System

Abstract

An object of a system according to an embodiment is to appropriately perform reward and evaluation for volunteer activities.SOLUTION: A system according to an embodiment includes a reward giving unit, an evaluation unit, and a system providing unit. The reward granting unit grants points, honors, or positions that can be converted into cash for volunteer activities. The evaluation unit scores the results of the volunteer activities using the AI. The system providing unit intervenes between the volunteer group and the local government and gives a reward in return for the social contribution.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies offer few methods for rewarding or evaluating volunteer work, which can lead to a decline in volunteer motivation.

[0005] The system according to the embodiment aims to appropriately reward and evaluate volunteer activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reward granting unit, an evaluation unit, and a system providing unit. The reward granting unit grants points, honor, and status that can be converted into cash for volunteer activities. The evaluation unit uses AI to score the results of volunteer activities. The system providing unit acts as an intermediary between volunteer groups and local governments, and grants rewards in return for social contributions. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately reward and evaluate volunteer activities. [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) The volunteer activity support system according to an embodiment of the present invention is a system that awards points, honor, and status that can be converted into cash for volunteer activities, evaluates them using AI, and provides rewards. This system can improve the motivation of volunteers and stimulate their activities.

[0029] A volunteer activity support system according to an embodiment includes a reward granting unit, an evaluation unit, and a system providing unit. The reward granting unit grants points, honor, and status that can be exchanged for volunteer activities. For example, if a volunteer performs a certain activity, points corresponding to the activity are awarded. These points can later be exchanged for cash, gift certificates, or the like. Furthermore, by awarding honor and status for specific activities, volunteers' motivation is increased. The evaluation unit uses AI to score the results of volunteer activities. For example, the AI ​​analyzes the time, content, and deliverables of the volunteer's activities and evaluates them based on the results. The AI ​​analyzes the activity content and calculates an evaluation score using text generation AI (e.g., LLM) or multimodal generation AI. The system providing unit acts as an intermediary between volunteer organizations and local governments and awards rewards in return for social contributions. For example, the local government provides a points budget, and the system provider awards these points to volunteers. These points are awarded based on the evaluation of the volunteer activity. This allows the volunteer activity support system to increase volunteer motivation and stimulate volunteer activity.

[0030] The reward granting unit uses AI to generate individually customized reward plans based on the content of the volunteer activity, allowing the AI ​​to provide rewards tailored to the needs of each volunteer. For example, the reward granting unit analyzes the content of the volunteer activity in detail, and the AI ​​generates individually customized reward plans. For example, volunteers who perform educational support activities are rewarded with educational books and teaching materials. The reward granting unit also considers the volunteer's past activity history and skill set and uses AI to propose optimal reward plans. For example, medical volunteers are rewarded with medical equipment and specialized books. The reward granting unit also collects the individual needs and wishes of volunteers in the form of a questionnaire, and the AI ​​generates customized reward plans based on that data. For example, volunteers who perform environmental protection activities are rewarded with eco-friendly products and recycled goods. This makes it possible to provide rewards tailored to the individual needs of volunteers.

[0031] The reward granting unit uses AI to analyze volunteer activity history and cumulatively increase rewards based on long-term contributions. For example, the reward granting unit uses AI to analyze volunteer activity history data and cumulatively increase rewards based on long-term contributions. For example, volunteers who work more than 100 hours per year are given special rewards. The reward granting unit also builds a system that regularly evaluates volunteer activity history and increases rewards based on cumulative contributions. For example, volunteers who work continuously for five years are given special titles and honors. The reward granting unit also uses AI to analyze volunteer activity history and gradually increase rewards based on long-term contributions. For example, reward points are accumulated based on the number of hours worked each year, and special rewards are provided when a certain number of points are reached. This allows rewards to cumulatively increase based on long-term contributions.

[0032] The reward granting unit can revitalize the local economy by providing local specialty products or services as rewards for volunteer activities. The reward granting unit, for example, provides local specialty products as rewards for volunteer activities. For example, local agricultural products or crafts can be provided as rewards to revitalize the local economy. The reward granting unit also provides local services as rewards for volunteer activities. For example, vouchers for use at local hot spring facilities or restaurants can be provided as rewards to support the local tourism industry. The reward granting unit also provides tickets to local events or festivals as rewards for volunteer activities. For example, tickets to local music festivals or cultural events can be provided as rewards to support local cultural activities. This can revitalize the local economy.

[0033] The reward granting unit can apply the reward for volunteer activities to other social contribution activities, thereby promoting a wide range of social contributions. The reward granting unit, for example, applies the reward for volunteer activities to other social contribution activities. For example, a volunteer who participates in environmental protection activities is provided with an environment-related reward. The reward granting unit also provides an education-related reward to a volunteer who participates in educational support activities. For example, education-related books and teaching materials are provided as rewards for volunteer work at educational institutions. The reward granting unit also builds a system that provides rewards for a wide range of social contribution activities. For example, volunteers who participate in medical support activities or disaster relief activities are provided with rewards according to their respective activities. This makes it possible to promote a wide range of social contributions.

[0034] The evaluation unit can analyze video data of volunteer activities and evaluate the quality and efficiency of the activities. For example, the evaluation unit uses AI to analyze video data of volunteer activities and evaluate the quality and efficiency of the activities. For example, it analyzes the movements and procedures during the activity and evaluates whether the work is being carried out efficiently. The evaluation unit also uses AI to analyze the content of the volunteer's activities in detail based on the video data and evaluates the quality of the activities. For example, it evaluates the state of communication and cooperation during the activity. The evaluation unit also builds a system in which AI analyzes the video data and evaluates the efficiency of the volunteer's activities. For example, it analyzes the working time and workload and evaluates whether the activities are being carried out efficiently. This makes it possible to evaluate the quality and efficiency of the activities.

[0035] The evaluation unit can use AI to automatically generate activity reports using natural language processing to evaluate volunteer activities and reflect the contents of those reports in the evaluation. For example, the evaluation unit uses AI to automatically generate volunteer activity reports using natural language processing. For example, it analyzes the activity content and results as text data and creates a report. The evaluation unit also uses AI to evaluate the volunteer activities based on the automatically generated activity reports. For example, it analyzes the content of the report and evaluates the quality and results of the activities. The evaluation unit also builds a system in which AI uses natural language processing to automatically generate volunteer activity reports and reflects the contents of those reports in the evaluation. For example, it analyzes the text data of the report and calculates an evaluation score. This allows activity reports to be automatically generated and their contents to be reflected in the evaluation.

[0036] The evaluation department can link the evaluation of volunteer activities with other evaluation systems to perform a comprehensive evaluation. The evaluation department, for example, links the evaluation of volunteer activities with performance evaluation at work. For example, the evaluation department performs a comprehensive evaluation of volunteer activities based on evaluation data at work. The evaluation department also builds a system that links academic performance evaluation with the evaluation of volunteer activities. For example, the evaluation department evaluates volunteer activities based on academic performance data. The evaluation department also links the evaluation of volunteer activities with other evaluation systems to perform a comprehensive evaluation. For example, evaluation data from the workplace or school is integrated and reflected in the evaluation of volunteer activities. This allows for a comprehensive evaluation.

[0037] The evaluation department can use the evaluation results of volunteer activities to plan and improve other volunteer activities, thereby improving the quality of the activities. For example, the evaluation department plans other volunteer activities based on the evaluation results of volunteer activities. For example, it creates new activity plans by referring to activities that have received high evaluations. The evaluation department also uses the evaluation results to identify areas for improvement in volunteer activities and improve the quality of the activities. For example, it proposes improvement measures for activities that have received low evaluations. The evaluation department also builds a system that feeds back the evaluation results of volunteer activities to other activities and improves the quality of the activities. For example, it shares areas for improvement in activities based on the evaluation results. This can improve the quality of the activities.

[0038] The system provider can develop an algorithm that analyzes volunteer activity data and automatically generates an optimal compensation plan. For example, the system provider collects volunteer activity data, and AI analyzes the data to automatically generate an optimal compensation plan. For example, it proposes compensation based on the activity content and level of contribution. The system provider also develops an algorithm that automatically generates a compensation plan based on volunteer activity data. For example, it sets compensation based on the type of activity and time. The system provider also builds a system that analyzes volunteer activity data and uses AI to propose an optimal compensation plan. For example, it determines compensation based on the results and evaluation of the activity. This makes it possible to develop an algorithm that automatically generates an optimal compensation plan.

[0039] The system provider can introduce a system that monitors the results of volunteer activities in real time and awards rewards instantly. For example, the system provider can build a system that monitors the results of volunteer activities in real time and awards rewards instantly. For example, reward points can be awarded immediately after the activity is completed. The system provider can also introduce a system that evaluates the results of volunteer activities in real time and awards rewards instantly. For example, data during the activity can be analyzed and rewards can be determined instantly. The system provider can also develop a system that monitors the results of volunteer activities in real time and awards rewards instantly using AI. For example, rewards can be awarded in stages depending on the progress of the activity. This makes it possible to monitor the results of volunteer activities in real time and award rewards instantly.

[0040] The system provider provides a mechanism for providing rewards for other social contribution activities, thereby promoting contributions to society as a whole. The system provider provides a mechanism for providing rewards for donation activities, for example. For example, reward points are awarded when a certain amount of donation is made. The system provider also builds a system for providing rewards for environmental protection activities, for example, providing rewards to volunteers who participate in recycling activities or tree planting activities. The system provider also provides a mechanism for providing rewards for other social contribution activities, thereby promoting contributions to society as a whole. For example, rewards are provided to volunteers who participate in local cleanup activities or welfare activities. This makes it possible to promote contributions to society as a whole.

[0041] The system provider can analyze data regarding the granting of rewards and build a feedback loop to maximize the effectiveness of rewards. For example, the system provider collects data regarding the granting of rewards and builds a feedback loop in which AI analyzes the data to maximize the effectiveness of rewards. For example, the system provider adjusts the type and timing of rewards. The system provider also evaluates the effectiveness of rewards based on the data regarding the granting of rewards and proposes an optimal reward plan. For example, the system provider provides additional rewards for activities that are highly effective in rewarding. The system provider also develops a system that analyzes data regarding the granting of rewards and builds a feedback loop to maximize the effectiveness of rewards. For example, the system provider monitors the effectiveness of rewards in real time and adjusts as necessary. This makes it possible to build a feedback loop to maximize the effectiveness of rewards.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The reward granting unit can provide local specialties and services as rewards for volunteer activities, thereby revitalizing the local economy. For example, local agricultural products and crafts can be provided as rewards to revitalize the local economy. It can also provide vouchers for local hot spring facilities and restaurants as rewards to support the local tourism industry. It can also provide tickets to local events and festivals to support local cultural activities. This can revitalize the local economy.

[0044] The evaluation unit can analyze video data of volunteer activities and evaluate the quality and efficiency of the activities. For example, it can analyze the movements and procedures during the activity and evaluate whether the work is being carried out efficiently. In addition, based on the video data, AI can analyze the volunteer's activities in detail and evaluate the quality of the activities. Furthermore, it is possible to build a system that analyzes the work time and workload and evaluates whether the activities are being carried out efficiently. This makes it possible to evaluate the quality and efficiency of the activities.

[0045] The evaluation unit can use AI to automatically generate activity reports using natural language processing to evaluate volunteer activities, and reflect the content of those reports in the evaluation. For example, it can analyze the activity content and results as text data and create a report. AI can also evaluate volunteer activities based on the automatically generated activity reports. Furthermore, it can also build a system that analyzes the text data of the reports and calculates an evaluation score. This allows activity reports to be automatically generated and their content to be reflected in the evaluation.

[0046] The evaluation department can use the evaluation results of volunteer activities to plan and improve other volunteer activities, thereby improving the quality of the activities. For example, they can create new activity plans using activities that have received high evaluations as a reference. They can also use the evaluation results to identify areas for improvement in volunteer activities and improve the quality of the activities. Furthermore, they can build a system that feeds back the evaluation results to other activities and improves the quality of activities. This can improve the quality of activities.

[0047] The system provider can develop an algorithm that analyzes volunteer activity data and automatically generates the optimal compensation plan. For example, it can propose compensation based on the activity content and level of contribution. It can also develop an algorithm that automatically generates compensation plans based on volunteer activity data. It can also build a system that determines compensation based on the results and evaluation of the activity. This makes it possible to develop an algorithm that automatically generates the optimal compensation plan.

[0048] The system provider can provide a system for providing rewards for other social contribution activities, thereby promoting contributions to society as a whole. For example, a system for providing rewards for donation activities can be provided. Reward points can be awarded when a certain amount of donation is made. A system can also be built to provide rewards for environmental protection activities. Reward volunteers who participate in recycling activities or tree planting activities can be provided. Furthermore, a system can be built to provide rewards to volunteers who participate in local cleanup activities or welfare activities. This can promote contributions to society as a whole.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The reward granting unit grants points, honor, and status that can be exchanged for cash for volunteer activities. For example, if a volunteer performs a certain activity, points corresponding to that activity are awarded. These points can later be exchanged for cash, gift certificates, etc. In addition, by awarding honor and status for specific activities, volunteer motivation is improved. Step 2: The evaluation department uses AI to score the results of the volunteer activities. For example, the AI ​​analyzes the time, content, and deliverables of the volunteer's activities and evaluates them based on that. The AI ​​uses text generation AI (e.g., LLM) and multimodal generation AI to analyze the activity content and calculate an evaluation score. Step 3: The system provider acts as an intermediary between the volunteer group and the local government and awards rewards in return for social contributions. For example, the local government provides a points budget, and the system provider awards those points to the volunteers. These points are awarded based on an evaluation of the volunteer activities.

[0051] (Example 2) The volunteer activity support system according to an embodiment of the present invention is a system that awards points, honor, and status that can be converted into cash for volunteer activities, evaluates them using AI, and provides rewards. This system can improve the motivation of volunteers and stimulate their activities.

[0052] A volunteer activity support system according to an embodiment includes a reward granting unit, an evaluation unit, and a system providing unit. The reward granting unit grants points, honor, and status that can be exchanged for volunteer activities. For example, if a volunteer performs a certain activity, points corresponding to the activity are awarded. These points can later be exchanged for cash, gift certificates, or the like. Furthermore, by awarding honor and status for specific activities, volunteers' motivation is increased. The evaluation unit uses AI to score the results of volunteer activities. For example, the AI ​​analyzes the time, content, and deliverables of the volunteer's activities and evaluates them based on the results. The AI ​​analyzes the activity content and calculates an evaluation score using text generation AI (e.g., LLM) or multimodal generation AI. The system providing unit acts as an intermediary between volunteer organizations and local governments and awards rewards in return for social contributions. For example, the local government provides a points budget, and the system provider awards these points to volunteers. These points are awarded based on the evaluation of the volunteer activity. This allows the volunteer activity support system to increase volunteer motivation and stimulate volunteer activity.

[0053] The reward granting unit uses AI to generate individually customized reward plans based on the content of the volunteer activity, allowing the AI ​​to provide rewards tailored to the needs of each volunteer. For example, the reward granting unit analyzes the content of the volunteer activity in detail, and the AI ​​generates individually customized reward plans. For example, volunteers who perform educational support activities are rewarded with educational books and teaching materials. The reward granting unit also considers the volunteer's past activity history and skill set and uses AI to propose optimal reward plans. For example, medical volunteers are rewarded with medical equipment and specialized books. The reward granting unit also collects the individual needs and wishes of volunteers in the form of a questionnaire, and the AI ​​generates customized reward plans based on that data. For example, volunteers who perform environmental protection activities are rewarded with eco-friendly products and recycled goods. This makes it possible to provide rewards tailored to the individual needs of volunteers.

[0054] The reward granting unit uses AI to analyze volunteer activity history and cumulatively increase rewards based on long-term contributions. For example, the reward granting unit uses AI to analyze volunteer activity history data and cumulatively increase rewards based on long-term contributions. For example, volunteers who work more than 100 hours per year are given special rewards. The reward granting unit also builds a system that regularly evaluates volunteer activity history and increases rewards based on cumulative contributions. For example, volunteers who work continuously for five years are given special titles and honors. The reward granting unit also uses AI to analyze volunteer activity history and gradually increase rewards based on long-term contributions. For example, reward points are accumulated based on the number of hours worked each year, and special rewards are provided when a certain number of points are reached. This allows rewards to cumulatively increase based on long-term contributions.

[0055] The reward granting unit can use the emotion estimation function to evaluate the emotional state of a volunteer and propose a reward plan that elicits positive emotions. For example, the reward granting unit uses the emotion estimation function to evaluate the emotional state of a volunteer in real time and propose a reward plan that elicits positive emotions. For example, if positive emotions are strong during an activity, a special reward is provided. The reward granting unit also collects emotional data of volunteers and uses AI to analyze the data to generate a reward plan that elicits positive emotions. For example, a thank-you message or an invitation to a special event is provided as a reward. The reward granting unit also uses the emotion estimation function to evaluate the emotional state of a volunteer and provide incentives to elicit positive emotions. For example, additional reward points are awarded to volunteers who receive positive feedback after an activity. This makes it possible to propose a reward plan that elicits positive emotions.

[0056] The reward granting unit can revitalize the local economy by providing local specialty products or services as rewards for volunteer activities. The reward granting unit, for example, provides local specialty products as rewards for volunteer activities. For example, local agricultural products or crafts can be provided as rewards to revitalize the local economy. The reward granting unit also provides local services as rewards for volunteer activities. For example, vouchers for use at local hot spring facilities or restaurants can be provided as rewards to support the local tourism industry. The reward granting unit also provides tickets to local events or festivals as rewards for volunteer activities. For example, tickets to local music festivals or cultural events can be provided as rewards to support local cultural activities. This can revitalize the local economy.

[0057] The reward granting unit can apply the reward for volunteer activities to other social contribution activities, thereby promoting a wide range of social contributions. The reward granting unit, for example, applies the reward for volunteer activities to other social contribution activities. For example, a volunteer who participates in environmental protection activities is provided with an environment-related reward. The reward granting unit also provides an education-related reward to a volunteer who participates in educational support activities. For example, education-related books and teaching materials are provided as rewards for volunteer work at educational institutions. The reward granting unit also builds a system that provides rewards for a wide range of social contribution activities. For example, volunteers who participate in medical support activities or disaster relief activities are provided with rewards according to their respective activities. This makes it possible to promote a wide range of social contributions.

[0058] The reward granting unit can use the emotion estimation function to analyze the emotions of volunteers when they receive their rewards and identify the reward format that provides the highest level of satisfaction. For example, the reward granting unit uses the emotion estimation function to analyze the emotions of volunteers when they receive their rewards in real time. For example, it analyzes their facial expressions and voices at the moment they receive their rewards and evaluates their level of satisfaction. The reward granting unit also collects emotional data from volunteers and uses AI to analyze the data to identify the reward format that provides the highest level of satisfaction. For example, it prioritizes providing reward formats with high emotion scores. The reward granting unit also uses the emotion estimation function to analyze the emotions of volunteers when they receive their rewards and suggests reward formats that provide the highest level of satisfaction. For example, it presents options for reward formats and allows the volunteer to choose the format that provides the highest level of satisfaction. This makes it possible to identify the reward format that provides the highest level of satisfaction.

[0059] The evaluation unit can analyze video data of volunteer activities and evaluate the quality and efficiency of the activities. For example, the evaluation unit uses AI to analyze video data of volunteer activities and evaluate the quality and efficiency of the activities. For example, it analyzes the movements and procedures during the activity and evaluates whether the work is being carried out efficiently. The evaluation unit also uses AI to analyze the content of the volunteer's activities in detail based on the video data and evaluates the quality of the activities. For example, it evaluates the state of communication and cooperation during the activity. The evaluation unit also builds a system in which AI analyzes the video data and evaluates the efficiency of the volunteer's activities. For example, it analyzes the working time and workload and evaluates whether the activities are being carried out efficiently. This makes it possible to evaluate the quality and efficiency of the activities.

[0060] The evaluation unit can use AI to automatically generate activity reports using natural language processing to evaluate volunteer activities and reflect the contents of those reports in the evaluation. For example, the evaluation unit uses AI to automatically generate volunteer activity reports using natural language processing. For example, it analyzes the activity content and results as text data and creates a report. The evaluation unit also uses AI to evaluate the volunteer activities based on the automatically generated activity reports. For example, it analyzes the content of the report and evaluates the quality and results of the activities. The evaluation unit also builds a system in which AI uses natural language processing to automatically generate volunteer activity reports and reflects the contents of those reports in the evaluation. For example, it analyzes the text data of the report and calculates an evaluation score. This allows activity reports to be automatically generated and their contents to be reflected in the evaluation.

[0061] The evaluation unit can use the emotion estimation function to analyze changes in emotions during volunteer activities and incorporate emotional fulfillment into the evaluation. The evaluation unit, for example, uses the emotion estimation function to analyze changes in emotions during volunteer activities in real time. For example, it analyzes facial expressions and voices during activities and evaluates emotional changes. The evaluation unit also collects emotional data from volunteers, analyzes the data using AI, and incorporates emotional fulfillment into the evaluation. For example, activities with a high proportion of positive emotions are rated highly. The evaluation unit also uses the emotion estimation function to analyze changes in emotions during volunteer activities and build a system that reflects emotional fulfillment in the evaluation. For example, it calculates an evaluation score based on the emotion score. This makes it possible to incorporate emotional fulfillment into the evaluation.

[0062] The evaluation department can link the evaluation of volunteer activities with other evaluation systems to perform a comprehensive evaluation. The evaluation department, for example, links the evaluation of volunteer activities with performance evaluation at work. For example, the evaluation department performs a comprehensive evaluation of volunteer activities based on evaluation data at work. The evaluation department also builds a system that links academic performance evaluation with the evaluation of volunteer activities. For example, the evaluation department evaluates volunteer activities based on academic performance data. The evaluation department also links the evaluation of volunteer activities with other evaluation systems to perform a comprehensive evaluation. For example, evaluation data from the workplace or school is integrated and reflected in the evaluation of volunteer activities. This allows for a comprehensive evaluation.

[0063] The evaluation department can use the evaluation results of volunteer activities to plan and improve other volunteer activities, thereby improving the quality of the activities. For example, the evaluation department plans other volunteer activities based on the evaluation results of volunteer activities. For example, it creates new activity plans by referring to activities that have received high evaluations. The evaluation department also uses the evaluation results to identify areas for improvement in volunteer activities and improve the quality of the activities. For example, it proposes improvement measures for activities that have received low evaluations. The evaluation department also builds a system that feeds back the evaluation results of volunteer activities to other activities and improves the quality of the activities. For example, it shares areas for improvement in activities based on the evaluation results. This can improve the quality of the activities.

[0064] The evaluation unit uses the emotion estimation function to collect volunteers' emotional responses to the evaluation results of their volunteer activities, which can be used to improve the evaluation system. For example, the evaluation unit uses the emotion estimation function to collect volunteers' emotional responses to the evaluation results of their volunteer activities in real time. For example, it analyzes facial expressions and voices when they receive the evaluation results. The evaluation unit also collects emotional data from volunteers, and AI analyzes the data to help improve the evaluation system. For example, if there are many positive responses to the evaluation results, it strengthens the evaluation criteria. The evaluation unit also uses the emotion estimation function to collect emotional responses to the evaluation results of volunteer activities, and builds a system that reflects this in improving the evaluation system. For example, it adjusts the evaluation criteria based on the emotion scores. This can be used to improve the evaluation system.

[0065] The system provider can develop an algorithm that analyzes volunteer activity data and automatically generates an optimal compensation plan. For example, the system provider collects volunteer activity data, and AI analyzes the data to automatically generate an optimal compensation plan. For example, it proposes compensation based on the activity content and level of contribution. The system provider also develops an algorithm that automatically generates a compensation plan based on volunteer activity data. For example, it sets compensation based on the type of activity and time. The system provider also builds a system that analyzes volunteer activity data and uses AI to propose an optimal compensation plan. For example, it determines compensation based on the results and evaluation of the activity. This makes it possible to develop an algorithm that automatically generates an optimal compensation plan.

[0066] The system provider can introduce a system that monitors the results of volunteer activities in real time and awards rewards instantly. For example, the system provider can build a system that monitors the results of volunteer activities in real time and awards rewards instantly. For example, reward points can be awarded immediately after the activity is completed. The system provider can also introduce a system that evaluates the results of volunteer activities in real time and awards rewards instantly. For example, data during the activity can be analyzed and rewards can be determined instantly. The system provider can also develop a system that monitors the results of volunteer activities in real time and awards rewards instantly using AI. For example, rewards can be awarded in stages depending on the progress of the activity. This makes it possible to monitor the results of volunteer activities in real time and award rewards instantly.

[0067] The system providing unit can use the emotion estimation function to analyze the emotions of volunteers when they receive their rewards and optimize the format and timing of the rewards. For example, the system providing unit uses the emotion estimation function to analyze the emotions of volunteers when they receive their rewards in real time. For example, it analyzes their facial expressions and voices at the moment they receive their rewards and determines the optimal format of the rewards. The system providing unit also collects emotional data from volunteers and uses AI to analyze the data to optimize the format and timing of the rewards. For example, it provides rewards at times when the emotional score is high. The system providing unit also uses the emotion estimation function to analyze the emotions of volunteers when they receive their rewards and builds a system that suggests the optimal format and timing of the rewards. For example, it presents options for reward formats and allows volunteers to choose the format that satisfies them most. This makes it possible to optimize the format and timing of the rewards.

[0068] The system provider provides a mechanism for providing rewards for other social contribution activities, thereby promoting contributions to society as a whole. The system provider provides a mechanism for providing rewards for donation activities, for example. For example, reward points are awarded when a certain amount of donation is made. The system provider also builds a system for providing rewards for environmental protection activities, for example, providing rewards to volunteers who participate in recycling activities or tree planting activities. The system provider also provides a mechanism for providing rewards for other social contribution activities, thereby promoting contributions to society as a whole. For example, rewards are provided to volunteers who participate in local cleanup activities or welfare activities. This makes it possible to promote contributions to society as a whole.

[0069] The system provider can analyze data regarding the granting of rewards and build a feedback loop to maximize the effectiveness of rewards. For example, the system provider collects data regarding the granting of rewards and builds a feedback loop in which AI analyzes the data to maximize the effectiveness of rewards. For example, the system provider adjusts the type and timing of rewards. The system provider also evaluates the effectiveness of rewards based on the data regarding the granting of rewards and proposes an optimal reward plan. For example, the system provider provides additional rewards for activities that are highly effective in rewarding. The system provider also develops a system that analyzes data regarding the granting of rewards and builds a feedback loop to maximize the effectiveness of rewards. For example, the system provider monitors the effectiveness of rewards in real time and adjusts as necessary. This makes it possible to build a feedback loop to maximize the effectiveness of rewards.

[0070] The system providing unit uses the emotion estimation function to collect the emotional responses of volunteers to the granting of rewards, which can be used to improve the reward system. For example, the system providing unit uses the emotion estimation function to collect the emotional responses of volunteers to the granting of rewards in real time. For example, the system providing unit analyzes facial expressions and voices when receiving rewards and calculates an emotion score. The system providing unit also collects emotional data of volunteers, and AI analyzes the data to help improve the reward system. For example, reward formats with high emotion scores are given priority. The system providing unit also uses the emotion estimation function to collect the emotional responses of volunteers to the granting of rewards, and builds a system that reflects this in improving the reward system. For example, the reward format and timing are adjusted based on the emotion score. This can be used to help improve the reward system.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reward granting unit can provide local specialties and services as rewards for volunteer activities, thereby revitalizing the local economy. For example, local agricultural products and crafts can be provided as rewards to revitalize the local economy. It can also provide vouchers for local hot spring facilities and restaurants as rewards to support the local tourism industry. It can also provide tickets to local events and festivals to support local cultural activities. This can revitalize the local economy.

[0073] The evaluation unit can analyze video data of volunteer activities and evaluate the quality and efficiency of the activities. For example, it can analyze the movements and procedures during the activity and evaluate whether the work is being carried out efficiently. In addition, based on the video data, AI can analyze the volunteer's activities in detail and evaluate the quality of the activities. Furthermore, it is possible to build a system that analyzes the work time and workload and evaluates whether the activities are being carried out efficiently. This makes it possible to evaluate the quality and efficiency of the activities.

[0074] The reward granting unit can use the emotion estimation function to evaluate the emotional state of the volunteer and propose a reward plan that elicits positive emotions. For example, if the volunteer has strong positive emotions during the activity, a special reward can be provided. It can also provide a thank-you message or an invitation to a special event as a reward. Furthermore, it can grant additional reward points to volunteers who receive positive feedback after the activity. This makes it possible to propose a reward plan that elicits positive emotions.

[0075] The evaluation unit can use AI to automatically generate activity reports using natural language processing to evaluate volunteer activities, and reflect the content of those reports in the evaluation. For example, it can analyze the activity content and results as text data and create a report. AI can also evaluate volunteer activities based on the automatically generated activity reports. Furthermore, it can also build a system that analyzes the text data of the reports and calculates an evaluation score. This allows activity reports to be automatically generated and their content to be reflected in the evaluation.

[0076] The reward granting unit can use the emotion estimation function to analyze the emotions of volunteers when they receive their reward and identify the reward format that provides the highest level of satisfaction. For example, it can analyze facial expressions and voice at the moment of receiving the reward to evaluate satisfaction. It can also collect emotion data and use AI to analyze that data to identify the reward format that provides the highest level of satisfaction. It can also present options for reward formats so that the volunteer can choose the format that provides the highest level of satisfaction. This makes it possible to identify the reward format that provides the highest level of satisfaction.

[0077] The evaluation department can use the evaluation results of volunteer activities to plan and improve other volunteer activities, thereby improving the quality of the activities. For example, they can create new activity plans using activities that have received high evaluations as a reference. They can also use the evaluation results to identify areas for improvement in volunteer activities and improve the quality of the activities. Furthermore, they can build a system that feeds back the evaluation results to other activities and improves the quality of activities. This can improve the quality of activities.

[0078] The system provider can develop an algorithm that analyzes volunteer activity data and automatically generates the optimal compensation plan. For example, it can propose compensation based on the activity content and level of contribution. It can also develop an algorithm that automatically generates compensation plans based on volunteer activity data. It can also build a system that determines compensation based on the results and evaluation of the activity. This makes it possible to develop an algorithm that automatically generates the optimal compensation plan.

[0079] The system provider can use the emotion estimation function to analyze the emotions of volunteers when they receive their rewards and optimize the format and timing of the rewards. For example, it can analyze facial expressions and voice at the moment of receiving the reward to determine the optimal format of the reward. It can also collect emotion data and use AI to analyze that data to optimize the format and timing of the rewards. It can also build a system that presents options for reward formats and allows volunteers to choose the format that satisfies them most. This makes it possible to optimize the format and timing of the rewards.

[0080] The system provider can provide a system for providing rewards for other social contribution activities, thereby promoting contributions to society as a whole. For example, a system for providing rewards for donation activities can be provided. Reward points can be awarded when a certain amount of donation is made. A system can also be built to provide rewards for environmental protection activities. Reward volunteers who participate in recycling activities or tree planting activities can be provided. Furthermore, a system can be built to provide rewards to volunteers who participate in local cleanup activities or welfare activities. This can promote contributions to society as a whole.

[0081] The system provider can use the emotion estimation function to collect the emotional responses of volunteers to the awarding of rewards and use the collected data to improve the reward system. For example, the system provider can analyze facial expressions and voices when receiving rewards and calculate an emotion score. It can also collect emotion data and use AI to analyze the data to improve the reward system. It can also build a system that adjusts the reward format and timing based on the emotion score. This can be useful for improving the reward system.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The reward granting unit grants points, honor, and status that can be exchanged for cash for volunteer activities. For example, if a volunteer performs a certain activity, points corresponding to that activity are awarded. These points can later be exchanged for cash, gift certificates, etc. In addition, by awarding honor and status for specific activities, volunteer motivation is improved. Step 2: The evaluation department uses AI to score the results of the volunteer activities. For example, the AI ​​analyzes the time, content, and deliverables of the volunteer's activities and evaluates them based on that. The AI ​​uses text generation AI (e.g., LLM) and multimodal generation AI to analyze the activity content and calculate an evaluation score. Step 3: The system provider acts as an intermediary between the volunteer group and the local government and awards rewards in return for social contributions. For example, the local government provides a points budget, and the system provider awards those points to the volunteers. These points are awarded based on an evaluation of the volunteer activities.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0120] The 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.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 reward awarding unit that awards points, honor, and status that can be exchanged for cash for volunteer activities; an evaluation department that uses AI to score the results of the volunteer activities; and a system provider that acts as an intermediary between volunteer groups and local governments and provides rewards in return for social contributions. A system characterized by:

2. The reward granting unit Based on the content of the volunteer activities, the AI ​​generates an individually customized compensation plan and provides compensation according to the needs of each individual volunteer.

2. The system of claim 1.

3. The reward granting unit The AI ​​analyzes the volunteer activity history and cumulatively increases the reward based on the long-term contribution.

2. The system of claim 1.

4. The reward granting unit Assess the emotional state of volunteers and propose reward plans that elicit positive emotions 2. The system of claim 1.

5. The reward granting unit In return for volunteering, local specialties and services will be provided to revitalize the local economy.

2. The system of claim 1.

6. The reward granting unit The rewards for volunteer activities will be applied to other social contribution activities to promote a wide range of social contributions.

2. The system of claim 1.

7. The reward granting unit Analyze volunteers' emotions when receiving compensation and identify the most satisfying forms of compensation 2. The system of claim 1.

8. The evaluation unit Analyze the video data of the volunteer activities and evaluate the quality and efficiency of the activities.

2. The system of claim 1.

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