioritizes the analysis of recently performed activities. For example, the analysis unit prioritizes the analysis of activities related to important events. The analysis unit can also prioritize the analysis of activities that are performed periodically. For example, the analysis unit prioritizes the analysis of activities that are performed periodically. In this way, by determining the priority of analysis based on the time when the activity was performed, the latest activities can be quickly evaluated.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of activities. Criteria for evaluating the relevance include, but are not limited to, common themes and related keywords. For example, the analysis unit analyzes activities in the same category together. For example, it prioritizes the analysis of highly relevant activities. The analysis unit can also postpone the analysis of less relevant activities. For example, it postpones the analysis of less relevant activities. In this way, by adjusting the order of analysis based on the relevance of activities, it is possible to prioritize the evaluation of highly relevant activities.

[0085] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the volunteer's level of expertise. Criteria for evaluating the level of expertise include, but are not limited to, qualifications and years of experience. For example, the analysis unit provides analysis results that use a lot of technical terminology to a volunteer with high level of expertise. For example, it provides analysis results that are explained in simple language to a volunteer with low level of expertise. The analysis unit can also adjust the use of appropriate technical terminology according to the volunteer's level of expertise. For example, it adjusts the use of appropriate technical terminology according to the volunteer's level of expertise. This makes it easier to understand the analysis results by adjusting the use of technical terminology in the analysis according to the volunteer's level of expertise.

[0086] The providing unit can estimate the emotion of the volunteer and adjust the reward method to be provided based on the estimated emotion of the volunteer. Examples of reward methods include, but are not limited to, monetary rewards and material rewards. For example, if the volunteer is tired, the providing unit can provide a reward that helps the volunteer relax. For example, if the volunteer is excited, the providing unit can provide an immediate reward. Furthermore, if the volunteer is relaxed, the providing unit can provide a normal reward. For example, if the volunteer is relaxed, the normal reward is provided. This allows the reward method to be adjusted according to the emotion of the volunteer, thereby improving the volunteer's satisfaction. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When providing the reward, the providing unit can select the optimal reward method by analyzing the volunteer's past activities. Criteria for selecting the optimal reward method include, but are not limited to, the content of the volunteer's past activities and the effectiveness of the reward. For example, the providing unit preferentially provides rewards that the volunteer has preferred in the past. For example, the providing unit selects the optimal reward method based on the content of the volunteer's past activities. The providing unit can also adjust the reward method by referring to the volunteer's past feedback. For example, the providing unit adjusts the reward method by referring to the volunteer's past feedback. In this way, the effectiveness of the reward can be maximized by selecting the optimal reward method based on the volunteer's past activities.

[0088] The providing unit can customize the reward means based on the volunteer's current living situation when providing the reward. Methods of collecting living situation include, but are not limited to, income and family structure. For example, if the volunteer is a student, the providing unit can provide a reward that is useful for their studies. For example, if the volunteer is elderly, the providing unit can provide a health-conscious reward. Furthermore, if the volunteer is working, the providing unit can also provide a reward that is useful for work. For example, if the volunteer is working, the providing unit can provide a reward that is useful for work. In this way, customizing the reward means based on the volunteer's current living situation allows the volunteer to receive the reward more appropriately.

[0089] The providing unit can improve the reward method by reflecting the volunteer's feedback when providing the reward. Criteria for improving the reward method include, but are not limited to, monetary rewards and material rewards. The providing unit can, for example, increase the number of types of rewards based on the volunteer's feedback. For example, improve the method for receiving the reward based on the volunteer's feedback. The providing unit can also adjust the timing of providing the reward based on the volunteer's feedback. For example, adjust the timing of providing the reward based on the volunteer's feedback. In this way, by improving the reward method based on the volunteer's feedback, the reward can be received more appropriately.

[0090] The providing unit can estimate the emotions of the volunteers and determine the priority of rewards to be provided based on the estimated emotions of the volunteers. Criteria for determining the priority include, but are not limited to, importance and urgency. For example, if the volunteer is feeling stressed, the providing unit can provide a reward that helps the volunteer to relax. For example, if the volunteer is relaxed, the providing unit can provide a normal reward. The providing unit can also provide a reward immediately if the volunteer is excited. For example, if the volunteer is excited, the reward can be provided immediately. In this way, by determining the priority of rewards according to the emotions of the volunteers, important rewards can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The providing unit can select the optimal reward method by taking into consideration the geographical location information of the volunteer when providing the reward. Methods for collecting geographical location information include, but are not limited to, GPS data and location information services. For example, if the volunteer is active in a specific area, the providing unit can provide a reward related to that area. For example, if the volunteer is traveling, the providing unit can provide a reward closest to the current location. Furthermore, if the volunteer is participating in a specific event, the providing unit can provide a reward related to that event. For example, if the volunteer is participating in a specific event, the providing unit can provide a reward related to that event. This allows the effectiveness of the reward to be maximized by selecting the optimal reward method based on the geographical location information of the volunteer.

[0092] When providing the rewards, the providing unit can analyze the social media activity of the volunteer and suggest reward methods. Methods for analyzing social media activity include, but are not limited to, the content of posts and the number of followers. The providing unit can, for example, suggest rewards based on the activity content shared by the volunteer on social media. For example, the content of the volunteer's social media posts can be analyzed to suggest related rewards. The providing unit can also suggest related rewards based on the activity of the volunteer's friends on social media. For example, the rewards can be suggested based on the activity content shared by the volunteer's friends on social media. In this way, by suggesting reward methods based on the volunteer's social media activity, the relevance of the rewards can be increased.

[0093] When providing rewards, the providing unit can customize the reward method by reflecting the volunteer's past feedback. Customization criteria for the reward method include, but are not limited to, monetary rewards and material rewards. For example, the providing unit can increase the types of rewards based on the volunteer's past feedback. For example, the reward receiving method can be improved based on the volunteer's past feedback. The providing unit can also adjust the timing of reward provision based on the volunteer's past feedback. For example, the timing of reward provision can be adjusted based on the volunteer's past feedback. In this way, customizing the reward method based on the volunteer's past feedback allows for more appropriate reward receipt. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides a form for volunteers to enter activity details in text and a function for uploading images and videos. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the report content using natural language processing technology and image recognition technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides scores, ranks, and feedback to volunteers based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides a form for volunteers to enter text about their activities and a function for uploading images and videos. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the report content using natural language processing technology and image recognition technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides scores, ranks, and feedback to volunteers based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides a form for volunteers to enter details of their activities in text and a function for uploading images and videos. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the report content using natural language processing technology and image recognition technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides scores, ranks, and feedback to volunteers based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides a form for the volunteer to enter the details of their activities in text and a function for uploading images and videos. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the report content using natural language processing technology and image recognition technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the volunteer with a score, rank, and feedback based on the analysis results.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The analysis unit can evaluate the environmental impact of volunteer activities based on the reported content of the activities. For example, it can analyze the reported content of cleanup activities to evaluate the amount and type of garbage collected. It can also analyze the reported content of tree planting activities to evaluate the number and type of trees planted. It can also analyze the reported content of energy saving activities to evaluate the amount of energy saved. This makes it possible to specifically evaluate the contribution of volunteer activities to the environment.

[0096] The provision department can provide educational opportunities to volunteers based on the evaluation results. For example, highly rated volunteers can be offered the opportunity to participate in specialized training or workshops. It can also provide online courses to improve skills for volunteers with specific skills. It can also provide support for obtaining qualifications related to volunteer activities. This can support the skill development and career development of volunteers.

[0097] The provision department can provide health management services to volunteers based on the evaluation results. For example, free health checkups and fitness programs can be provided to volunteers who receive high evaluations. Counseling services for stress management can also be provided. Furthermore, nutritional guidance and meal plans for maintaining health can be provided. This can support the health of volunteers and increase the sustainability of their activities.

[0098] The analysis unit can evaluate the social impact of volunteer activities based on the reported content of the activities. For example, it can analyze the reported content of educational support activities to evaluate the learning outcomes of the children who received support. It can also analyze the reported content of local events to evaluate the satisfaction of participants and the degree of revitalization of the local area. It can also analyze the reported content of nursing care activities to evaluate the degree of improvement in the quality of life of elderly people who received care. This makes it possible to specifically evaluate the social contribution of volunteer activities.

[0099] The provision unit can provide travel and leisure opportunities to volunteers based on the evaluation results. For example, free travel vouchers or leisure tickets can be provided to volunteers who receive high evaluations. In addition, invitations to local tourist facilities and events can be provided for specific activities. Furthermore, parties and social events can be held to celebrate the results of volunteer activities. This can increase volunteer motivation and make the activity more enjoyable.

[0100] The analysis unit can estimate the emotions of the volunteer and adjust the feedback method based on the estimated emotions of the volunteer. For example, if the volunteer is tired, concise and positive feedback can be provided. For example, if the volunteer is excited, detailed and specific feedback can be provided. Also, if the volunteer is relaxed, normal feedback can be provided. In this way, by adjusting the feedback method according to the emotions of the volunteer, it is possible to improve the ease of feedback acceptance.

[0101] The providing unit can estimate the emotion of the volunteer and select the type of reward based on the estimated emotion of the volunteer. For example, if the volunteer is tired, a reward that allows them to relax can be provided. For example, if the volunteer is excited, a reward that allows them to be active can be provided. Also, if the volunteer is relaxed, a normal reward can be provided. In this way, by selecting the type of reward according to the emotion of the volunteer, the effect of the reward can be maximized.

[0102] The reception unit can estimate the emotion of the volunteer and adjust the method of receiving the report based on the estimated emotion of the volunteer. For example, if the volunteer is feeling stressed, a simple method of reporting can be provided. For example, if the volunteer is relaxed, a detailed method of reporting can be provided. Also, if the volunteer is excited, the report can be immediately received. In this way, the burden of reporting can be reduced by adjusting the method of receiving the report according to the emotion of the volunteer.

[0103] The reward providing unit can estimate the emotion of the volunteer and adjust the timing of providing the reward based on the estimated emotion of the volunteer. For example, if the volunteer is tired, the reward can be provided at a later date. For example, if the volunteer is excited, the reward can be provided immediately. Also, if the volunteer is relaxed, the reward can be provided at the usual timing. In this way, by adjusting the timing of providing the reward according to the emotion of the volunteer, the reward can be received more appropriately.

[0104] The analysis unit can estimate the emotion of the volunteer and adjust the level of detail of the analysis based on the estimated emotion of the volunteer. For example, if the volunteer is nervous, it can provide a concise and to-the-point analysis result. For example, if the volunteer is relaxed, it can provide a detailed analysis result. Also, if the volunteer is excited, it can provide a visually stimulating analysis result. In this way, adjusting the level of detail of the analysis according to the emotion of the volunteer makes it easier to understand the analysis result.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The reception unit accepts reports of volunteer activities. Reports can include text, images, videos, etc. The reception unit provides a form for volunteers to enter details of their activities in text, and also provides a function for uploading images and videos. In addition, reports can be accepted using voice input. Step 2: The analysis unit analyzes the report content received by the reception unit. The analysis is performed using data mining, statistical analysis, natural language processing technology, image recognition technology, voice analysis technology, etc. For example, text data is analyzed using natural language processing technology, image and video content is analyzed using image recognition technology, and voice data is analyzed using voice analysis technology. Step 3: The providing unit provides the evaluation results analyzed by the analyzing unit. The evaluation results include scores, ranks, feedback, etc. For example, the providing unit assigns scores to volunteers based on the analysis results, determines their ranks, and provides feedback.

[0107] 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.

[0108] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 reception desk that accepts reports on volunteer activities, an analysis unit that analyzes the report content received by the reception unit; a providing unit that provides the evaluation results analyzed by the analyzing unit; Equipped with A system characterized by:

2. The providing unit Based on the evaluation results, points that can be redeemed for cash are awarded.

2. The system of claim 1.

3. The providing unit Awarding honor or status based on evaluation results 2. The system of claim 1.

4. The analysis unit Analyze data on activity duration, impact area, and number of participants 2. The system of claim 1.

5. The reception unit Register the details of your volunteer activities in the system 2. The system of claim 1.

6. The providing unit Manage how you redeem your points 2. The system of claim 1.

7. The providing unit Administering the awarding of honors and ranks 2. The system of claim 1.

8. The reception unit Estimate the emotions of volunteers and adjust the timing of receiving reports based on the estimated emotions of volunteers.

2. The system of claim 1.

9. The reception unit Analyze past volunteer activity history and select appropriate reporting methods 2. The system of claim 1.

Images