system

Abstract

The system according to this embodiment aims to enable users to visually understand the impact their eco-friendly behaviors will have on the future environment. [Solution] The system according to the embodiment comprises a recording unit, a prediction unit, and a display unit. The recording unit records eco-friendly behaviors. The prediction unit analyzes the eco-friendly behavior data recorded by the recording unit and predicts future environmental conditions. The display unit graphically displays the future environmental conditions predicted by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to specifically grasp the influence of a user's eco-friendly behavior on the future environment.

[0005] The system according to the embodiment aims to enable visual understanding of the influence of a user's eco-friendly behavior on the future environment.

Means for Solving the Problems

[0006] The system according to the embodiment includes a recording unit, a prediction unit, and a display unit. The recording unit records eco-friendly behaviors. The prediction unit analyzes the eco-friendly behavior data recorded by the recording unit and predicts future environmental states. The display unit graphically displays the future environmental states predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to visually understand the impact their eco-friendly behaviors will have on the future environment. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The Eco-Future Action AI System according to an embodiment of the present invention is a platform that records a user's eco-behavior, predicts future environmental conditions using a generative AI, and visually represents the results. When a user records their eco-behavior, this Eco-Future Action AI System generates a simulation of the future global environment from these actions and visually displays the specific impact of the user's actions on the environment. Furthermore, users can earn "eco-points" through their eco-behavior and participate in regularly held "eco-challenges" to earn special digital badges. Additionally, by connecting with other users aiming for the same eco-behavior in an "eco-community," all participants can experience a sense of accomplishment. This platform aims to provide motivation for users to continue eco-behavior and raise awareness of environmental protection. For example, if a user recycles, the system simulates and visually displays the impact of that action on future carbon dioxide emissions. Furthermore, users can earn "eco-points" through their eco-behavior and participate in regularly held "eco-challenges" to earn special digital badges. Additionally, by connecting with other users aiming for the same eco-behavior in an "eco-community," all participants can experience a sense of accomplishment. This platform aims to provide motivation for users to continue eco-behavior and raise awareness of environmental protection. This allows the Eco Future Action AI system to record users' eco-friendly behavior, predict future environmental conditions using generative AI, and visually represent the results.

[0029] The Eco Future Action AI system according to this embodiment comprises a recording unit, a prediction unit, and a display unit. The recording unit records the user's eco-behavior. The user's eco-behavior includes, but is not limited to, recycling, energy saving, and tree planting. For example, the recording unit records the act of recycling. The recording unit can also record the act of energy saving. The recording unit can also record the act of tree planting. For example, when the user recycles, the recording unit records that act. When recording the act of energy saving, for example, it records the act of reducing electricity consumption or reducing gas use. When recording the act of tree planting, for example, it records the act of planting a specific type of tree in a specific area. The prediction unit uses generative AI to analyze the eco-behavior data recorded by the recording unit and predict the future environmental state. The prediction is made using, for example, a prediction model based on past data or machine learning, but is not limited to such examples. For example, the prediction unit uses a prediction model based on past data to predict future carbon dioxide emissions. The prediction unit can also use machine learning to predict future temperature changes. Furthermore, the prediction unit can also predict future environmental conditions using generative AI. For example, the prediction unit predicts future environmental conditions using a prediction model based on past data. The display unit visually displays the future environmental conditions predicted by the prediction unit. The display is in the form of graphs, charts, maps, etc., but is not limited to these examples. For example, the display unit displays future carbon dioxide emissions in a graph. The display unit can also display future temperature changes in a chart. The display unit can also display future environmental conditions on a map. For example, the display unit displays future carbon dioxide emissions in a graph. Thus, the Eco Future Action AI system according to this embodiment can record the user's eco-behavior, predict future environmental conditions using generative AI, and visually represent the results.

[0030] The Eco Future Action AI System includes a point management unit that manages eco points based on eco actions. The point management unit manages eco points based on eco actions. Eco points are awarded for specific eco actions, for example, but are not limited to such examples. For example, the point management unit awards eco points for recycling. The point management unit can also award eco points for energy saving actions. The point management unit can also award eco points for tree planting actions. For example, the point management unit awards eco points for recycling actions. The criteria for awarding eco points are set, for example, based on the amount and frequency of recycling. When awarding eco points for energy saving actions, the criteria are set, for example, based on the amount of reduction in electricity consumption or gas use. When awarding eco points for tree planting actions, the criteria are set, for example, based on the number and type of trees planted. In this way, the Eco Future Action AI System can manage points based on eco actions.

[0031] The Eco Future Action AI System includes a Challenge Management Department that operates Eco Challenges. The Challenge Management Department operates Eco Challenges. Eco Challenges aim to achieve specific eco-behaviors, but are not limited to such examples. For example, the Challenge Management Department operates a recycling challenge. The Challenge Management Department can also operate an energy saving challenge. The Challenge Management Department can also operate a tree planting challenge. For example, the Challenge Management Department operates a recycling challenge. The content of the Eco Challenge is set, for example, based on the amount and frequency of recycling. When operating an energy saving challenge, it is set, for example, based on the amount of reduction in electricity consumption or gas use. When operating a tree planting challenge, it is set, for example, based on the number and type of trees to be planted. In this way, the Eco Future Action AI System can operate Eco Challenges.

[0032] The Eco Future Action AI System includes a Community Management Unit that manages eco-communities. The Community Management Unit manages eco-communities. Eco-communities are, for example, groups of users who aim for the same eco-friendly actions, but are not limited to this example. For example, the Community Management Unit manages recycling communities. The Community Management Unit can also manage energy conservation communities. The Community Management Unit can also manage tree-planting communities. For example, the Community Management Unit manages recycling communities. The content of eco-communities is set, for example, based on the amount and frequency of recycling. When managing energy conservation communities, it is set, for example, based on the amount of reduction in electricity consumption or gas use. When managing tree-planting communities, it is set, for example, based on the number and type of trees planted. In this way, the Eco Future Action AI System can manage eco-communities.

[0033] The recording unit can record eco-friendly actions such as recycling, energy saving, and tree planting. For example, the recording unit can record recycling actions. Specific methods and objects of recycling include, but are not limited to, plastics, paper, and metals. For example, the recording unit can record plastic recycling. It can also record paper recycling. It can also record metal recycling. When recording energy saving actions, for example, actions such as reducing electricity consumption or gas usage can be recorded. Specific methods and objects of energy saving include, but are not limited to, reducing electricity consumption or gas usage. For example, the recording unit can record reductions in electricity consumption. It can also record reductions in gas usage. When recording tree planting actions, for example, actions such as planting a specific type of tree in a specific area can be recorded. Specific methods and areas of tree planting include, but are not limited to, which types of trees to plant in which areas. For example, the recording unit can record actions such as planting a specific type of tree in a specific area. Furthermore, the recording unit can also record actions based on the number and type of trees planted. This allows the recording unit to record a variety of eco-friendly activities.

[0034] The prediction unit can predict future environmental conditions using a prediction model based on historical data or machine learning. For example, the prediction unit can predict future carbon dioxide emissions using a prediction model based on historical data. Specific types of prediction models and their usage methods include, but are not limited to, statistical models and simulation models. For example, the prediction unit can predict future carbon dioxide emissions using a statistical model. The prediction unit can also predict future temperature changes using a simulation model. When predicting future environmental conditions using machine learning, algorithms such as neural networks and support vector machines are used. Specific algorithms and implementation methods of machine learning include, but are not limited to, neural networks and support vector machines. For example, the prediction unit can predict future temperature changes using a neural network. The prediction unit can also predict future carbon dioxide emissions using a support vector machine. This allows the prediction unit to predict future environmental conditions based on historical data.

[0035] The recording unit can analyze the user's past eco-behavior history and select an appropriate recording method. For example, the recording unit can automatically display eco-behaviors that the user has frequently performed in the past as candidates. The specific content and analysis method of past eco-behavior history include, but are not limited to, the frequency and type of behavior. For example, the recording unit can automatically display frequently performed eco-behaviors as candidates based on the user's past eco-behavior history. The recording unit can also suggest the most effective recording method based on the user's past eco-behavior history. For example, the recording unit can suggest the most effective recording method based on the user's past eco-behavior history. The recording unit can also customize the recording method based on the user's past eco-behavior history and provide the user with the most suitable method. For example, the recording unit can customize the recording method based on the user's past eco-behavior history and provide the user with the most suitable method. In this way, the recording unit can select the optimal recording method based on the user's past eco-behavior history.

[0036] The recording unit can filter the recording of eco-friendly actions based on the user's current living situation and areas of interest. For example, the recording unit can prioritize recording eco-friendly actions that the user is currently interested in. Specific details of living situation and filtering methods include, but are not limited to, home environment, work environment, and daily behavioral patterns. For example, the recording unit can suggest and encourage appropriate eco-friendly actions based on the user's home environment. It can also suggest and encourage appropriate eco-friendly actions based on the user's work environment. It can also suggest and encourage appropriate eco-friendly actions based on the user's daily behavioral patterns. Specific details of areas of interest and filtering methods include, but are not limited to, environmental protection, energy saving, and recycling. For example, if the user is interested in environmental protection, the recording unit can prioritize recording related eco-friendly actions. It can also prioritize recording related eco-friendly actions if the user is interested in energy saving. It can also prioritize recording related eco-friendly actions if the user is interested in recycling. This allows the recording unit to filter eco-friendly behaviors based on the user's lifestyle and areas of interest.

[0037] The recording unit can prioritize recording eco-behaviors that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific area, the recording unit will prioritize recording eco-behaviors recommended for that area. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and address information. For example, the recording unit can acquire the user's geographical location using GPS data. The recording unit can also acquire the user's geographical location using address information. Specific criteria and selection methods for highly relevant behaviors include, but are not limited to, behaviors related to local environmental issues. For example, the recording unit will prioritize recording eco-behaviors related to local environmental issues. The recording unit can also suggest and encourage recording of local eco-events and activities based on the user's current location. This allows the recording unit to prioritize recording eco-behaviors based on the user's geographical location.

[0038] The recording unit can analyze a user's social media activity and record relevant actions when recording eco-friendly behaviors. For example, the recording unit can automatically record eco-friendly behaviors shared by users on social media. Specific examples of social media activity and analysis methods include, but are not limited to, posts, the number of likes, and the number of followers. For example, the recording unit can analyze the content of a user's social media posts and record relevant eco-friendly behaviors. The recording unit can also record relevant eco-friendly behaviors based on the number of likes a user receives on social media. The recording unit can also record relevant eco-friendly behaviors based on the number of followers a user has on social media. The recording unit analyzes eco-friendly behaviors of interest from the user's social media activity and prompts recording. For example, the recording unit analyzes a user's social media activity and records eco-friendly behaviors of interest. The recording unit can also record relevant actions based on posts about eco-friendly behaviors on the user's social media. This allows the recording unit to record eco-friendly behaviors based on the user's social media activity.

[0039] The prediction unit can adjust the accuracy of its predictions based on the importance of the eco-actions during the prediction process. For example, the prediction unit provides detailed prediction results for highly important eco-actions. Specific evaluation criteria and adjustment methods for the importance of eco-actions include, but are not limited to, the degree of environmental impact and the difficulty of implementation. For example, the prediction unit evaluates the importance of eco-actions based on their environmental impact. The prediction unit can also evaluate the importance of eco-actions based on the difficulty of implementation. For less important eco-actions, the prediction unit provides concise prediction results. For example, the prediction unit provides concise prediction results for less important eco-actions. The prediction unit can also provide visually stimulating prediction results for less important eco-actions. Depending on the importance of the eco-actions, the prediction unit adjusts how the prediction results are displayed. For example, the prediction unit adjusts how the prediction results are displayed depending on the importance of the eco-actions. The prediction unit can also adjust the level of detail in the prediction results depending on the importance of the eco-actions. This allows the prediction unit to adjust the level of detail in the predictions based on the importance of the eco-actions.

[0040] The prediction unit can apply different prediction algorithms depending on the category of eco-behavior during prediction. For example, for eco-behavior related to recycling, the prediction unit applies an algorithm that predicts the effect of recycling. Specific classification methods for eco-behavior categories and the prediction algorithms to be applied include, but are not limited to, recycling, energy saving, and tree planting. For example, the prediction unit applies an algorithm that predicts the effect of recycling to the recycling category. The prediction unit can also apply an algorithm that predicts the effect of reducing energy consumption to the energy saving category. The prediction unit can also apply an algorithm that predicts the amount of carbon dioxide absorbed to the tree planting category. For eco-behavior related to energy saving, the prediction unit applies an algorithm that predicts the effect of reducing energy consumption. For example, the prediction unit applies an algorithm that predicts the effect of reducing energy consumption to the energy saving category. The prediction unit can also apply an algorithm that predicts the effect of reducing energy consumption to the energy saving category. For eco-behavior related to tree planting, the prediction unit applies an algorithm that predicts the amount of carbon dioxide absorbed. For example, the prediction unit applies an algorithm that predicts the amount of carbon dioxide absorbed to the tree planting category. Furthermore, the prediction unit can apply an algorithm to predict carbon dioxide absorption for the tree planting category. This allows the prediction unit to apply different prediction algorithms depending on the category of eco-friendly activity.

[0041] The prediction unit can determine the priority of predictions based on the timing of eco-friendly actions. For example, the prediction unit prioritizes prediction results for eco-friendly actions that have been implemented most recently. Specific criteria for setting implementation timing and prediction priorities include, but are not limited to, seasons or specific event periods. For example, the prediction unit can set the timing of eco-friendly actions based on seasons. The prediction unit can also set the timing of eco-friendly actions based on specific event periods. For long-term eco-friendly actions, the prediction unit provides detailed prediction results. For example, the prediction unit provides detailed prediction results for long-term eco-friendly actions. The prediction unit can also provide visually appealing prediction results for long-term eco-friendly actions. Depending on the timing of eco-friendly actions, the prediction unit adjusts the display order of prediction results. For example, the prediction unit adjusts the display order of prediction results depending on the timing of eco-friendly actions. The prediction unit can also adjust the display order of prediction results depending on the timing of eco-friendly actions. This allows the prediction unit to determine the priority of predictions based on the timing of eco-friendly actions.

[0042] The prediction unit can adjust the order of predictions based on the relevance of eco-behaviors during the prediction process. For example, the prediction unit prioritizes providing prediction results for highly relevant eco-behaviors. Specific evaluation criteria and adjustment methods for the relevance of eco-behaviors include, but are not limited to, interactions between behaviors and common goals. For example, the prediction unit evaluates the relevance of eco-behaviors based on interactions between behaviors. The prediction unit can also evaluate the relevance of eco-behaviors based on common goals. For less relevant eco-behaviors, the prediction unit provides concise prediction results. For example, the prediction unit provides concise prediction results for less relevant eco-behaviors. The prediction unit can also provide visually stimulating prediction results for less relevant eco-behaviors. Depending on the relevance of eco-behaviors, the prediction unit adjusts the display order of prediction results. For example, the prediction unit adjusts the display order of prediction results according to the relevance of eco-behaviors. The prediction unit can also adjust the display order of prediction results according to the relevance of eco-behaviors. This allows the prediction unit to adjust the order of predictions based on the relevance of eco-behaviors.

[0043] The display unit can adjust the level of detail of its display based on the impact of the eco-behavior. For example, the display unit provides detailed displays for eco-behaviors with a high impact. Specific evaluation criteria for the impact of eco-behaviors and the level of detail of the display include, but are not limited to, environmental impact and social impact. For example, the display unit evaluates the impact of eco-behaviors based on their environmental impact. The display unit can also evaluate the impact of eco-behaviors based on their social impact. For eco-behaviors with a low impact, the display unit provides a concise display. For example, the display unit provides a concise display for eco-behaviors with a low impact. The display unit can also provide visually stimulating displays for eco-behaviors with a low impact. The display unit adjusts the level of detail of its display according to the impact of the eco-behavior. For example, the display unit adjusts the level of detail of its display according to the impact of the eco-behavior. The display unit can also adjust the level of detail of its display according to the impact of the eco-behavior. This allows the display unit to adjust the level of detail of its display based on the impact of the eco-behavior.

[0044] The display unit can apply different display formats depending on the category of eco-action when displaying information. For example, for eco-actions related to recycling, the display unit visually displays the effects of recycling. Specific types of display formats and application methods include, but are not limited to, graphs, charts, and maps. For example, the display unit can visually display the effects of recycling in graph format. The display unit can also visually display the effects of recycling in chart format. The display unit can also visually display the effects of recycling in map format. For eco-actions related to energy saving, the display unit visually displays the effect of reducing energy consumption. For example, the display unit can visually display the effect of reducing energy consumption for the energy saving category. The display unit can also visually display the effect of reducing energy consumption for the energy saving category. For eco-actions related to tree planting, the display unit visually displays the amount of carbon dioxide absorbed. For example, the display unit can visually display the amount of carbon dioxide absorbed for the tree planting category. The display unit can also visually display the amount of carbon dioxide absorbed for the tree planting category. This allows the display unit to apply different display formats depending on the category of eco-friendly behavior.

[0045] The display unit can determine the priority of displays based on the timing of the implementation of eco-friendly actions. For example, the display unit can prioritize displays for the most recent eco-friendly actions. Specific criteria and methods for setting display priority include, but are not limited to, the impact and ease of implementation of the eco-friendly actions. For example, the display unit can determine the priority of displays based on the impact of the eco-friendly actions. The display unit can also determine the priority of displays based on the ease of implementation of the eco-friendly actions. For long-term eco-friendly actions, the display unit provides detailed displays. For example, the display unit provides detailed displays for long-term eco-friendly actions. The display unit can also provide visually stimulating displays for long-term eco-friendly actions. Depending on the timing of the implementation of the eco-friendly actions, the display unit adjusts the order of displays. For example, the display unit adjusts the order of displays according to the timing of the implementation of the eco-friendly actions. The display unit can also adjust the order of displays according to the timing of the implementation of the eco-friendly actions. This allows the display unit to determine the priority of displays based on the timing of the implementation of the eco-friendly actions.

[0046] The display unit can adjust the order of display based on the relevance of eco-behaviors during display. For example, the display unit can prioritize displaying eco-behaviors that are highly relevant. Specific evaluation criteria and adjustment methods for the relevance of eco-behaviors include, but are not limited to, interactions between behaviors and common goals. For example, the display unit can evaluate the relevance of eco-behaviors based on interactions between behaviors. The display unit can also evaluate the relevance of eco-behaviors based on common goals. For less relevant eco-behaviors, the display unit provides a concise display. For example, the display unit provides a concise display for less relevant eco-behaviors. The display unit can also provide a visually stimulating display for less relevant eco-behaviors. The display unit adjusts the order of display according to the relevance of eco-behaviors. For example, the display unit adjusts the order of display according to the relevance of eco-behaviors. The display unit can also adjust the order of display according to the relevance of eco-behaviors. This allows the display unit to adjust the order of display based on the relevance of eco-behaviors.

[0047] The point management unit can adjust the level of detail in point allocation based on the impact of eco-behavior during point management. For example, the point management unit provides detailed explanations of point allocation for high-impact eco-behavior. Specific criteria for setting and adjusting the level of detail in points include, but are not limited to, the unit of points and the frequency of allocation. For example, the point management unit adjusts the level of detail in points based on the unit of points. The point management unit can also adjust the level of detail in points based on the frequency of allocation. For low-impact eco-behavior, the point management unit provides a concise explanation of point allocation. For example, the point management unit provides a concise explanation of point allocation for low-impact eco-behavior. The point management unit can also provide a visually stimulating explanation of point allocation for low-impact eco-behavior. The point management unit adjusts the level of detail in point allocation according to the impact of eco-behavior. For example, the point management unit adjusts the level of detail in point allocation according to the impact of eco-behavior. The point management unit can also adjust the level of detail in point allocation according to the impact of eco-behavior. This allows the point management unit to adjust the level of detail in points based on the impact of eco-behavior.

[0048] The points management department can weight points based on the timing of eco-friendly actions during point management. For example, the points management department will prioritize awarding points to eco-friendly actions performed most recently. Specific criteria and methods for setting point weighting include, but are not limited to, the timing of the eco-friendly action and the importance of the action. For example, the points management department can weight points based on the timing of the eco-friendly action. The points management department can also weight points based on the importance of the action. For long-term eco-friendly actions, the points management department provides detailed explanations of point awarding. For example, the points management department can provide detailed explanations of point awarding for long-term eco-friendly actions. The points management department can also provide visually stimulating explanations of point awarding for long-term eco-friendly actions. Depending on the timing of the eco-friendly action, the points management department adjusts the weighting of point awarding. For example, the points management department can adjust the weighting of point awarding depending on the timing of the eco-friendly action. The points management department can also adjust the weighting of point awarding depending on the timing of the eco-friendly action. This allows the points management department to weight points based on when eco-friendly actions were taken.

[0049] The Challenge Management Department can select appropriate challenge content by referring to past challenge data when running a challenge. For example, the Challenge Management Department can select the optimal challenge content based on past successful challenges. Specific examples of past challenge data and methods of reference include, but are not limited to, success rates, number of participants, and feedback. For example, the Challenge Management Department can refer to past challenge data based on success rates. The Challenge Management Department can also refer to past challenge data based on the number of participants. From past challenge data, they can select challenge content that will attract user interest. For example, the Challenge Management Department can select challenge content that will attract user interest based on past challenge data. Furthermore, the Challenge Management Department can analyze past challenge data to select the most effective challenge content. This allows the Challenge Management Department to select the optimal challenge content based on past challenge data.

[0050] The Challenge Management Department can weight challenges based on the timing of eco-friendly actions during the challenge management process. For example, the Challenge Management Department may prioritize challenges for eco-friendly actions that have been implemented most recently. Specific criteria and methods for weighting challenges include, but are not limited to, the timing of the eco-friendly action and the importance of the action. For example, the Challenge Management Department can weight challenges based on the timing of the eco-friendly action. The Challenge Management Department can also weight challenges based on the importance of the action. For long-term eco-friendly actions, the Challenge Management Department provides detailed challenge content. For example, the Challenge Management Department can provide detailed challenge content for long-term eco-friendly actions. The Challenge Management Department can also provide visually stimulating challenge content for long-term eco-friendly actions. Depending on the timing of the eco-friendly action, the Challenge Management Department can adjust the weighting of challenges. For example, the Challenge Management Department can adjust the weighting of challenges depending on the timing of the eco-friendly action. The Challenge Management Department can also adjust the weighting of challenges depending on the timing of the eco-friendly action. This allows the Challenge Management Department to weight challenges based on the timing of the eco-friendly action.

[0051] The community management department can select the optimal management method by referring to past community data when managing a community. For example, the community management department can select the optimal management method based on successful community management methods from the past. The specific content and methods of referring to past community data include, but are not limited to, the number of participants, the content of activities, and the feedback. For example, the community management department can refer to past community data based on the number of participants. The community management department can also refer to past community data based on the content of activities. From past community data, the community management department can select a management method that will attract user interest. For example, the community management department can select a management method that will attract user interest based on past community data. The community management department can also analyze past community data and select the most effective management method. In this way, the community management department can select the optimal management method based on past community data.

[0052] The Community Management Department can weight communities based on the timing of eco-friendly actions during community management. For example, the Community Management Department can prioritize community management for most recently implemented eco-friendly actions. Specific criteria and methods for setting community weighting include, but are not limited to, the timing of eco-friendly actions and the importance of the actions. For example, the Community Management Department can weight communities based on the timing of eco-friendly actions. The Community Management Department can also weight communities based on the importance of the actions. For long-term eco-friendly actions, the Community Management Department can provide detailed community management methods. For example, the Community Management Department can provide detailed community management methods for long-term eco-friendly actions. The Community Management Department can also provide visually stimulating community management methods for long-term eco-friendly actions. Depending on the timing of eco-friendly actions, the Community Management Department can adjust the weighting of community management. For example, the Community Management Department can adjust the weighting of community management depending on the timing of eco-friendly actions. The Community Management Department can also adjust the weighting of community management depending on the timing of eco-friendly actions. This allows the community management department to weight communities based on the timing of their eco-friendly activities.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The Eco Future Action AI system can also be equipped with a health management unit that monitors the user's health status. The health management unit collects the user's health data and analyzes the impact of eco-friendly behaviors on health. For example, the health management unit can record the user's steps and heart rate and evaluate the impact of eco-friendly behaviors on exercise levels. The health management unit can also record the user's diet and analyze the impact of eco-friendly behaviors on their eating habits. Furthermore, the health management unit can collect the user's sleep data and evaluate the impact of eco-friendly behaviors on sleep quality. As a result, the Eco Future Action AI system can comprehensively evaluate the impact of eco-friendly behaviors on the user's health.

[0055] The Eco Future Action AI system can also be equipped with an evaluation unit that assesses and provides feedback on the user's eco-friendly actions. The evaluation unit analyzes the effectiveness of the user's eco-friendly actions and provides specific feedback. For example, the evaluation unit can assess the effectiveness of the user's recycling activities and provide feedback on how much resources were saved. It can also assess the effectiveness of the user's energy-saving activities and provide feedback on how much energy was saved. Furthermore, the evaluation unit can assess the effectiveness of the user's tree-planting activities and provide feedback on how much carbon dioxide was absorbed. In this way, the evaluation unit can specifically demonstrate the effectiveness of the user's eco-friendly actions and encourage further eco-friendly behavior.

[0056] The Eco Future Action AI system can also be equipped with a supplementary unit that automatically completes the details of user eco-behavior when recording it. The supplementary unit automatically completes the details of user eco-behavior when the user records it. For example, when the user recycles, the supplementary unit automatically completes the type and amount of recycled materials. It can also automatically complete the type and amount of energy saved when the user saves energy. Furthermore, when the user plants trees, the supplementary unit can automatically complete the type and number of trees planted. This allows the supplementary unit to simplify the recording of user eco-behavior and provide accurate data.

[0057] The Eco Future Action AI system can also be equipped with a pattern analysis unit that analyzes users' eco-behavior and extracts behavioral patterns. The pattern analysis unit analyzes users' eco-behavior data and extracts behavioral patterns. For example, the pattern analysis unit can analyze the tendency of users to engage in eco-behavior on specific days of the week or at specific times of day. It can also analyze the tendency of users to engage in eco-behavior under specific seasons or weather conditions. Furthermore, it can analyze the tendency of users to engage in eco-behavior during specific events or campaign periods. As a result, the pattern analysis unit can understand the patterns of users' eco-behavior and provide effective eco-behavior suggestions and feedback.

[0058] The Eco Future Action AI system can further include a comparison unit that compares a user's eco-behavior with that of other users. The comparison unit compares a user's eco-behavior data with that of other users and provides a relative evaluation. For example, the comparison unit can compare the amount of a user's recycling activities with those of other users and evaluate how much better they are. The comparison unit can also compare the effectiveness of a user's energy-saving activities with those of other users and evaluate how effective they are. Furthermore, the comparison unit can compare the results of a user's tree-planting activities with those of other users and evaluate how much they contribute. In this way, the comparison unit can compare a user's eco-behavior with that of other users and provide a relative evaluation.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The recording unit records the user's eco-friendly actions. These actions include recycling, energy saving, and tree planting. For example, the recording unit records when a user recycles, records energy saving actions such as reducing electricity consumption or gas usage, and records tree planting actions such as planting a specific type of tree in a specific area. Step 2: The prediction unit uses generative AI to analyze the eco-behavior data recorded by the recording unit and predict future environmental conditions. Predictions are made using predictive models based on historical data and machine learning. For example, the prediction unit uses predictive models based on historical data to predict future carbon dioxide emissions and uses machine learning to predict future temperature changes. Step 3: The display unit visually displays the future environmental conditions predicted by the forecasting unit. The display is presented in the form of graphs, charts, maps, etc. For example, the display unit may show future carbon dioxide emissions in a graph, future temperature changes in a chart, and future environmental conditions in a map.

[0061] (Example of form 2) The Eco-Future Action AI System according to an embodiment of the present invention is a platform that records a user's eco-behavior, predicts future environmental conditions using a generative AI, and visually represents the results. When a user records their eco-behavior, this Eco-Future Action AI System generates a simulation of the future global environment from these actions and visually displays the specific impact of the user's actions on the environment. Furthermore, users can earn "eco-points" through their eco-behavior and participate in regularly held "eco-challenges" to earn special digital badges. Additionally, by connecting with other users aiming for the same eco-behavior in an "eco-community," all participants can experience a sense of accomplishment. This platform aims to provide motivation for users to continue eco-behavior and raise awareness of environmental protection. For example, if a user recycles, the system simulates and visually displays the impact of that action on future carbon dioxide emissions. Furthermore, users can earn "eco-points" through their eco-behavior and participate in regularly held "eco-challenges" to earn special digital badges. Additionally, by connecting with other users aiming for the same eco-behavior in an "eco-community," all participants can experience a sense of accomplishment. This platform aims to provide motivation for users to continue eco-behavior and raise awareness of environmental protection. This allows the Eco Future Action AI system to record users' eco-friendly behavior, predict future environmental conditions using generative AI, and visually represent the results.

[0062] The Eco Future Action AI system according to this embodiment comprises a recording unit, a prediction unit, and a display unit. The recording unit records the user's eco-behavior. The user's eco-behavior includes, but is not limited to, recycling, energy saving, and tree planting. For example, the recording unit records the act of recycling. The recording unit can also record the act of energy saving. The recording unit can also record the act of tree planting. For example, when the user recycles, the recording unit records that act. When recording the act of energy saving, for example, it records the act of reducing electricity consumption or reducing gas use. When recording the act of tree planting, for example, it records the act of planting a specific type of tree in a specific area. The prediction unit uses generative AI to analyze the eco-behavior data recorded by the recording unit and predict the future environmental state. The prediction is made using, for example, a prediction model based on past data or machine learning, but is not limited to such examples. For example, the prediction unit uses a prediction model based on past data to predict future carbon dioxide emissions. The prediction unit can also use machine learning to predict future temperature changes. Furthermore, the prediction unit can also predict future environmental conditions using generative AI. For example, the prediction unit predicts future environmental conditions using a prediction model based on past data. The display unit visually displays the future environmental conditions predicted by the prediction unit. The display is in the form of graphs, charts, maps, etc., but is not limited to these examples. For example, the display unit displays future carbon dioxide emissions in a graph. The display unit can also display future temperature changes in a chart. The display unit can also display future environmental conditions on a map. For example, the display unit displays future carbon dioxide emissions in a graph. Thus, the Eco Future Action AI system according to this embodiment can record the user's eco-behavior, predict future environmental conditions using generative AI, and visually represent the results.

[0063] The Eco Future Action AI System includes a point management unit that manages eco points based on eco actions. The point management unit manages eco points based on eco actions. Eco points are awarded for specific eco actions, for example, but are not limited to such examples. For example, the point management unit awards eco points for recycling. The point management unit can also award eco points for energy saving actions. The point management unit can also award eco points for tree planting actions. For example, the point management unit awards eco points for recycling actions. The criteria for awarding eco points are set, for example, based on the amount and frequency of recycling. When awarding eco points for energy saving actions, the criteria are set, for example, based on the amount of reduction in electricity consumption or gas use. When awarding eco points for tree planting actions, the criteria are set, for example, based on the number and type of trees planted. In this way, the Eco Future Action AI System can manage points based on eco actions.

[0064] The Eco Future Action AI System includes a Challenge Management Department that operates Eco Challenges. The Challenge Management Department operates Eco Challenges. Eco Challenges aim to achieve specific eco-behaviors, but are not limited to such examples. For example, the Challenge Management Department operates a recycling challenge. The Challenge Management Department can also operate an energy saving challenge. The Challenge Management Department can also operate a tree planting challenge. For example, the Challenge Management Department operates a recycling challenge. The content of the Eco Challenge is set, for example, based on the amount and frequency of recycling. When operating an energy saving challenge, it is set, for example, based on the amount of reduction in electricity consumption or gas use. When operating a tree planting challenge, it is set, for example, based on the number and type of trees to be planted. In this way, the Eco Future Action AI System can operate Eco Challenges.

[0065] The Eco Future Action AI System includes a Community Management Unit that manages eco-communities. The Community Management Unit manages eco-communities. Eco-communities are, for example, groups of users who aim for the same eco-friendly actions, but are not limited to this example. For example, the Community Management Unit manages recycling communities. The Community Management Unit can also manage energy conservation communities. The Community Management Unit can also manage tree-planting communities. For example, the Community Management Unit manages recycling communities. The content of eco-communities is set, for example, based on the amount and frequency of recycling. When managing energy conservation communities, it is set, for example, based on the amount of reduction in electricity consumption or gas use. When managing tree-planting communities, it is set, for example, based on the number and type of trees planted. In this way, the Eco Future Action AI System can manage eco-communities.

[0066] The recording unit can record eco-friendly actions such as recycling, energy saving, and tree planting. For example, the recording unit can record recycling actions. Specific methods and objects of recycling include, but are not limited to, plastics, paper, and metals. For example, the recording unit can record plastic recycling. It can also record paper recycling. It can also record metal recycling. When recording energy saving actions, for example, actions such as reducing electricity consumption or gas usage can be recorded. Specific methods and objects of energy saving include, but are not limited to, reducing electricity consumption or gas usage. For example, the recording unit can record reductions in electricity consumption. It can also record reductions in gas usage. When recording tree planting actions, for example, actions such as planting a specific type of tree in a specific area can be recorded. Specific methods and areas of tree planting include, but are not limited to, which types of trees to plant in which areas. For example, the recording unit can record actions such as planting a specific type of tree in a specific area. Furthermore, the recording unit can also record actions based on the number and type of trees planted. This allows the recording unit to record a variety of eco-friendly activities.

[0067] The prediction unit can predict future environmental conditions using a prediction model based on historical data or machine learning. For example, the prediction unit can predict future carbon dioxide emissions using a prediction model based on historical data. Specific types of prediction models and their usage methods include, but are not limited to, statistical models and simulation models. For example, the prediction unit can predict future carbon dioxide emissions using a statistical model. The prediction unit can also predict future temperature changes using a simulation model. When predicting future environmental conditions using machine learning, algorithms such as neural networks and support vector machines are used. Specific algorithms and implementation methods of machine learning include, but are not limited to, neural networks and support vector machines. For example, the prediction unit can predict future temperature changes using a neural network. The prediction unit can also predict future carbon dioxide emissions using a support vector machine. This allows the prediction unit to predict future environmental conditions based on historical data.

[0068] The recording unit can estimate the user's emotions and adjust the timing of recording eco-behaviors based on the estimated emotions. For example, if the user is stressed, the recording unit may simplify the recording of eco-behaviors to complete them in a shorter time. Methods for estimating user emotions include, but are not limited to, facial recognition, voice analysis, and survey results. For example, the recording unit may use facial recognition technology to estimate the user's emotions. The recording unit may also use voice analysis technology to estimate the user's emotions. The recording unit may also estimate the user's emotions based on survey results. If the user is relaxed, the recording unit may prompt for detailed recording of eco-behaviors and request additional information. For example, the recording unit may prompt for detailed recording of eco-behaviors when the user is relaxed. The recording unit may also request additional information when the user is relaxed. If the user is busy, the recording unit may postpone recording eco-behaviors and set a reminder to record them later. For example, the recording unit may postpone recording eco-behaviors when the user is busy. Furthermore, the recording unit can also set reminders to record later. This allows the recording unit to adjust the timing of recording eco-friendly behaviors according to the user's mood.

[0069] The recording unit can analyze the user's past eco-behavior history and select an appropriate recording method. For example, the recording unit can automatically display eco-behaviors that the user has frequently performed in the past as candidates. The specific content and analysis method of past eco-behavior history include, but are not limited to, the frequency and type of behavior. For example, the recording unit can automatically display frequently performed eco-behaviors as candidates based on the user's past eco-behavior history. The recording unit can also suggest the most effective recording method based on the user's past eco-behavior history. For example, the recording unit can suggest the most effective recording method based on the user's past eco-behavior history. The recording unit can also customize the recording method based on the user's past eco-behavior history and provide the user with the most suitable method. For example, the recording unit can customize the recording method based on the user's past eco-behavior history and provide the user with the most suitable method. In this way, the recording unit can select the optimal recording method based on the user's past eco-behavior history.

[0070] The recording unit can filter the recording of eco-friendly actions based on the user's current living situation and areas of interest. For example, the recording unit can prioritize recording eco-friendly actions that the user is currently interested in. Specific details of living situation and filtering methods include, but are not limited to, home environment, work environment, and daily behavioral patterns. For example, the recording unit can suggest and encourage appropriate eco-friendly actions based on the user's home environment. It can also suggest and encourage appropriate eco-friendly actions based on the user's work environment. It can also suggest and encourage appropriate eco-friendly actions based on the user's daily behavioral patterns. Specific details of areas of interest and filtering methods include, but are not limited to, environmental protection, energy saving, and recycling. For example, if the user is interested in environmental protection, the recording unit can prioritize recording related eco-friendly actions. It can also prioritize recording related eco-friendly actions if the user is interested in energy saving. It can also prioritize recording related eco-friendly actions if the user is interested in recycling. This allows the recording unit to filter eco-friendly behaviors based on the user's lifestyle and areas of interest.

[0071] The recording unit can estimate the user's emotions and set priorities for eco-friendly actions to record based on those emotions. For example, if the user is stressed, the recording unit will prioritize recording simple and easy eco-friendly actions. Specific criteria and methods for setting priorities include, but are not limited to, the impact and ease of implementation of the eco-friendly actions. For example, the recording unit can set priorities based on the impact of the eco-friendly actions. It can also set priorities based on the ease of implementation. If the user is relaxed, the recording unit will prioritize recording highly effective eco-friendly actions. For example, when the user is relaxed, the recording unit will prioritize recording highly effective eco-friendly actions. It can also encourage the user to record more detailed eco-friendly actions when they are relaxed. If the user is busy, the recording unit will prioritize recording eco-friendly actions that can be completed in a short time. For example, when the user is busy, the recording unit will prioritize recording eco-friendly actions that can be completed in a short time. It can also allow the user to postpone recording eco-friendly actions when they are busy and set a reminder to record them later. This allows the recording unit to prioritize eco-friendly actions based on the user's emotions.

[0072] The recording unit can prioritize recording eco-behaviors that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific area, the recording unit will prioritize recording eco-behaviors recommended for that area. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and address information. For example, the recording unit can acquire the user's geographical location using GPS data. The recording unit can also acquire the user's geographical location using address information. Specific criteria and selection methods for highly relevant behaviors include, but are not limited to, behaviors related to local environmental issues. For example, the recording unit will prioritize recording eco-behaviors related to local environmental issues. The recording unit can also suggest and encourage recording of local eco-events and activities based on the user's current location. This allows the recording unit to prioritize recording eco-behaviors based on the user's geographical location.

[0073] The recording unit can analyze a user's social media activity and record relevant actions when recording eco-friendly behaviors. For example, the recording unit can automatically record eco-friendly behaviors shared by users on social media. Specific examples of social media activity and analysis methods include, but are not limited to, posts, the number of likes, and the number of followers. For example, the recording unit can analyze the content of a user's social media posts and record relevant eco-friendly behaviors. The recording unit can also record relevant eco-friendly behaviors based on the number of likes a user receives on social media. The recording unit can also record relevant eco-friendly behaviors based on the number of followers a user has on social media. The recording unit analyzes eco-friendly behaviors of interest from the user's social media activity and prompts recording. For example, the recording unit analyzes a user's social media activity and records eco-friendly behaviors of interest. The recording unit can also record relevant actions based on posts about eco-friendly behaviors on the user's social media. This allows the recording unit to record eco-friendly behaviors based on the user's social media activity.

[0074] The prediction unit can estimate the user's emotions and adjust the way the prediction is presented based on the estimated emotions. For example, if the user is relaxed, the prediction unit provides detailed prediction results. Specific types of prediction presentation methods and adjustment methods include, but are not limited to, text, graphs, and animations. For example, the prediction unit can provide detailed prediction results in text format. It can also provide detailed prediction results in graph format. It can also provide detailed prediction results in animation format. If the user is stressed, the prediction unit provides concise and to-the-point prediction results. For example, when the user is stressed, the prediction unit provides concise and to-the-point prediction results. It can also provide visually stimulating prediction results when the user is stressed. If the user is excited, the prediction unit provides visually stimulating prediction results. For example, when the user is excited, the prediction unit provides visually stimulating prediction results. It can also provide detailed prediction results when the user is excited. This allows the prediction unit to adjust the way the prediction is presented according to the user's emotions.

[0075] The prediction unit can adjust the accuracy of its predictions based on the importance of the eco-actions during the prediction process. For example, the prediction unit provides detailed prediction results for highly important eco-actions. Specific evaluation criteria and adjustment methods for the importance of eco-actions include, but are not limited to, the degree of environmental impact and the difficulty of implementation. For example, the prediction unit evaluates the importance of eco-actions based on their environmental impact. The prediction unit can also evaluate the importance of eco-actions based on the difficulty of implementation. For less important eco-actions, the prediction unit provides concise prediction results. For example, the prediction unit provides concise prediction results for less important eco-actions. The prediction unit can also provide visually stimulating prediction results for less important eco-actions. Depending on the importance of the eco-actions, the prediction unit adjusts how the prediction results are displayed. For example, the prediction unit adjusts how the prediction results are displayed depending on the importance of the eco-actions. The prediction unit can also adjust the level of detail in the prediction results depending on the importance of the eco-actions. This allows the prediction unit to adjust the level of detail in the predictions based on the importance of the eco-actions.

[0076] The prediction unit can apply different prediction algorithms depending on the category of eco-behavior during prediction. For example, for eco-behavior related to recycling, the prediction unit applies an algorithm that predicts the effect of recycling. Specific classification methods for eco-behavior categories and the prediction algorithms to be applied include, but are not limited to, recycling, energy saving, and tree planting. For example, the prediction unit applies an algorithm that predicts the effect of recycling to the recycling category. The prediction unit can also apply an algorithm that predicts the effect of reducing energy consumption to the energy saving category. The prediction unit can also apply an algorithm that predicts the amount of carbon dioxide absorbed to the tree planting category. For eco-behavior related to energy saving, the prediction unit applies an algorithm that predicts the effect of reducing energy consumption. For example, the prediction unit applies an algorithm that predicts the effect of reducing energy consumption to the energy saving category. The prediction unit can also apply an algorithm that predicts the effect of reducing energy consumption to the energy saving category. For eco-behavior related to tree planting, the prediction unit applies an algorithm that predicts the amount of carbon dioxide absorbed. For example, the prediction unit applies an algorithm that predicts the amount of carbon dioxide absorbed to the tree planting category. Furthermore, the prediction unit can apply an algorithm to predict carbon dioxide absorption for the tree planting category. This allows the prediction unit to apply different prediction algorithms depending on the category of eco-friendly activity.

[0077] The prediction unit can estimate the user's emotions and adjust the prediction period based on the estimated emotions. For example, if the user is in a hurry, the prediction unit will provide a short, concise prediction. Specific criteria for setting and adjusting the prediction period include, but are not limited to, short-term, medium-term, and long-term predictions. For example, the prediction unit can provide a short-term prediction. It can also provide a medium-term prediction. It can also provide a long-term prediction. If the user is relaxed, the prediction unit will provide a detailed prediction. For example, when the user is relaxed, the prediction unit will provide a detailed prediction. It can also provide a visually stimulating prediction. If the user is excited, the prediction unit will provide a visually stimulating prediction. For example, when the user is excited, the prediction unit will provide a visually stimulating prediction. It can also provide a detailed prediction. This allows the prediction unit to adjust the prediction period according to the user's emotions.

[0078] The prediction unit can determine the priority of predictions based on the timing of eco-friendly actions. For example, the prediction unit prioritizes prediction results for eco-friendly actions that have been implemented most recently. Specific criteria for setting implementation timing and prediction priorities include, but are not limited to, seasons or specific event periods. For example, the prediction unit can set the timing of eco-friendly actions based on seasons. The prediction unit can also set the timing of eco-friendly actions based on specific event periods. For long-term eco-friendly actions, the prediction unit provides detailed prediction results. For example, the prediction unit provides detailed prediction results for long-term eco-friendly actions. The prediction unit can also provide visually appealing prediction results for long-term eco-friendly actions. Depending on the timing of eco-friendly actions, the prediction unit adjusts the display order of prediction results. For example, the prediction unit adjusts the display order of prediction results depending on the timing of eco-friendly actions. The prediction unit can also adjust the display order of prediction results depending on the timing of eco-friendly actions. This allows the prediction unit to determine the priority of predictions based on the timing of eco-friendly actions.

[0079] The prediction unit can adjust the order of predictions based on the relevance of eco-behaviors during the prediction process. For example, the prediction unit prioritizes providing prediction results for highly relevant eco-behaviors. Specific evaluation criteria and adjustment methods for the relevance of eco-behaviors include, but are not limited to, interactions between behaviors and common goals. For example, the prediction unit evaluates the relevance of eco-behaviors based on interactions between behaviors. The prediction unit can also evaluate the relevance of eco-behaviors based on common goals. For less relevant eco-behaviors, the prediction unit provides concise prediction results. For example, the prediction unit provides concise prediction results for less relevant eco-behaviors. The prediction unit can also provide visually stimulating prediction results for less relevant eco-behaviors. Depending on the relevance of eco-behaviors, the prediction unit adjusts the display order of prediction results. For example, the prediction unit adjusts the display order of prediction results according to the relevance of eco-behaviors. The prediction unit can also adjust the display order of prediction results according to the relevance of eco-behaviors. This allows the prediction unit to adjust the order of predictions based on the relevance of eco-behaviors.

[0080] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. Specific types of display methods and adjustment methods include, but are not limited to, text displays, graphical displays, and audio guidance. For example, the display unit can provide text displays. It can also provide graphical displays. It can also provide audio guidance. If the user is relaxed, the display unit provides a display method that includes detailed information. For example, when the user is relaxed, the display unit provides a display method that includes detailed information. It can also provide a visually stimulating display method when the user is relaxed. If the user is in a hurry, the display unit provides a display method that gets straight to the point. For example, when the user is in a hurry, the display unit provides a display method that gets straight to the point. It can also provide a visually stimulating display method when the user is in a hurry. This allows the display unit to adjust the display method according to the user's emotions.

[0081] The display unit can adjust the level of detail of its display based on the impact of the eco-behavior. For example, the display unit provides detailed displays for eco-behaviors with a high impact. Specific evaluation criteria for the impact of eco-behaviors and the level of detail of the display include, but are not limited to, environmental impact and social impact. For example, the display unit evaluates the impact of eco-behaviors based on their environmental impact. The display unit can also evaluate the impact of eco-behaviors based on their social impact. For eco-behaviors with a low impact, the display unit provides a concise display. For example, the display unit provides a concise display for eco-behaviors with a low impact. The display unit can also provide visually stimulating displays for eco-behaviors with a low impact. The display unit adjusts the level of detail of its display according to the impact of the eco-behavior. For example, the display unit adjusts the level of detail of its display according to the impact of the eco-behavior. The display unit can also adjust the level of detail of its display according to the impact of the eco-behavior. This allows the display unit to adjust the level of detail of its display based on the impact of the eco-behavior.

[0082] The display unit can apply different display formats depending on the category of eco-action when displaying information. For example, for eco-actions related to recycling, the display unit visually displays the effects of recycling. Specific types of display formats and application methods include, but are not limited to, graphs, charts, and maps. For example, the display unit can visually display the effects of recycling in graph format. The display unit can also visually display the effects of recycling in chart format. The display unit can also visually display the effects of recycling in map format. For eco-actions related to energy saving, the display unit visually displays the effect of reducing energy consumption. For example, the display unit can visually display the effect of reducing energy consumption for the energy saving category. The display unit can also visually display the effect of reducing energy consumption for the energy saving category. For eco-actions related to tree planting, the display unit visually displays the amount of carbon dioxide absorbed. For example, the display unit can visually display the amount of carbon dioxide absorbed for the tree planting category. The display unit can also visually display the amount of carbon dioxide absorbed for the tree planting category. This allows the display unit to apply different display formats depending on the category of eco-friendly behavior.

[0083] The display unit can estimate the user's emotions and adjust the display duration based on the estimated emotions. For example, if the user is in a hurry, the display unit provides a short, concise display. Specific criteria for setting and adjusting the display duration include, but are not limited to, short-term, medium-term, and long-term displays. For example, the display unit can provide a short-term display. It can also provide a medium-term display. It can also provide a long-term display. If the user is relaxed, the display unit provides a detailed display. For example, when the user is relaxed, the display unit provides a detailed display. It can also provide a visually stimulating display when the user is relaxed. If the user is excited, the display unit provides a visually stimulating display. For example, when the user is excited, the display unit provides a visually stimulating display. It can also provide a detailed display when the user is excited. This allows the display unit to adjust the display duration according to the user's emotions.

[0084] The display unit can determine the priority of displays based on the timing of the implementation of eco-friendly actions. For example, the display unit can prioritize displays for the most recent eco-friendly actions. Specific criteria and methods for setting display priority include, but are not limited to, the impact and ease of implementation of the eco-friendly actions. For example, the display unit can determine the priority of displays based on the impact of the eco-friendly actions. The display unit can also determine the priority of displays based on the ease of implementation of the eco-friendly actions. For long-term eco-friendly actions, the display unit provides detailed displays. For example, the display unit provides detailed displays for long-term eco-friendly actions. The display unit can also provide visually stimulating displays for long-term eco-friendly actions. Depending on the timing of the implementation of the eco-friendly actions, the display unit adjusts the order of displays. For example, the display unit adjusts the order of displays according to the timing of the implementation of the eco-friendly actions. The display unit can also adjust the order of displays according to the timing of the implementation of the eco-friendly actions. This allows the display unit to determine the priority of displays based on the timing of the implementation of the eco-friendly actions.

[0085] The display unit can adjust the order of display based on the relevance of eco-behaviors during display. For example, the display unit can prioritize displaying eco-behaviors that are highly relevant. Specific evaluation criteria and adjustment methods for the relevance of eco-behaviors include, but are not limited to, interactions between behaviors and common goals. For example, the display unit can evaluate the relevance of eco-behaviors based on interactions between behaviors. The display unit can also evaluate the relevance of eco-behaviors based on common goals. For less relevant eco-behaviors, the display unit provides a concise display. For example, the display unit provides a concise display for less relevant eco-behaviors. The display unit can also provide a visually stimulating display for less relevant eco-behaviors. The display unit adjusts the order of display according to the relevance of eco-behaviors. For example, the display unit adjusts the order of display according to the relevance of eco-behaviors. The display unit can also adjust the order of display according to the relevance of eco-behaviors. This allows the display unit to adjust the order of display based on the relevance of eco-behaviors.

[0086] The points management unit can estimate the user's emotions and adjust the point awarding method based on the estimated emotions. For example, if the user is relaxed, the points management unit will provide a detailed explanation of point awarding. Specific criteria and adjustment methods for point awarding include, but are not limited to, the type of eco-behavior and the frequency of the behavior. For example, the points management unit will adjust the point awarding method based on the type of eco-behavior. The points management unit can also adjust the point awarding method based on the frequency of the behavior. If the user is stressed, the points management unit will provide a concise and to-the-point explanation of point awarding. For example, the points management unit will provide a concise and to-the-point explanation of point awarding when the user is stressed. The points management unit can also provide a visually stimulating explanation of point awarding when the user is stressed. If the user is excited, the points management unit will provide a visually stimulating explanation of point awarding. For example, the points management unit will provide a visually stimulating explanation of point awarding when the user is excited. The points management unit can also provide a detailed explanation of point awarding when the user is excited. This allows the points management unit to adjust the point awarding method according to the user's emotions.

[0087] The point management unit can adjust the level of detail in point allocation based on the impact of eco-behavior during point management. For example, the point management unit provides detailed explanations of point allocation for high-impact eco-behavior. Specific criteria for setting and adjusting the level of detail in points include, but are not limited to, the unit of points and the frequency of allocation. For example, the point management unit adjusts the level of detail in points based on the unit of points. The point management unit can also adjust the level of detail in points based on the frequency of allocation. For low-impact eco-behavior, the point management unit provides a concise explanation of point allocation. For example, the point management unit provides a concise explanation of point allocation for low-impact eco-behavior. The point management unit can also provide a visually stimulating explanation of point allocation for low-impact eco-behavior. The point management unit adjusts the level of detail in point allocation according to the impact of eco-behavior. For example, the point management unit adjusts the level of detail in point allocation according to the impact of eco-behavior. The point management unit can also adjust the level of detail in point allocation according to the impact of eco-behavior. This allows the point management unit to adjust the level of detail in points based on the impact of eco-behavior.

[0088] The points management unit can estimate the user's emotions and determine the priority of points based on those emotions. For example, if the user is relaxed, the points management unit can provide a detailed explanation for awarding points. Specific criteria and methods for setting point priorities include, but are not limited to, the impact of eco-friendly behaviors and the ease of implementation. For example, the points management unit can determine point priorities based on the impact of eco-friendly behaviors. It can also determine point priorities based on the ease of implementation of eco-friendly behaviors. If the user is stressed, the points management unit can provide a concise and to-the-point explanation for awarding points. For example, if the user is stressed, the points management unit can provide a concise and to-the-point explanation for awarding points. It can also provide a visually stimulating explanation for awarding points when the user is stressed. If the user is excited, the points management unit can provide a visually stimulating explanation for awarding points. For example, if the user is excited, the points management unit can provide a visually stimulating explanation for awarding points. It can also provide a detailed explanation for awarding points when the user is excited. This allows the points management unit to determine the priority of points based on the user's emotions.

[0089] The points management department can weight points based on the timing of eco-friendly actions during point management. For example, the points management department will prioritize awarding points to eco-friendly actions performed most recently. Specific criteria and methods for setting point weighting include, but are not limited to, the timing of the eco-friendly action and the importance of the action. For example, the points management department can weight points based on the timing of the eco-friendly action. The points management department can also weight points based on the importance of the action. For long-term eco-friendly actions, the points management department provides detailed explanations of point awarding. For example, the points management department can provide detailed explanations of point awarding for long-term eco-friendly actions. The points management department can also provide visually stimulating explanations of point awarding for long-term eco-friendly actions. Depending on the timing of the eco-friendly action, the points management department adjusts the weighting of point awarding. For example, the points management department can adjust the weighting of point awarding depending on the timing of the eco-friendly action. The points management department can also adjust the weighting of point awarding depending on the timing of the eco-friendly action. This allows the points management department to weight points based on when eco-friendly actions were taken.

[0090] The Challenge Management team can estimate the user's emotions and adjust the challenge content based on those emotions. For example, if the user is relaxed, the Challenge Management team will provide a detailed challenge. Specific criteria for setting and adjusting the challenge content include, but are not limited to, the type of challenge, difficulty level, and duration. For example, the Challenge Management team can adjust the challenge content based on the type of challenge. They can also adjust the challenge content based on the difficulty level. If the user is stressed, the Challenge Management team will provide a concise and to-the-point challenge. For example, when the user is stressed, the Challenge Management team will provide a concise and to-the-point challenge. They can also provide a visually stimulating challenge. If the user is excited, the Challenge Management team will provide a visually stimulating challenge. For example, when the user is excited, the Challenge Management team will provide a visually stimulating challenge. They can also provide a detailed challenge. This allows the Challenge Management team to adjust the challenge content according to the user's emotions.

[0091] The Challenge Management Department can select appropriate challenge content by referring to past challenge data when running a challenge. For example, the Challenge Management Department can select the optimal challenge content based on past successful challenges. Specific examples of past challenge data and methods of reference include, but are not limited to, success rates, number of participants, and feedback. For example, the Challenge Management Department can refer to past challenge data based on success rates. The Challenge Management Department can also refer to past challenge data based on the number of participants. From past challenge data, they can select challenge content that will attract user interest. For example, the Challenge Management Department can select challenge content that will attract user interest based on past challenge data. Furthermore, the Challenge Management Department can analyze past challenge data to select the most effective challenge content. This allows the Challenge Management Department to select the optimal challenge content based on past challenge data.

[0092] The Challenge Management team can estimate the user's emotions and prioritize challenges based on those emotions. For example, if the user is relaxed, the Challenge Management team will provide detailed challenges. Specific criteria and methods for setting challenge priorities include, but are not limited to, the impact and ease of completion of the challenges. For example, the Challenge Management team may prioritize challenges based on their impact. They may also prioritize challenges based on their ease of completion. If the user is stressed, the Challenge Management team will provide concise and to-the-point challenges. For example, they may provide concise and to-the-point challenges when the user is stressed. They may also provide visually stimulating challenges when the user is stressed. If the user is excited, the Challenge Management team will provide visually stimulating challenges. For example, they may provide visually stimulating challenges when the user is excited. They may also provide detailed challenges when the user is excited. This allows the challenge management team to prioritize challenges based on user sentiment.

[0093] The Challenge Management Department can weight challenges based on the timing of eco-friendly actions during the challenge management process. For example, the Challenge Management Department may prioritize challenges for eco-friendly actions that have been implemented most recently. Specific criteria and methods for weighting challenges include, but are not limited to, the timing of the eco-friendly action and the importance of the action. For example, the Challenge Management Department can weight challenges based on the timing of the eco-friendly action. The Challenge Management Department can also weight challenges based on the importance of the action. For long-term eco-friendly actions, the Challenge Management Department provides detailed challenge content. For example, the Challenge Management Department can provide detailed challenge content for long-term eco-friendly actions. The Challenge Management Department can also provide visually stimulating challenge content for long-term eco-friendly actions. Depending on the timing of the eco-friendly action, the Challenge Management Department can adjust the weighting of challenges. For example, the Challenge Management Department can adjust the weighting of challenges depending on the timing of the eco-friendly action. The Challenge Management Department can also adjust the weighting of challenges depending on the timing of the eco-friendly action. This allows the Challenge Management Department to weight challenges based on the timing of the eco-friendly action.

[0094] The community management department can estimate users' emotions and adjust community management methods based on those estimates. For example, if a user is relaxed, the community management department can provide detailed community management methods. Specific criteria and adjustment methods for community management methods include, but are not limited to, activities, participation conditions, and management structure. For example, the community management department can adjust community management methods based on activities. It can also adjust community management methods based on participation conditions. If a user is stressed, the community management department can provide concise and to-the-point community management methods. For example, when a user is stressed, the community management department can provide concise and to-the-point community management methods. It can also provide visually stimulating community management methods when a user is stressed. If a user is excited, the community management department can provide visually stimulating community management methods. For example, when a user is excited, the community management department can provide visually stimulating community management methods. It can also provide detailed community management methods when a user is excited. This allows the community management team to adjust how the community is run in response to user sentiment.

[0095] The community management department can select the optimal management method by referring to past community data when managing a community. For example, the community management department can select the optimal management method based on successful community management methods from the past. The specific content and methods of referring to past community data include, but are not limited to, the number of participants, the content of activities, and the feedback. For example, the community management department can refer to past community data based on the number of participants. The community management department can also refer to past community data based on the content of activities. From past community data, the community management department can select a management method that will attract user interest. For example, the community management department can select a management method that will attract user interest based on past community data. The community management department can also analyze past community data and select the most effective management method. In this way, the community management department can select the optimal management method based on past community data.

[0096] The community management department can estimate user emotions and determine community priorities based on those emotions. For example, if a user is relaxed, the community management department can provide detailed community management methods. Specific criteria and methods for setting community priorities include, but are not limited to, the impact of activities and the level of participant interest. For example, the community management department can determine community priorities based on the impact of activities. It can also determine community priorities based on the level of participant interest. If a user is stressed, the community management department can provide concise and to-the-point community management methods. For example, when a user is stressed, the community management department can provide concise and to-the-point community management methods. It can also provide visually stimulating community management methods. If a user is excited, the community management department can provide visually stimulating community management methods. For example, when a user is excited, the community management department can provide visually stimulating community management methods. It can also provide detailed community management methods when a user is excited. This allows the community management team to prioritize communities based on user sentiment.

[0097] The Community Management Department can weight communities based on the timing of eco-friendly actions during community management. For example, the Community Management Department can prioritize community management for most recently implemented eco-friendly actions. Specific criteria and methods for setting community weighting include, but are not limited to, the timing of eco-friendly actions and the importance of the actions. For example, the Community Management Department can weight communities based on the timing of eco-friendly actions. The Community Management Department can also weight communities based on the importance of the actions. For long-term eco-friendly actions, the Community Management Department can provide detailed community management methods. For example, the Community Management Department can provide detailed community management methods for long-term eco-friendly actions. The Community Management Department can also provide visually stimulating community management methods for long-term eco-friendly actions. Depending on the timing of eco-friendly actions, the Community Management Department can adjust the weighting of community management. For example, the Community Management Department can adjust the weighting of community management depending on the timing of eco-friendly actions. The Community Management Department can also adjust the weighting of community management depending on the timing of eco-friendly actions. This allows the community management department to weight communities based on the timing of their eco-friendly activities. === Hard Collateral 1-1 === Each of the multiple elements described above, including the recording unit, prediction unit, display unit, point management unit, challenge operation unit, and community management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records the user's eco-behavior using the control unit 46A of the smart device 14. The prediction unit predicts future environmental conditions using generated AI by the specific processing unit 290 of the data processing unit 12. The display unit visually displays the prediction results on the display 40A of the smart device 14. The point management unit manages eco-points using the specific processing unit 290 of the data processing unit 12. The challenge operation unit operates eco-challenges using the specific processing unit 290 of the data processing unit 12. The community management unit manages eco-communities using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the recording unit, prediction unit, display unit, point management unit, challenge operation unit, and community management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records the user's eco-behavior using the control unit 46A of the smart glasses 214. The prediction unit predicts future environmental conditions using generated AI by the specific processing unit 290 of the data processing unit 12. The display unit visually displays the prediction results on the display of the smart glasses 214. The point management unit manages eco-points using the specific processing unit 290 of the data processing unit 12. The challenge operation unit operates eco-challenges using the specific processing unit 290 of the data processing unit 12. The community management unit manages eco-communities using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the recording unit, prediction unit, display unit, point management unit, challenge operation unit, and community management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records the user's eco-behavior using the control unit 46A of the headset terminal 314. The prediction unit predicts future environmental conditions using generated AI by the specific processing unit 290 of the data processing unit 12. The display unit visually displays the prediction results on the display 343 of the headset terminal 314. The point management unit manages eco-points using the specific processing unit 290 of the data processing unit 12. The challenge operation unit operates eco-challenges using the specific processing unit 290 of the data processing unit 12. The community management unit manages eco-communities using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the recording unit, prediction unit, display unit, point management unit, challenge operation unit, and community management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recording unit records the user's eco-behavior using the control unit 46A of the robot 414. The prediction unit predicts future environmental conditions using generated AI by the specific processing unit 290 of the data processing unit 12. The display unit visually displays the prediction results on the display of the robot 414. The point management unit manages eco-points using the specific processing unit 290 of the data processing unit 12. The challenge operation unit operates the eco-challenge using the specific processing unit 290 of the data processing unit 12. The community management unit manages the eco-community using the control unit 46A of the robot 414.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The Eco Future Action AI system can also be equipped with a health management unit that monitors the user's health status. The health management unit collects the user's health data and analyzes the impact of eco-friendly behaviors on health. For example, the health management unit can record the user's steps and heart rate and evaluate the impact of eco-friendly behaviors on exercise levels. The health management unit can also record the user's diet and analyze the impact of eco-friendly behaviors on their eating habits. Furthermore, the health management unit can collect the user's sleep data and evaluate the impact of eco-friendly behaviors on sleep quality. As a result, the Eco Future Action AI system can comprehensively evaluate the impact of eco-friendly behaviors on the user's health.

[0100] The Eco Future Action AI system can also include a suggestion unit that estimates the user's emotions and proposes eco-friendly actions based on those emotions. If the user is feeling stressed, the suggestion unit will suggest relaxing eco-friendly actions. For example, when the user is stressed, the suggestion unit might suggest activities like nature walks or gardening. Furthermore, if the user is relaxed, the suggestion unit can also suggest more proactive eco-friendly actions. For example, when the user is relaxed, the suggestion unit might suggest activities like community work or volunteer work. Additionally, if the user is excited, the suggestion unit can suggest eco-friendly actions that allow the user to release energy. For example, when the user is excited, the suggestion unit might suggest activities like recycling or participating in eco-events. This allows the suggestion unit to propose eco-friendly actions that are tailored to the user's emotions.

[0101] The Eco Future Action AI system can also be equipped with an evaluation unit that assesses and provides feedback on the user's eco-friendly actions. The evaluation unit analyzes the effectiveness of the user's eco-friendly actions and provides specific feedback. For example, the evaluation unit can assess the effectiveness of the user's recycling activities and provide feedback on how much resources were saved. It can also assess the effectiveness of the user's energy-saving activities and provide feedback on how much energy was saved. Furthermore, the evaluation unit can assess the effectiveness of the user's tree-planting activities and provide feedback on how much carbon dioxide was absorbed. In this way, the evaluation unit can specifically demonstrate the effectiveness of the user's eco-friendly actions and encourage further eco-friendly behavior.

[0102] The Eco Future Action AI system can further include a reward adjustment unit that estimates the user's emotions and adjusts the rewards for eco-friendly actions based on those emotions. The reward adjustment unit provides detailed reward explanations when the user is relaxed. For example, it explains the detailed criteria for awarding eco-points and the benefits when the user is relaxed. The reward adjustment unit can also provide concise and to-the-point reward explanations when the user is stressed. For example, it briefly explains the criteria for awarding eco-points and the benefits when the user is stressed. Furthermore, the reward adjustment unit can provide visually stimulating reward explanations when the user is excited. For example, it visually displays the eco-point benefits when the user is excited. This allows the reward adjustment unit to provide reward explanations tailored to the user's emotions.

[0103] The Eco Future Action AI system can also be equipped with a supplementary unit that automatically completes the details of user eco-behavior when recording it. The supplementary unit automatically completes the details of user eco-behavior when the user records it. For example, when the user recycles, the supplementary unit automatically completes the type and amount of recycled materials. It can also automatically complete the type and amount of energy saved when the user saves energy. Furthermore, when the user plants trees, the supplementary unit can automatically complete the type and number of trees planted. This allows the supplementary unit to simplify the recording of user eco-behavior and provide accurate data.

[0104] The Eco Future Action AI system may further include a recording adjustment unit that estimates the user's emotions and adjusts the recording method of eco-behavior based on the estimated emotions. The recording adjustment unit provides a detailed recording method when the user is relaxed. For example, it explains the detailed recording method of eco-behavior when the user is relaxed. The recording adjustment unit can also provide a concise and to-the-point recording method when the user is stressed. For example, it briefly explains the recording method of eco-behavior when the user is stressed. Furthermore, the recording adjustment unit can provide a visually stimulating recording method when the user is excited. For example, it visually displays the recording method of eco-behavior when the user is excited. This allows the recording adjustment unit to provide a recording method that is appropriate to the user's emotions.

[0105] The Eco Future Action AI system can also be equipped with a pattern analysis unit that analyzes users' eco-behavior and extracts behavioral patterns. The pattern analysis unit analyzes users' eco-behavior data and extracts behavioral patterns. For example, the pattern analysis unit can analyze the tendency of users to engage in eco-behavior on specific days of the week or at specific times of day. It can also analyze the tendency of users to engage in eco-behavior under specific seasons or weather conditions. Furthermore, it can analyze the tendency of users to engage in eco-behavior during specific events or campaign periods. As a result, the pattern analysis unit can understand the patterns of users' eco-behavior and provide effective eco-behavior suggestions and feedback.

[0106] The Eco Future Action AI system can further include a feedback adjustment unit that estimates the user's emotions and adjusts the feedback on eco-friendly behaviors based on those emotions. The feedback adjustment unit provides detailed feedback when the user is relaxed. For example, it explains in detail the effects and areas for improvement of eco-friendly behaviors when the user is relaxed. The feedback adjustment unit can also provide concise and to-the-point feedback when the user is stressed. For example, it briefly explains the effects and areas for improvement of eco-friendly behaviors when the user is stressed. Furthermore, the feedback adjustment unit can provide visually stimulating feedback when the user is excited. For example, it visually displays the effects and areas for improvement of eco-friendly behaviors when the user is excited. This allows the feedback adjustment unit to provide feedback tailored to the user's emotions.

[0107] The Eco Future Action AI system can further include a comparison unit that compares a user's eco-behavior with that of other users. The comparison unit compares a user's eco-behavior data with that of other users and provides a relative evaluation. For example, the comparison unit can compare the amount of a user's recycling activities with those of other users and evaluate how much better they are. The comparison unit can also compare the effectiveness of a user's energy-saving activities with those of other users and evaluate how effective they are. Furthermore, the comparison unit can compare the results of a user's tree-planting activities with those of other users and evaluate how much they contribute. In this way, the comparison unit can compare a user's eco-behavior with that of other users and provide a relative evaluation.

[0108] The Eco Future Action AI system can further include a goal-setting unit that estimates the user's emotions and sets eco-behavior goals based on those emotions. The goal-setting unit sets detailed goals when the user is relaxed. For example, it sets specific eco-behavior goals when the user is relaxed. The goal-setting unit can also set concise and easily achievable goals when the user is stressed. For example, it sets short-term eco-behavior goals when the user is stressed. Furthermore, the goal-setting unit can set challenging goals when the user is excited. For example, it sets long-term, rewarding eco-behavior goals when the user is excited. This allows the goal-setting unit to set eco-behavior goals that are appropriate to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The recording unit records the user's eco-friendly actions. These actions include recycling, energy saving, and tree planting. For example, the recording unit records when a user recycles, records energy saving actions such as reducing electricity consumption or gas usage, and records tree planting actions such as planting a specific type of tree in a specific area. Step 2: The prediction unit uses generative AI to analyze the eco-behavior data recorded by the recording unit and predict future environmental conditions. Predictions are made using predictive models based on historical data and machine learning. For example, the prediction unit uses predictive models based on historical data to predict future carbon dioxide emissions and uses machine learning to predict future temperature changes. Step 3: The display unit visually displays the future environmental conditions predicted by the forecasting unit. The display is presented in the form of graphs, charts, maps, etc. For example, the display unit may show future carbon dioxide emissions in a graph, future temperature changes in a chart, and future environmental conditions in a map.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recording unit for documenting eco-friendly actions, A prediction unit analyzes the eco-behavior data recorded by the aforementioned recording unit and predicts future environmental conditions, The system includes a display unit that graphically displays the future environmental conditions predicted by the prediction unit. A system characterized by the following features.

2. A points management department is in place to manage eco-points earned through eco-friendly actions. The system according to feature 1.

3. The Eco Challenge is managed by the Challenge Management Department. The system according to feature 1.

4. It has a community management department that manages the eco-community. The system according to feature 1.

5. The aforementioned recording unit is Record eco-friendly actions such as recycling, energy saving, and tree planting. The system according to feature 1.

6. The prediction unit, Predicting future environmental conditions using predictive models or machine learning based on historical data. The system according to feature 1.

7. The aforementioned recording unit is It estimates the user's emotions and adjusts the timing of recording eco-friendly behaviors based on the estimated user emotions. The system according to feature 1.

8. The aforementioned recording unit is Analyze the user's past eco-friendly behavior history and select the appropriate recording method. The system according to feature 1.

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