System

Abstract

An object of the system according to the embodiment is to enable an individual to specifically indicate an action for contributing to an environmental problem and to realize an effect thereof.SOLUTION: A system includes an action report part, an emission reduction amount calculation part, a visualization part, and an action suggestion part. The action reporting unit reports an action of the user. The emission reduction amount calculation unit calculates an emission reduction amount based on the action reported by the action reporting unit. The visualizer visualizes the emission reduction amount calculated by the emission reduction amount calculator. The action suggestion unit suggests a further action on the basis of the result visualized by the visualization unit.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 have made it difficult for individuals to take concrete actions to contribute to environmental issues or to realize the effects of those actions, making it difficult for them to continue participating.

[0005] The system according to the embodiment aims to provide individuals with concrete examples of actions they can take to contribute to environmental issues, and to enable them to realize the effects of such actions. [Means for solving the problem]

[0006] The system according to the embodiment includes an action reporting unit, an emission reduction calculation unit, a visualization unit, and an action suggestion unit. The action reporting unit reports user actions. The emission reduction calculation unit calculates an emission reduction based on the actions reported by the action reporting unit. The visualization unit visualizes the emission reduction calculated by the emission reduction calculation unit. The action suggestion unit suggests further actions based on the results visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific examples of actions that individuals can take to contribute to environmental issues, allowing them to realize the effects of such actions. [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 Net Zero Contribution System according to an embodiment of the present invention is a system that encourages individuals to take actions to contribute to "Net Zero" and enables them to realize the effects of their actions. In this Net Zero Contribution System, a generative AI reports individuals' actions, calculates and visualizes the amount of emissions reductions resulting from those actions, and suggests further actions. In this way, the Net Zero Contribution System can raise individuals' awareness of contributing to environmental issues and contribute to the realization of a sustainable society.

[0029] A net-zero contribution system according to an embodiment includes an action reporting unit, an emission reduction calculation unit, a visualization unit, and an action suggestion unit. The action reporting unit reports a user's action. For example, a user can report, "Today, I commuted by bicycle." Furthermore, the action reporting unit uses a generation AI to analyze the user's action and organize the report content. For example, the generation AI analyzes the user's report using natural language processing technology and extracts details of the action. The emission reduction calculation unit calculates the emission reduction amount based on the action reported by the action reporting unit. For example, it calculates the emission reduction amount if commuting by bicycle. Furthermore, the emission reduction calculation unit evaluates the environmental impact of the reported action and calculates the specific reduction amount. For example, the generation AI calculates the reduction in CO2 emissions resulting from a change in transportation mode. The visualization unit visualizes the emission reduction amount calculated by the emission reduction calculation unit. For example, it displays it as a graph or numerical values. Furthermore, the visualization unit displays the calculation results of the generation AI in a visually easy-to-understand manner. For example, the generation AI displays the reduction amount as a pie chart or bar graph. The action suggestion unit suggests further actions based on the results visualized by the visualization unit. For example, it suggests, "Try using public transportation next time." The action suggestion unit also uses the generation AI to suggest new actions based on the user's behavioral data. For example, the generation AI analyzes the user's past behavioral history and suggests optimal actions. In this way, the net-zero contribution system according to the embodiment can specifically grasp the impact that an individual's behavior has on the environment and encourage further actions.

[0030] The behavior reporting unit can refer to the user's past behavior history and check the consistency of the reported content. For example, when a user reports an action, the generation AI refers to the past behavior history and checks whether the reported content is consistent. For example, it checks whether there are any inconsistencies with the action reported the previous day. The behavior reporting unit also analyzes the user's past behavior history and provides feedback on the reported content. For example, it evaluates whether there has been progress compared with past actions. Furthermore, when a user reports an action, the generation AI checks for consistency based on the past behavior history and prompts corrections if there are any inconsistencies. For example, it checks whether the same action has been reported multiple times. This makes it possible to maintain consistency in the reported content.

[0031] The behavior reporting unit enables behavior reports to be made using not only voice input but also images or videos, and can also analyze visual information. For example, the behavior reporting unit allows users to upload images and videos in addition to voice input when reporting an activity. For example, a video of the recycling process may be included in the report. In addition, the behavior reporting unit uses a generation AI to analyze the uploaded images and videos and reflect them in the report. For example, image recognition technology may be used to identify the types of items recycled. In addition, when a user reports an activity, the generation AI combines and analyzes the voice input with images and videos to generate more detailed report content. For example, a video of a bicycle commute may be integrated with an audio description. This enables detailed reports that also include visual information.

[0032] The behavior reporting unit can integrate behavior reports from different devices to provide a seamless user experience. For example, the behavior reporting unit allows users to report behavior from different devices, such as smartphones, smartwatches, and smart speakers, and the generation AI integrates the data. For example, it links exercise data from a smartwatch with behavior reports from a smartphone. The behavior reporting unit also seamlessly integrates behavior reports from different devices to provide consistent feedback to the user. For example, it integrates audio reports from a smart speaker with image reports from a smartphone. When a user reports behavior from different devices, the generation AI synchronizes data between devices in real time to provide a seamless user experience. For example, it automatically updates the exercise data from a smartwatch in a smartphone app. This allows behavior reports from different devices to be integrated to provide a seamless user experience.

[0033] The emission reduction calculation unit can compare the emission reduction amounts for each region based on the user's behavioral data and promote competition between regions. In the emission reduction calculation unit, for example, the generation AI calculates and compares the emission reduction amounts for each region based on the user's behavioral data. For example, the emission reduction amount for each region is displayed in a graph to promote competition between regions. The emission reduction calculation unit also analyzes the user's behavioral data and displays the emission reduction amount for each region in a ranking format. For example, special titles and rewards are provided to top-ranking regions. In addition, the emission reduction calculation unit updates the emission reduction amount for each region in real time using the generation AI to provide the user with the latest information. For example, the fluctuations in emission reduction amount for each region are displayed in a graph to promote competition. This can promote competition between regions and raise awareness of emission reductions.

[0034] The emission reduction calculation unit takes into account external data such as weather or traffic conditions when calculating the emission reduction amount, allowing for more accurate calculations. For example, the emission reduction calculation unit takes weather data into account when the generation AI calculates the emission reduction amount. For example, the emission reduction amount of commuting by bicycle on rainy days is adjusted compared to normal times. The emission reduction calculation unit also calculates the emission reduction amount based on traffic condition data. For example, the emission reduction effect of using public transportation during traffic congestion is adjusted compared to normal times. The emission reduction calculation unit also collects external data (weather, traffic conditions, etc.) in real time and reflects this in the calculation of the emission reduction amount. For example, the emission reduction amount is dynamically adjusted according to changes in weather or traffic conditions. In this way, by taking external data into account, more accurate calculation of the emission reduction amount is possible.

[0035] The emission reduction calculation unit can visualize the emission reduction amount using augmented reality (AR) technology, allowing the user to visually experience the reduction effect in an actual environment. In the emission reduction calculation unit, for example, the generation AI visualizes the emission reduction amount using augmented reality (AR) technology, allowing the user to visually experience the reduction effect in an actual environment. For example, the emission reduction effect is displayed through a smartphone camera. The emission reduction calculation unit also allows the user to experience the visualization of the emission reduction amount using an AR device. For example, the emission reduction effect is displayed in real time through AR glasses. In the emission reduction calculation unit, the generation AI also uses AR technology to superimpose the emission reduction effect on the user's surrounding environment. For example, the energy consumption reduction effect at home is visualized using AR. This allows the user to visually experience the reduction effect in an actual environment.

[0036] The emission reduction calculation unit can link with different data sources to perform a more multifaceted calculation of emission reductions. For example, the generation AI in the emission reduction calculation unit links with smart meter and energy consumption data to more accurately calculate emission reductions. For example, it calculates the emission reduction effect based on household energy consumption data. The emission reduction calculation unit also integrates different data sources (smart meter, energy consumption data, etc.), and the generation AI calculates a multifaceted emission reduction amount. For example, it combines multiple data sources to evaluate the overall emission reduction effect. The emission reduction calculation unit also uses the generation AI to collect data from different data sources in real time and reflect this in the calculation of emission reductions. For example, it obtains smart meter data in real time and dynamically calculates the emission reduction effect. This makes it possible to calculate emission reductions more multifaceted by linking with different data sources.

[0037] The action suggestion unit can make individually customized action suggestions based on the user's past behavioral data. In the action suggestion unit, for example, the generation AI analyzes the user's past behavioral data and makes individually customized action suggestions. For example, a user who has commuted by bicycle in the past is recommended to commute by bicycle again next time. In addition, the action suggestion unit makes individually customized action suggestions based on the user's behavior history. For example, a user who frequently recycles is suggested a new recycling method. In addition, the action suggestion unit makes individually customized action suggestions based on the user's past behavioral data. For example, a user who uses eco-bags is suggested an even more environmentally friendly action. In this way, it is possible to make individually customized action suggestions for the user.

[0038] The action suggestion unit can make feasible suggestions by taking into account the user's lifestyle patterns and schedule. In the action suggestion unit, for example, the generation AI analyzes the user's lifestyle patterns and schedule and makes feasible action suggestions. For example, suggestions are made taking into account commuting time and how people spend their holidays. The action suggestion unit also makes feasible action suggestions by the generation AI based on the user's schedule data. For example, it suggests environmentally conscious actions that can be easily carried out on busy days. The action suggestion unit also analyzes the user's lifestyle patterns and makes feasible action suggestions. For example, it suggests environmentally conscious actions that can be carried out at night for a night owl user. This makes it possible to make feasible suggestions based on the user's lifestyle patterns and schedule.

[0039] The action suggestion unit can increase the user's willingness to participate by gamifying the action suggestions. For example, the generation AI of the action suggestion unit gamifies the action suggestions, increasing the user's willingness to participate. For example, a system is introduced that allows points and badges to be earned by completing the action suggestions. The action suggestion unit also incorporates game elements to provide enjoyment when the user executes the action suggestions. For example, the action suggestions may be presented in the form of a mission, giving the user a sense of accomplishment. The action suggestion unit also gamifies the action suggestions by the generation AI, encouraging competition and cooperation between users. For example, a system is introduced that allows special rewards to be earned by completing the action suggestions together with a friend. In this way, the gamification can increase the user's willingness to participate.

[0040] The action suggestion unit can add a function to compare action proposals between different users and promote competition and cooperation. For example, the action suggestion unit adds a function to compare action proposals between users with different generation AIs and promote competition. For example, the achievement level of action proposals is displayed in a ranking format, and rewards are provided to top users. The action suggestion unit also adds a function to enable users to cooperate with each other to execute action proposals. For example, a system is introduced that allows users to earn special rewards by teaming up to complete action proposals. The action suggestion unit also adds a function to compare action proposals between users with different generation AIs and promote competition and cooperation. For example, by executing action proposals together with a friend, users can encourage each other and share a sense of accomplishment. This can promote competition and cooperation between users.

[0041] The motivation improvement unit can periodically provide feedback on achieved goals and progress based on the user's behavioral history. In the motivation improvement unit, for example, the generation AI analyzes the user's behavioral history and periodically provides feedback on achieved goals and progress. For example, a weekly progress report is provided to visualize the user's efforts. In addition, the motivation improvement unit periodically provides feedback on achieved goals and progress by the generation AI. For example, the monthly emission reduction amount is displayed in a graph to highlight the user's achievements. In addition, the motivation improvement unit periodically provides feedback on achieved goals and progress by the generation AI based on the user's behavioral history. For example, a message of praise is displayed when a specific goal is achieved. In this way, regular feedback on the user's achieved goals and progress can be improved.

[0042] The motivation improvement unit can provide encouraging messages and rewards to the user to improve motivation. For example, the generation AI provides encouraging messages for the user's actions to improve motivation. For example, after reporting an action, the motivation improvement unit displays a message such as "Great! Keep it up!". The motivation improvement unit also provides rewards when the user achieves a specific goal. For example, a system that allows users to earn digital badges or points is introduced. The motivation improvement unit also provides encouraging messages and rewards for the user's actions to improve motivation. For example, a special title is awarded if the user reports actions consecutively. In this way, encouraging messages and rewards can be provided to the user to improve motivation.

[0043] The motivation improvement unit can provide a mechanism for collaborating with the user's friends and family to jointly achieve a goal. For example, the motivation improvement unit provides a mechanism for the generation AI to collaborating with the user's friends and family to jointly achieve a goal. For example, the whole family may report their actions and set a joint goal. The motivation improvement unit also improves the user's motivation by having the user carry out suggested actions together with friends. For example, a mechanism may be introduced for completing suggested actions while competing with friends. The motivation improvement unit also provides a mechanism for the generation AI to collaborating with the user's friends and family to jointly achieve a goal. For example, the users may report their actions together with friends and achieve their goals by encouraging each other. In this way, the user's motivation can be improved by collaborating with the user's friends and family to jointly achieve a goal.

[0044] The motivation improvement unit can provide a combination of different motivation improvement techniques. For example, the generation AI of the motivation improvement unit provides a combination of different motivation improvement techniques. For example, playing the user's favorite music after the behavior report. The motivation improvement unit also provides visual content to improve the user's motivation. For example, displaying a beautiful landscape image after the behavior report. The motivation improvement unit also provides a combination of different motivation improvement techniques of the generation AI. For example, providing music and visual content simultaneously after the behavior report. In this way, the user's motivation can be improved by providing a combination of different motivation improvement techniques.

[0045] The community formation unit can match users with common interests and goals based on user behavior data. In this unit, for example, the generation AI analyzes user behavior data and matches users with common interests and goals. For example, it can connect users who are engaged in recycling activities in the same area. In addition, the community formation unit can match users with common interests and goals based on user behavior data. For example, it can connect users who commute by bicycle. In addition, the community formation unit can analyze user behavior data and match users with common interests and goals. For example, it can connect users who use eco-bags. This can promote the formation of communities by matching users with common interests and goals.

[0046] The community formation unit monitors activity within the community and can provide rewards and praise to active users. For example, the generation AI in the community formation unit monitors activity within the community and provides rewards to active users. For example, a digital badge is awarded to the user who reports the most activities. The community formation unit also monitors activity within the community and provides praise to active users. For example, the top user of the month is announced and a message of praise is displayed. The community formation unit also monitors activity within the community and provides rewards and praise to active users. For example, a special title is awarded to users who achieve a specific goal. In this way, by monitoring activity within the community and providing rewards and praise to active users, it is possible to promote the vitality of the community.

[0047] The community formation unit can host activities within the community as virtual events or workshops, promoting interaction between users. For example, the generation AI can host activities within the community as virtual events, promoting interaction between users. For example, an online recycling workshop can be held. The community formation unit can also host activities within the community in the form of a workshop, promoting interaction between users. For example, an online workshop on making eco-bags can be held. The community formation unit can also host activities within the community as virtual events, promoting interaction between users. For example, an online discussion event on environmental protection can be held. This can promote interaction between users through virtual events or workshops.

[0048] The community formation unit can link different communities together to form a larger network. For example, the community formation unit links communities with different generation AIs to form a larger network. For example, it can integrate regional communities to build a nationwide network. The community formation unit also links different communities together to connect users with common goals. For example, it can link communities that are engaged in recycling activities. The community formation unit also links communities with different generation AIs to form a larger network. For example, it can connect communities in different regions to promote information sharing and cooperation. In this way, a larger network can be formed by linking different communities together.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The behavior reporting unit reports the user's behavior. For example, a user can report, "I commuted by bicycle today." The behavior reporting unit also uses the generation AI to analyze the user's behavior and organize the report content. For example, the generation AI analyzes the user's report using natural language processing technology and extracts details of the behavior. The emission reduction calculation unit calculates the emission reduction amount based on the behavior reported by the behavior reporting unit. For example, it calculates the emission reduction amount if commuting by bicycle. The emission reduction calculation unit also evaluates the environmental impact of the reported behavior and calculates the specific reduction amount. For example, the generation AI calculates the reduction in CO2 emissions due to a change in transportation method. The visualization unit visualizes the emission reduction amount calculated by the emission reduction calculation unit. For example, it displays it as a graph or numerical value. The visualization unit also displays the calculation results visually in an easy-to-understand manner. For example, the generation AI displays the reduction amount as a pie chart or bar graph. The action suggestion unit suggests further actions based on the results visualized by the visualization unit. For example, it suggests, "Try using public transportation next time." In addition, the action suggestion unit uses the generation AI to suggest new actions based on the user's behavioral data. For example, the generation AI analyzes the user's past behavioral history and suggests optimal actions. This allows the net-zero contribution system according to the embodiment to specifically grasp the impact that an individual's behavior has on the environment and encourage further action.

[0051] The behavior reporting unit can refer to the user's past behavior history and check the consistency of the reported content. For example, when a user reports an action, the generation AI refers to the past behavior history and checks whether the reported content is consistent. For example, it checks whether there are any inconsistencies with the action reported the previous day. The behavior reporting unit also analyzes the user's past behavior history and provides feedback on the reported content. For example, it evaluates whether there has been progress compared with past actions. Furthermore, when a user reports an action, the generation AI checks for consistency based on the past behavior history and prompts corrections if there are any inconsistencies. For example, it checks whether the same action has been reported multiple times. This makes it possible to maintain consistency in the reported content.

[0052] The behavior reporting unit enables behavior reports to be made using not only voice input but also images or videos, and can also analyze visual information. For example, the behavior reporting unit allows users to upload images and videos in addition to voice input when reporting an activity. For example, a video of the recycling process may be included in the report. In addition, the behavior reporting unit uses a generation AI to analyze the uploaded images and videos and reflect them in the report. For example, image recognition technology may be used to identify the types of items recycled. In addition, when a user reports an activity, the generation AI combines and analyzes the voice input with images and videos to generate more detailed report content. For example, a video of a bicycle commute may be integrated with an audio description. This enables detailed reports that also include visual information.

[0053] The behavior reporting unit can integrate behavior reports from different devices to provide a seamless user experience. For example, the behavior reporting unit allows users to report behavior from different devices, such as smartphones, smartwatches, and smart speakers, and the generation AI integrates the data. For example, it links exercise data from a smartwatch with behavior reports from a smartphone. The behavior reporting unit also seamlessly integrates behavior reports from different devices to provide consistent feedback to the user. For example, it integrates audio reports from a smart speaker with image reports from a smartphone. When a user reports behavior from different devices, the generation AI synchronizes data between devices in real time to provide a seamless user experience. For example, it automatically updates the exercise data from a smartwatch in a smartphone app. This allows behavior reports from different devices to be integrated to provide a seamless user experience.

[0054] The emission reduction calculation unit can compare the emission reduction amounts for each region based on the user's behavioral data and promote competition between regions. In the emission reduction calculation unit, for example, the generation AI calculates and compares the emission reduction amounts for each region based on the user's behavioral data. For example, the emission reduction amount for each region is displayed in a graph to promote competition between regions. The emission reduction calculation unit also analyzes the user's behavioral data and displays the emission reduction amount for each region in a ranking format. For example, special titles and rewards are provided to top-ranking regions. In addition, the emission reduction calculation unit updates the emission reduction amount for each region in real time using the generation AI to provide the user with the latest information. For example, the fluctuations in emission reduction amount for each region are displayed in a graph to promote competition. This can promote competition between regions and raise awareness of emission reductions.

[0055] The emission reduction calculation unit takes into account external data such as weather or traffic conditions when calculating the emission reduction amount, allowing for more accurate calculations. For example, the emission reduction calculation unit takes weather data into account when the generation AI calculates the emission reduction amount. For example, the emission reduction amount of commuting by bicycle on rainy days is adjusted compared to normal times. The emission reduction calculation unit also calculates the emission reduction amount based on traffic condition data. For example, the emission reduction effect of using public transportation during traffic congestion is adjusted compared to normal times. The emission reduction calculation unit also collects external data (weather, traffic conditions, etc.) in real time and reflects this in the calculation of the emission reduction amount. For example, the emission reduction amount is dynamically adjusted according to changes in weather or traffic conditions. In this way, by taking external data into account, more accurate calculation of the emission reduction amount is possible.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The behavior reporting unit reports the user's behavior. For example, a user can report, "I commuted to work by bicycle today." The behavior reporting unit also uses a generation AI to analyze the user's behavior and organize the report content. For example, the generation AI analyzes the user's report using natural language processing technology and extracts details of the behavior. Step 2: The emission reduction calculation unit calculates the emission reduction amount based on the behavior reported by the behavior reporting unit. For example, it calculates the emission reduction amount if commuting by bicycle. In addition, the generation AI in the emission reduction calculation unit evaluates the environmental impact of the reported behavior and calculates the specific reduction amount. For example, the generation AI calculates the reduction in CO2 emissions due to a change in transportation mode. Step 3: The visualization unit visualizes the emission reductions calculated by the emission reduction calculation unit. For example, it displays them as graphs or numerical values. The visualization unit also allows the generation AI to display the calculation results in a visually easy-to-understand manner. For example, the generation AI displays the reductions as a pie chart or bar graph. Step 4: The action suggestion unit suggests further actions based on the results visualized by the visualization unit. For example, it might suggest, "Try using public transportation next time." The action suggestion unit also uses the generation AI to suggest new actions based on the user's behavioral data. For example, the generation AI might analyze the user's past behavioral history and suggest optimal actions.

[0058] (Example 2) The Net Zero Contribution System according to an embodiment of the present invention is a system that encourages individuals to take actions to contribute to "Net Zero" and enables them to realize the effects of their actions. In this Net Zero Contribution System, a generative AI reports individuals' actions, calculates and visualizes the amount of emissions reductions resulting from those actions, and suggests further actions. In this way, the Net Zero Contribution System can raise individuals' awareness of contributing to environmental issues and contribute to the realization of a sustainable society.

[0059] A net-zero contribution system according to an embodiment includes an action reporting unit, an emission reduction calculation unit, a visualization unit, and an action suggestion unit. The action reporting unit reports a user's action. For example, a user can report, "Today, I commuted by bicycle." Furthermore, the action reporting unit uses a generation AI to analyze the user's action and organize the report content. For example, the generation AI analyzes the user's report using natural language processing technology and extracts details of the action. The emission reduction calculation unit calculates the emission reduction amount based on the action reported by the action reporting unit. For example, it calculates the emission reduction amount if commuting by bicycle. Furthermore, the emission reduction calculation unit evaluates the environmental impact of the reported action and calculates the specific reduction amount. For example, the generation AI calculates the reduction in CO2 emissions resulting from a change in transportation mode. The visualization unit visualizes the emission reduction amount calculated by the emission reduction calculation unit. For example, it displays it as a graph or numerical values. Furthermore, the visualization unit displays the calculation results of the generation AI in a visually easy-to-understand manner. For example, the generation AI displays the reduction amount as a pie chart or bar graph. The action suggestion unit suggests further actions based on the results visualized by the visualization unit. For example, it suggests, "Try using public transportation next time." The action suggestion unit also uses the generation AI to suggest new actions based on the user's behavioral data. For example, the generation AI analyzes the user's past behavioral history and suggests optimal actions. In this way, the net-zero contribution system according to the embodiment can specifically grasp the impact that an individual's behavior has on the environment and encourage further actions.

[0060] The behavior reporting unit can analyze the user's tone of voice or facial expression, estimate their emotional state, and reflect this in the report content. For example, when a user reports their behavior, the generation AI analyzes their voice input and estimates their emotional state from the tone and speed of their voice. For example, if the user is excited, this emotion is reflected in the report content. When a user reports their behavior through a camera, the generation AI analyzes their facial expression and estimates their emotional state. For example, emotions are read from facial expressions such as smiling or furrowing the brow, and this is reflected in the report content. When a user reports their behavior, the generation AI analyzes both their voice and facial expression to comprehensively determine their emotional state. For example, if the voice tone is calm but the facial expression is tense, this emotion is reflected in the report content. This enables reports that take the user's emotional state into consideration.

[0061] The behavior reporting unit can refer to the user's past behavior history and check the consistency of the reported content. For example, when a user reports an action, the generation AI refers to the past behavior history and checks whether the reported content is consistent. For example, it checks whether there are any inconsistencies with the action reported the previous day. The behavior reporting unit also analyzes the user's past behavior history and provides feedback on the reported content. For example, it evaluates whether there has been progress compared with past actions. Furthermore, when a user reports an action, the generation AI checks for consistency based on the past behavior history and prompts corrections if there are any inconsistencies. For example, it checks whether the same action has been reported multiple times. This makes it possible to maintain consistency in the reported content.

[0062] The behavior reporting unit can use the emotion estimation function to analyze the emotions of the user when reporting and provide feedback to elicit positive emotions. For example, when a user reports an action, the behavior reporting unit uses the emotion estimation function to analyze the emotions of the generation AI and provide feedback to elicit positive emotions. For example, if the user is feeling down, an encouraging message is displayed. The behavior reporting unit also analyzes the user's emotional state in real time and provides specific advice to elicit positive emotions. For example, feedback that reminds the user of successful experiences is provided. The behavior reporting unit also analyzes the emotions of the user when reporting an action using the emotion estimation function and provides interactive feedback to elicit positive emotions. For example, music or images that correspond to the user's emotions are displayed. This makes it possible to elicit positive emotions from the user.

[0063] The behavior reporting unit enables behavior reports to be made using not only voice input but also images or videos, and can also analyze visual information. For example, the behavior reporting unit allows users to upload images and videos in addition to voice input when reporting an activity. For example, a video of the recycling process may be included in the report. In addition, the behavior reporting unit uses a generation AI to analyze the uploaded images and videos and reflect them in the report. For example, image recognition technology may be used to identify the types of items recycled. In addition, when a user reports an activity, the generation AI combines and analyzes the voice input with images and videos to generate more detailed report content. For example, a video of a bicycle commute may be integrated with an audio description. This enables detailed reports that also include visual information.

[0064] The behavior reporting unit can integrate behavior reports from different devices to provide a seamless user experience. For example, the behavior reporting unit allows users to report behavior from different devices, such as smartphones, smartwatches, and smart speakers, and the generation AI integrates the data. For example, it links exercise data from a smartwatch with behavior reports from a smartphone. The behavior reporting unit also seamlessly integrates behavior reports from different devices to provide consistent feedback to the user. For example, it integrates audio reports from a smart speaker with image reports from a smartphone. When a user reports behavior from different devices, the generation AI synchronizes data between devices in real time to provide a seamless user experience. For example, it automatically updates the exercise data from a smartwatch in a smartphone app. This allows behavior reports from different devices to be integrated to provide a seamless user experience.

[0065] The behavior reporting unit uses the emotion estimation function to analyze the emotions of the user when reporting in real time, and can provide emotional support according to the content of the report. For example, when a user reports an action, the generation AI in the behavior reporting unit uses the emotion estimation function to analyze the emotions in real time, and can provide emotional support according to the content of the report. For example, if the user is tired, the generation AI provides advice to relax. The behavior reporting unit also analyzes the user's emotional state in real time, and can provide emotional support according to the content of the report. For example, if the user feels a sense of accomplishment, the generation AI displays a message of praise. The behavior reporting unit also analyzes the emotions of the user when reporting an action, and can provide emotional support according to the content of the report. For example, if the user is feeling anxious, the generation AI displays a message of reassurance. This makes it possible to provide support according to the user's emotions.

[0066] The emission reduction calculation unit can compare the emission reduction amounts for each region based on the user's behavioral data and promote competition between regions. In the emission reduction calculation unit, for example, the generation AI calculates and compares the emission reduction amounts for each region based on the user's behavioral data. For example, the emission reduction amount for each region is displayed in a graph to promote competition between regions. The emission reduction calculation unit also analyzes the user's behavioral data and displays the emission reduction amount for each region in a ranking format. For example, special titles and rewards are provided to top-ranking regions. In addition, the emission reduction calculation unit updates the emission reduction amount for each region in real time using the generation AI to provide the user with the latest information. For example, the fluctuations in emission reduction amount for each region are displayed in a graph to promote competition. This can promote competition between regions and raise awareness of emission reductions.

[0067] The emission reduction calculation unit takes into account external data such as weather or traffic conditions when calculating the emission reduction amount, allowing for more accurate calculations. For example, the emission reduction calculation unit takes weather data into account when the generation AI calculates the emission reduction amount. For example, the emission reduction amount of commuting by bicycle on rainy days is adjusted compared to normal times. The emission reduction calculation unit also calculates the emission reduction amount based on traffic condition data. For example, the emission reduction effect of using public transportation during traffic congestion is adjusted compared to normal times. The emission reduction calculation unit also collects external data (weather, traffic conditions, etc.) in real time and reflects this in the calculation of the emission reduction amount. For example, the emission reduction amount is dynamically adjusted according to changes in weather or traffic conditions. In this way, by taking external data into account, more accurate calculation of the emission reduction amount is possible.

[0068] The emission reduction calculation unit can use the emotion estimation function to analyze the emotions of the user when they view the emission reduction results and provide feedback to elicit positive emotions. For example, when the generation AI displays the emission reduction results, the emission reduction calculation unit can use the emotion estimation function to analyze the user's emotions and provide feedback to elicit positive emotions. For example, it can display a message that makes the user feel happy. The emission reduction calculation unit can also analyze the emotions of the user when they view the emission reduction results in real time and provide specific advice to elicit positive emotions. For example, it can provide feedback that makes the user feel a sense of accomplishment. The emission reduction calculation unit can also use the emotion estimation function to analyze the emotions of the user when they view the emission reduction results and provide interactive feedback to elicit positive emotions. For example, it can display music or images that correspond to the user's emotions. In this way, a summary that captures emotional nuances can be generated, allowing emotional factors to be reflected in the evaluation.

[0069] The emission reduction calculation unit can visualize the emission reduction amount using augmented reality (AR) technology, allowing the user to visually experience the reduction effect in an actual environment. In the emission reduction calculation unit, for example, the generation AI visualizes the emission reduction amount using augmented reality (AR) technology, allowing the user to visually experience the reduction effect in an actual environment. For example, the emission reduction effect is displayed through a smartphone camera. The emission reduction calculation unit also allows the user to experience the visualization of the emission reduction amount using an AR device. For example, the emission reduction effect is displayed in real time through AR glasses. In the emission reduction calculation unit, the generation AI also uses AR technology to superimpose the emission reduction effect on the user's surrounding environment. For example, the energy consumption reduction effect at home is visualized using AR. This allows the user to visually experience the reduction effect in an actual environment.

[0070] The emission reduction calculation unit can link with different data sources to perform a more multifaceted calculation of emission reductions. For example, the generation AI in the emission reduction calculation unit links with smart meter and energy consumption data to more accurately calculate emission reductions. For example, it calculates the emission reduction effect based on household energy consumption data. The emission reduction calculation unit also integrates different data sources (smart meter, energy consumption data, etc.), and the generation AI calculates a multifaceted emission reduction amount. For example, it combines multiple data sources to evaluate the overall emission reduction effect. The emission reduction calculation unit also uses the generation AI to collect data from different data sources in real time and reflect this in the calculation of emission reductions. For example, it obtains smart meter data in real time and dynamically calculates the emission reduction effect. This makes it possible to calculate emission reductions more multifaceted by linking with different data sources.

[0071] The emission reduction calculation unit uses the emotion estimation function to analyze the user's emotions in real time when they view the emission reduction results, and can provide emotional support according to the results. For example, when the generation AI displays the emission reduction results, the emission reduction calculation unit uses the emotion estimation function to analyze the user's emotions in real time and provide emotional support according to the results. For example, if the user is disappointed, an encouraging message is displayed. The emission reduction calculation unit also analyzes the user's emotions in real time when they view the emission reduction results, and provides specific advice to elicit positive emotions. For example, feedback that makes the user feel a sense of accomplishment. The emission reduction calculation unit also uses the emotion estimation function to analyze the user's emotions in real time when they view the emission reduction results, and can provide emotional support according to the results. For example, if the user is feeling anxious, a message that gives them a sense of security is displayed. This makes it possible to provide support according to the user's emotions.

[0072] The action suggestion unit can make individually customized action suggestions based on the user's past behavioral data. In the action suggestion unit, for example, the generation AI analyzes the user's past behavioral data and makes individually customized action suggestions. For example, a user who has commuted by bicycle in the past is recommended to commute by bicycle again next time. In addition, the action suggestion unit makes individually customized action suggestions based on the user's behavior history. For example, a user who frequently recycles is suggested a new recycling method. In addition, the action suggestion unit makes individually customized action suggestions based on the user's past behavioral data. For example, a user who uses eco-bags is suggested an even more environmentally friendly action. In this way, it is possible to make individually customized action suggestions for the user.

[0073] The action suggestion unit can make feasible suggestions by taking into account the user's lifestyle patterns and schedule. In the action suggestion unit, for example, the generation AI analyzes the user's lifestyle patterns and schedule and makes feasible action suggestions. For example, suggestions are made taking into account commuting time and how people spend their holidays. The action suggestion unit also makes feasible action suggestions by the generation AI based on the user's schedule data. For example, it suggests environmentally conscious actions that can be easily carried out on busy days. The action suggestion unit also analyzes the user's lifestyle patterns and makes feasible action suggestions. For example, it suggests environmentally conscious actions that can be carried out at night for a night owl user. This makes it possible to make feasible suggestions based on the user's lifestyle patterns and schedule.

[0074] The action suggestion unit can use the emotion estimation function to analyze the emotions of the user when receiving a suggestion and provide feedback to elicit positive emotions. For example, when the generation AI makes an action suggestion, the action suggestion unit uses the emotion estimation function to analyze the user's emotions and provide feedback to elicit positive emotions. For example, if the user feels anxious about the suggestion, a message that gives a sense of security is displayed. The action suggestion unit also analyzes the emotions of the user when receiving an action suggestion in real time and provides specific advice to elicit positive emotions. For example, feedback that makes the user feel excited about the suggestion. The action suggestion unit also uses the emotion estimation function to analyze the emotions of the user when receiving an action suggestion in real time and provides interactive feedback to elicit positive emotions. For example, music or images that correspond to the user's emotions are displayed. This makes it possible to elicit positive emotions from the user.

[0075] The action suggestion unit can increase the user's willingness to participate by gamifying the action suggestions. For example, the generation AI of the action suggestion unit gamifies the action suggestions, increasing the user's willingness to participate. For example, a system is introduced that allows points and badges to be earned by completing the action suggestions. The action suggestion unit also incorporates game elements to provide enjoyment when the user executes the action suggestions. For example, the action suggestions may be presented in the form of a mission, giving the user a sense of accomplishment. The action suggestion unit also gamifies the action suggestions by the generation AI, encouraging competition and cooperation between users. For example, a system is introduced that allows special rewards to be earned by completing the action suggestions together with a friend. In this way, the gamification can increase the user's willingness to participate.

[0076] The action suggestion unit can add a function to compare action proposals between different users and promote competition and cooperation. For example, the action suggestion unit adds a function to compare action proposals between users with different generation AIs and promote competition. For example, the achievement level of action proposals is displayed in a ranking format, and rewards are provided to top users. The action suggestion unit also adds a function to enable users to cooperate with each other to execute action proposals. For example, a system is introduced that allows users to earn special rewards by teaming up to complete action proposals. The action suggestion unit also adds a function to compare action proposals between users with different generation AIs and promote competition and cooperation. For example, by executing action proposals together with a friend, users can encourage each other and share a sense of accomplishment. This can promote competition and cooperation between users.

[0077] The action suggestion unit uses the emotion estimation function to analyze the user's emotions in real time when receiving a suggestion, and can provide emotional support according to the content of the suggestion. For example, when the generation AI makes an action suggestion, the action suggestion unit uses the emotion estimation function to analyze the user's emotions in real time, and can provide emotional support according to the content of the suggestion. For example, if the user feels anxious about the suggestion, a message that gives a sense of security is displayed. The action suggestion unit also analyzes the user's emotions in real time when receiving an action suggestion, and provides specific advice to elicit positive emotions. For example, feedback that makes the user feel excited about the suggestion. The action suggestion unit also uses the emotion estimation function to analyze the user's emotions in real time when receiving an action suggestion, and can provide emotional support according to the content of the suggestion. For example, if the user has doubts about the suggestion, a specific explanation is provided. This makes it possible to provide support according to the user's emotions.

[0078] The motivation improvement unit can periodically provide feedback on achieved goals and progress based on the user's behavioral history. In the motivation improvement unit, for example, the generation AI analyzes the user's behavioral history and periodically provides feedback on achieved goals and progress. For example, a weekly progress report is provided to visualize the user's efforts. In addition, the motivation improvement unit periodically provides feedback on achieved goals and progress by the generation AI. For example, the monthly emission reduction amount is displayed in a graph to highlight the user's achievements. In addition, the motivation improvement unit periodically provides feedback on achieved goals and progress by the generation AI based on the user's behavioral history. For example, a message of praise is displayed when a specific goal is achieved. In this way, regular feedback on the user's achieved goals and progress can be improved.

[0079] The motivation improvement unit can provide encouraging messages and rewards to the user to improve motivation. For example, the generation AI provides encouraging messages for the user's actions to improve motivation. For example, after reporting an action, the motivation improvement unit displays a message such as "Great! Keep it up!". The motivation improvement unit also provides rewards when the user achieves a specific goal. For example, a system that allows users to earn digital badges or points is introduced. The motivation improvement unit also provides encouraging messages and rewards for the user's actions to improve motivation. For example, a special title is awarded if the user reports actions consecutively. In this way, encouraging messages and rewards can be provided to the user to improve motivation.

[0080] The motivation improvement unit can use the emotion estimation function to analyze the user's motivation level and provide appropriate support when it drops. For example, the generation AI in the motivation improvement unit uses the emotion estimation function to analyze the user's motivation level and provide appropriate support when it drops. For example, if the user is feeling down, it displays an encouraging message. The motivation improvement unit also analyzes the user's motivation level in real time and provides specific support when it drops. For example, it suggests activities to increase motivation. The motivation improvement unit also analyzes the user's motivation level using the emotion estimation function to provide appropriate support when it drops. For example, if the user is tired, it gives advice on how to relax. In this way, the user's motivation can be maintained by analyzing the user's motivation level and providing appropriate support when it drops.

[0081] The motivation improvement unit can provide a mechanism for collaborating with the user's friends and family to jointly achieve a goal. For example, the motivation improvement unit provides a mechanism for the generation AI to collaborating with the user's friends and family to jointly achieve a goal. For example, the whole family may report their actions and set a joint goal. The motivation improvement unit also improves the user's motivation by having the user carry out suggested actions together with friends. For example, a mechanism may be introduced for completing suggested actions while competing with friends. The motivation improvement unit also provides a mechanism for the generation AI to collaborating with the user's friends and family to jointly achieve a goal. For example, the users may report their actions together with friends and achieve their goals by encouraging each other. In this way, the user's motivation can be improved by collaborating with the user's friends and family to jointly achieve a goal.

[0082] The motivation improvement unit can provide a combination of different motivation improvement techniques. For example, the generation AI of the motivation improvement unit provides a combination of different motivation improvement techniques. For example, playing the user's favorite music after the behavior report. The motivation improvement unit also provides visual content to improve the user's motivation. For example, displaying a beautiful landscape image after the behavior report. The motivation improvement unit also provides a combination of different motivation improvement techniques of the generation AI. For example, providing music and visual content simultaneously after the behavior report. In this way, the user's motivation can be improved by providing a combination of different motivation improvement techniques.

[0083] The motivation improvement unit can use the emotion estimation function to analyze the user's motivation level in real time and provide support at the appropriate time. For example, the generation AI in the motivation improvement unit uses the emotion estimation function to analyze the user's motivation level in real time and provide support at the appropriate time. For example, if the user is losing motivation, an encouraging message is displayed. The motivation improvement unit also analyzes the user's motivation level in real time and provides specific support at the appropriate time. For example, it suggests activities to increase motivation. The motivation improvement unit also analyzes the user's motivation level in real time using the emotion estimation function to provide support at the appropriate time. For example, if the user is tired, it gives advice on how to relax. In this way, the user's motivation level can be analyzed in real time and support can be provided at the appropriate time, thereby maintaining motivation.

[0084] The community formation unit can match users with common interests and goals based on user behavior data. In this unit, for example, the generation AI analyzes user behavior data and matches users with common interests and goals. For example, it can connect users who are engaged in recycling activities in the same area. In addition, the community formation unit can match users with common interests and goals based on user behavior data. For example, it can connect users who commute by bicycle. In addition, the community formation unit can analyze user behavior data and match users with common interests and goals. For example, it can connect users who use eco-bags. This can promote the formation of communities by matching users with common interests and goals.

[0085] The community formation unit monitors activity within the community and can provide rewards and praise to active users. For example, the generation AI in the community formation unit monitors activity within the community and provides rewards to active users. For example, a digital badge is awarded to the user who reports the most activities. The community formation unit also monitors activity within the community and provides praise to active users. For example, the top user of the month is announced and a message of praise is displayed. The community formation unit also monitors activity within the community and provides rewards and praise to active users. For example, a special title is awarded to users who achieve a specific goal. In this way, by monitoring activity within the community and providing rewards and praise to active users, it is possible to promote the vitality of the community.

[0086] The community formation unit can use the emotion estimation function to analyze the emotional state of users in the community and provide feedback to elicit positive emotions. For example, the generation AI in the community formation unit uses the emotion estimation function to analyze the emotional state of users in the community and provide feedback to elicit positive emotions. For example, if a user is feeling down, the community formation unit can display an encouraging message. The community formation unit can also analyze the emotional state of users in the community in real time and provide specific advice to elicit positive emotions. For example, feedback that creates a sense of accomplishment. The community formation unit can also use the emotion estimation function to analyze the emotional state of users in the community and provide interactive feedback to elicit positive emotions. For example, music or images can be displayed that correspond to the user's emotions. This allows the community formation unit to promote community revitalization by analyzing the emotional state of users in the community and providing feedback to elicit positive emotions.

[0087] The community formation unit can host activities within the community as virtual events or workshops, promoting interaction between users. For example, the generation AI can host activities within the community as virtual events, promoting interaction between users. For example, an online recycling workshop can be held. The community formation unit can also host activities within the community in the form of a workshop, promoting interaction between users. For example, an online workshop on making eco-bags can be held. The community formation unit can also host activities within the community as virtual events, promoting interaction between users. For example, an online discussion event on environmental protection can be held. This can promote interaction between users through virtual events or workshops.

[0088] The community formation unit can link different communities together to form a larger network. For example, the community formation unit links communities with different generation AIs to form a larger network. For example, it can integrate regional communities to build a nationwide network. The community formation unit also links different communities together to connect users with common goals. For example, it can link communities that are engaged in recycling activities. The community formation unit also links communities with different generation AIs to form a larger network. For example, it can connect communities in different regions to promote information sharing and cooperation. In this way, a larger network can be formed by linking different communities together.

[0089] The community formation unit can use the emotion estimation function to analyze the emotional state of users in the community in real time and provide appropriate support. For example, the generation AI in the community formation unit uses the emotion estimation function to analyze the emotional state of users in the community in real time and provide appropriate support. For example, if a user is feeling down, an encouraging message is displayed. The community formation unit also analyzes the emotional state of users in the community in real time and provides appropriate support. For example, feedback that makes the user feel a sense of accomplishment. The community formation unit also analyzes the emotional state of users in the community in real time using the emotion estimation function to provide appropriate support. For example, if a user is feeling anxious, a message that gives a sense of security is displayed. In this way, the emotional state of users in the community can be analyzed in real time and appropriate support can be provided, thereby promoting the revitalization of the community.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The behavior reporting unit reports the user's behavior. For example, a user can report, "I commuted by bicycle today." The behavior reporting unit also uses the generation AI to analyze the user's behavior and organize the report content. For example, the generation AI analyzes the user's report using natural language processing technology and extracts details of the behavior. The emission reduction calculation unit calculates the emission reduction amount based on the behavior reported by the behavior reporting unit. For example, it calculates the emission reduction amount if commuting by bicycle. The emission reduction calculation unit also evaluates the environmental impact of the reported behavior and calculates the specific reduction amount. For example, the generation AI calculates the reduction in CO2 emissions due to a change in transportation method. The visualization unit visualizes the emission reduction amount calculated by the emission reduction calculation unit. For example, it displays it as a graph or numerical value. The visualization unit also displays the calculation results visually in an easy-to-understand manner. For example, the generation AI displays the reduction amount as a pie chart or bar graph. The action suggestion unit suggests further actions based on the results visualized by the visualization unit. For example, it suggests, "Try using public transportation next time." In addition, the action suggestion unit uses the generation AI to suggest new actions based on the user's behavioral data. For example, the generation AI analyzes the user's past behavioral history and suggests optimal actions. This allows the net-zero contribution system according to the embodiment to specifically grasp the impact that an individual's behavior has on the environment and encourage further action.

[0092] The behavior reporting unit can analyze the user's tone of voice or facial expression, estimate their emotional state, and reflect this in the report content. For example, when a user reports their behavior, the generation AI analyzes their voice input and estimates their emotional state from the tone and speed of their voice. For example, if the user is excited, this emotion is reflected in the report content. When a user reports their behavior through a camera, the generation AI analyzes their facial expression and estimates their emotional state. For example, emotions are read from facial expressions such as smiling or furrowing the brow, and this is reflected in the report content. When a user reports their behavior, the generation AI analyzes both their voice and facial expression to comprehensively determine their emotional state. For example, if the voice tone is calm but the facial expression is tense, this emotion is reflected in the report content. This enables reports that take the user's emotional state into consideration.

[0093] The behavior reporting unit can refer to the user's past behavior history and check the consistency of the reported content. For example, when a user reports an action, the generation AI refers to the past behavior history and checks whether the reported content is consistent. For example, it checks whether there are any inconsistencies with the action reported the previous day. The behavior reporting unit also analyzes the user's past behavior history and provides feedback on the reported content. For example, it evaluates whether there has been progress compared with past actions. Furthermore, when a user reports an action, the generation AI checks for consistency based on the past behavior history and prompts corrections if there are any inconsistencies. For example, it checks whether the same action has been reported multiple times. This makes it possible to maintain consistency in the reported content.

[0094] The behavior reporting unit can use the emotion estimation function to analyze the emotions of the user when reporting and provide feedback to elicit positive emotions. For example, when a user reports an action, the behavior reporting unit uses the emotion estimation function to analyze the emotions of the generation AI and provide feedback to elicit positive emotions. For example, if the user is feeling down, an encouraging message is displayed. The behavior reporting unit also analyzes the user's emotional state in real time and provides specific advice to elicit positive emotions. For example, feedback that reminds the user of successful experiences is provided. The behavior reporting unit also analyzes the emotions of the user when reporting an action using the emotion estimation function and provides interactive feedback to elicit positive emotions. For example, music or images that correspond to the user's emotions are displayed. This makes it possible to elicit positive emotions from the user.

[0095] The behavior reporting unit enables behavior reports to be made using not only voice input but also images or videos, and can also analyze visual information. For example, the behavior reporting unit allows users to upload images and videos in addition to voice input when reporting an activity. For example, a video of the recycling process may be included in the report. In addition, the behavior reporting unit uses a generation AI to analyze the uploaded images and videos and reflect them in the report. For example, image recognition technology may be used to identify the types of items recycled. In addition, when a user reports an activity, the generation AI combines and analyzes the voice input with images and videos to generate more detailed report content. For example, a video of a bicycle commute may be integrated with an audio description. This enables detailed reports that also include visual information.

[0096] The behavior reporting unit can integrate behavior reports from different devices to provide a seamless user experience. For example, the behavior reporting unit allows users to report behavior from different devices, such as smartphones, smartwatches, and smart speakers, and the generation AI integrates the data. For example, it links exercise data from a smartwatch with behavior reports from a smartphone. The behavior reporting unit also seamlessly integrates behavior reports from different devices to provide consistent feedback to the user. For example, it integrates audio reports from a smart speaker with image reports from a smartphone. When a user reports behavior from different devices, the generation AI synchronizes data between devices in real time to provide a seamless user experience. For example, it automatically updates the exercise data from a smartwatch in a smartphone app. This allows behavior reports from different devices to be integrated to provide a seamless user experience.

[0097] The behavior reporting unit uses the emotion estimation function to analyze the emotions of the user when reporting in real time, and can provide emotional support according to the content of the report. For example, when a user reports an action, the generation AI in the behavior reporting unit uses the emotion estimation function to analyze the emotions in real time, and can provide emotional support according to the content of the report. For example, if the user is tired, the generation AI provides advice to relax. The behavior reporting unit also analyzes the user's emotional state in real time, and can provide emotional support according to the content of the report. For example, if the user feels a sense of accomplishment, the generation AI displays a message of praise. The behavior reporting unit also analyzes the emotions of the user when reporting an action, and can provide emotional support according to the content of the report. For example, if the user is feeling anxious, the generation AI displays a message of reassurance. This makes it possible to provide support according to the user's emotions.

[0098] The emission reduction calculation unit can compare the emission reduction amounts for each region based on the user's behavioral data and promote competition between regions. In the emission reduction calculation unit, for example, the generation AI calculates and compares the emission reduction amounts for each region based on the user's behavioral data. For example, the emission reduction amount for each region is displayed in a graph to promote competition between regions. The emission reduction calculation unit also analyzes the user's behavioral data and displays the emission reduction amount for each region in a ranking format. For example, special titles and rewards are provided to top-ranking regions. In addition, the emission reduction calculation unit updates the emission reduction amount for each region in real time using the generation AI to provide the user with the latest information. For example, the fluctuations in emission reduction amount for each region are displayed in a graph to promote competition. This can promote competition between regions and raise awareness of emission reductions.

[0099] The emission reduction calculation unit takes into account external data such as weather or traffic conditions when calculating the emission reduction amount, allowing for more accurate calculations. For example, the emission reduction calculation unit takes weather data into account when the generation AI calculates the emission reduction amount. For example, the emission reduction amount of commuting by bicycle on rainy days is adjusted compared to normal times. The emission reduction calculation unit also calculates the emission reduction amount based on traffic condition data. For example, the emission reduction effect of using public transportation during traffic congestion is adjusted compared to normal times. The emission reduction calculation unit also collects external data (weather, traffic conditions, etc.) in real time and reflects this in the calculation of the emission reduction amount. For example, the emission reduction amount is dynamically adjusted according to changes in weather or traffic conditions. In this way, by taking external data into account, more accurate calculation of the emission reduction amount is possible.

[0100] The emission reduction calculation unit can use the emotion estimation function to analyze the emotions of the user when they view the emission reduction results and provide feedback to elicit positive emotions. For example, when the generation AI displays the emission reduction results, the emission reduction calculation unit can use the emotion estimation function to analyze the user's emotions and provide feedback to elicit positive emotions. For example, it can display a message that makes the user feel happy. The emission reduction calculation unit can also analyze the emotions of the user when they view the emission reduction results in real time and provide specific advice to elicit positive emotions. For example, it can provide feedback that makes the user feel a sense of accomplishment. The emission reduction calculation unit can also use the emotion estimation function to analyze the emotions of the user when they view the emission reduction results and provide interactive feedback to elicit positive emotions. For example, it can display music or images that correspond to the user's emotions. In this way, a summary that captures emotional nuances can be generated, allowing emotional factors to be reflected in the evaluation.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The behavior reporting unit reports the user's behavior. For example, a user can report, "I commuted to work by bicycle today." The behavior reporting unit also uses a generation AI to analyze the user's behavior and organize the report content. For example, the generation AI analyzes the user's report using natural language processing technology and extracts details of the behavior. Step 2: The emission reduction calculation unit calculates the emission reduction amount based on the behavior reported by the behavior reporting unit. For example, it calculates the emission reduction amount if commuting by bicycle. In addition, the generation AI in the emission reduction calculation unit evaluates the environmental impact of the reported behavior and calculates the specific reduction amount. For example, the generation AI calculates the reduction in CO2 emissions due to a change in transportation mode. Step 3: The visualization unit visualizes the emission reductions calculated by the emission reduction calculation unit. For example, it displays them as graphs or numerical values. The visualization unit also allows the generation AI to display the calculation results in a visually easy-to-understand manner. For example, the generation AI displays the reductions as a pie chart or bar graph. Step 4: The action suggestion unit suggests further actions based on the results visualized by the visualization unit. For example, it might suggest, "Try using public transportation next time." The action suggestion unit also uses the generation AI to suggest new actions based on the user's behavioral data. For example, the generation AI might analyze the user's past behavioral history and suggest optimal actions.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 behavior reporting unit that reports user behavior; an emission reduction calculation unit that calculates an emission reduction amount based on the behavior reported by the behavior reporting unit; a visualization unit that visualizes the emission reduction amount calculated by the emission reduction amount calculation unit; an action suggestion unit that suggests further actions based on the results visualized by the visualization unit; A system characterized by:

2. The behavior reporting unit Analyzing the user's tone of voice or facial expression to estimate their emotional state and reflect it in the report The system of claim 1 .

3. The behavior reporting unit Check the consistency of the reported content by looking at the user's past behavior history. The system of claim 1 .

4. The behavior reporting unit Analyzing the emotions of the user when reporting and providing feedback to elicit positive emotions. The system of claim 1 .

5. The behavior reporting unit Behavior reporting can be done not only by voice input but also by using images or videos, and visual information can also be analyzed. The system of claim 1 .

6. The behavior reporting unit Integrate activity reports from different devices to provide a seamless user experience The system of claim 1 .

7. The behavior reporting unit Analyze the user's emotions in real time when reporting and provide emotional support according to the content of the report. The system of claim 1 .

Images