system

Abstract

We provide the system. [Solution] Means of collecting activity data from users, A means for analyzing the data to calculate and visualize carbon dioxide emissions, A means of generating reduction measures based on analysis results and presenting them to the user, A means of providing information on environmental activities and encouraging users to participate, A means of sharing users' environmental contributions over the network, A means of providing information on local activities and supporting participation in community activities, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , ,

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 recent years, climate change on a global scale has been intensifying, and it has become difficult for individuals and companies to grasp the extent to which they are affecting the environment through their daily lives and business activities. As a result, there is a problem that specific and effective carbon dioxide reduction measures cannot be taken, and the realization of a sustainable society is delayed. In addition, there is also a lack of a mechanism for widely sharing the environmental contribution socially and raising the environmental awareness of the community.

Means for Solving the Problems

[0005] This invention solves these problems by providing a system equipped with means for collecting activity data from users and calculating and visualizing carbon dioxide emissions. Furthermore, by providing means for generating reduction measures based on analysis and proposing them to users, it enables individuals and companies to take concrete and effective environmental action. In addition, by providing information on environmental activities, encouraging participation, and enabling users to share their environmental contributions on social networks, it aims to raise environmental awareness throughout the community. With this configuration, users can promote sustainable behavioral change on a daily basis.

[0006] A "user" is an individual or organization that uses the system to provide data about their activities and receives analysis results and suggestions.

[0007] "Activity data" refers to information about users' daily lives and work activities, which is necessary for calculating carbon dioxide emissions.

[0008] "Carbon dioxide emissions" refers to the total amount of carbon dioxide generated as a result of user activities and is a measure used to evaluate the impact on the environment.

[0009] "Visualization" refers to displaying analyzed data in a format that is easy for users to understand (for example, graphs or charts).

[0010] "Reduction measures" refer to specific action guidelines or plans to effectively reduce users' carbon dioxide emissions.

[0011] "Environmental activities" refer to specific actions and events aimed at contributing to the protection and improvement of the global environment.

[0012] A "social network" refers to an online platform where users can share information and interact with other people.

[0013] "Environmental contribution" is an indicator that shows the degree of contribution a user has made to the environment, and includes specific activities and the amount of carbon dioxide reduced. [Brief explanation of the drawing]

[0014] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of this invention collects and analyzes activity data from users, makes suggestions for environmental improvement, and provides support for users to take sustainable actions.

[0036] In this system, users first provide data to the system either by entering their activity data or through automated collection by a terminal. This activity data includes information such as mode of transportation, power consumption, and purchased products. The terminal transmits this data to the server in a secure and anonymized form.

[0037] The server calculates the user's carbon dioxide emissions based on the received data and visualizes the results in real time. This visualized information is displayed on the user's device in a format that is easy for them to understand. For example, it can be viewed as a graph showing the trend of daily emissions.

[0038] Next, the server generates carbon dioxide reduction strategies tailored to the user's lifestyle based on the analyzed data. This uses machine learning algorithms to generate optimal suggestions, taking into account the user's activity patterns and past data. These reduction strategies are then communicated to the user's device as concise and easy-to-understand messages.

[0039] Furthermore, the system provides information on local environmental activities and events that users can participate in. The server collects information from the internet and external databases and filters events to match the user's interests. The terminal notifies the user and encourages them to participate.

[0040] Finally, a system is provided that aggregates the environmental contributions of users' activities and allows for easy sharing of this data on social networks. Users can share their daily contributions with other network members and contribute to raising environmental awareness within the community.

[0041] As a concrete example, when a user uses the system during their commute, they input their daily commute distance and mode of transportation into the terminal. The server then calculates the carbon dioxide emissions from their commute and suggests "commuting by bicycle once a week" as a concrete reduction measure. In response to this suggestion, the user changes their commuting style, and as a result, the system encourages conscious behavioral change.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users can either input their activity data into their device or have their activities automatically recorded using sensor data from their smart devices. This includes recording the user's means of transportation and distance traveled through the use of location services.

[0045] Step 2:

[0046] The device anonymizes the collected activity data and sends it to the server using a secure protocol. Anonymization is performed to protect user privacy.

[0047] Step 3:

[0048] The server analyzes the received data and calculates carbon dioxide emissions based on the user's activities. This calculation uses emission factors related to the mode of transportation and power consumption.

[0049] Step 4:

[0050] The server visualizes the calculated carbon dioxide emissions and generates them as easy-to-understand graphs and charts. This visualized information helps users intuitively understand their own environmental impact.

[0051] Step 5:

[0052] The visualized information is sent from the server to the terminal, and users can view the results in real time. This allows users to see the impact their daily activities have on the environment.

[0053] Step 6:

[0054] Based on the analysis results, the server uses machine learning algorithms to generate specific carbon dioxide reduction measures for the user. These suggestions may include specific behavioral changes (e.g., using public transportation).

[0055] Step 7:

[0056] The server translates the generated reduction suggestions into a message in natural language and sends it to the user's device. The user receives this notification and can review their daily actions.

[0057] Step 8:

[0058] The server retrieves information on local environmental activities from the network and external databases, and filters it based on the user's interests.

[0059] Step 9:

[0060] Filtered activity information is sent from the server to the terminal and notified to the user. This makes it easier for users to participate in activities that interest them.

[0061] Step 10:

[0062] The server aggregates the environmental contribution of users' activities and proposes this data as a post to social networks using a standardized template.

[0063] Step 11:

[0064] Users post information about their proposed environmental contributions to social media via their devices. This allows users to widely inform the community about their contributions and help raise environmental awareness.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In modern society, it is difficult to quantitatively visualize the impact of individual activities on the environment and encourage concrete behavioral improvements. Furthermore, there is a lack of mechanisms to intuitively understand one's contribution to the environment and raise awareness by sharing it with others. In addition, there is a need to provide highly relevant environmental information while securely handling users' personal information. A comprehensive system is needed to address these challenges.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for collecting activity-related information from the user, means for analyzing the information to calculate and visualize air pollutant emissions, and means for generating and presenting action improvement measures to the user based on the analysis results. This enables individuals to understand the environmental impact of their daily activities and implement concrete and effective improvement measures. Furthermore, sharing environmental contributions through a digital platform is expected to raise awareness throughout society.

[0070] A "user" is an individual or group that uses the system to provide information about their activities and to understand their impact on the environment.

[0071] "Information" refers to data related to a user's activities, including data on means of transportation, power consumption, and purchased products.

[0072] "Air pollutant emissions" refers to the amount of environmentally harmful substances emitted by user activities, and mainly includes carbon dioxide and other greenhouse gases.

[0073] "Visualization" is a method of representing numerical data as graphs and charts so that users can intuitively understand the environmental impact of their activities.

[0074] "Behavioral improvement measures" are actions that, based on user activity data, provide specific suggestions and policies to reduce the environmental impact.

[0075] A "digital platform" is a foundation for sharing and manipulating information using online systems and services.

[0076] "Sharing" refers to the act of users exchanging information with others about their contributions to the environment and their activities, thereby contributing to raising social awareness.

[0077] The embodiments for carrying out the present invention will now be described. This system visualizes the environmental impact based on activity information provided by the user and proposes measures to improve behavior.

[0078] Hardware and software

[0079] The device functions as a device for users to input activity information. Smartphones and tablets are specific examples, and these devices have GPS, Bluetooth, and Wi-Fi capabilities.

[0080] Servers act as central processing units for processing and analyzing large amounts of data. They are often built on cloud-based data processing systems and utilize AI models for data analysis.

[0081] The generative AI model is used to analyze user activity data, calculate air pollutant emissions from those activities, and generate optimal behavioral improvement strategies.

[0082] Data processing and data calculation

[0083] The terminal anonymizes the information entered by the user for security reasons and transmits it to the server via the communication network.

[0084] The server analyzes the received data using an AI model. The data analyzed includes transportation methods and power consumption, and based on this, it calculates air pollutant emissions.

[0085] Visualization involves sending visual data generated by a server to a terminal and displaying it in a format that is easy for the user to understand. This visualization uses graphs and charts based on the data.

[0086] Specific example

[0087] When a user uses their smartphone to input their commute distance from home to work and their mode of transportation, the device sends this information to a server. The server processes the data and calculates the amount of air pollutants emitted by the user's commute. Based on the calculation, it generates a behavioral improvement suggestion, such as "We suggest you try cycling to work once a week," and notifies the user of this suggestion on their device.

[0088] Example of a prompt

[0089] "Consider data on users' commuting activities and propose more environmentally friendly commuting methods."

[0090] This invention enables users to recognize the specific impact their personal activities have on the environment and to transform their behavior into a more sustainable one.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] Users input information about their daily activities into the device. Specifically, they use a smartphone app to provide information about their mode of transportation, distance, power consumption, and purchased products. This information is processed as input to the device.

[0094] Step 2:

[0095] The device receives information provided by the user and processes the data for anonymization. Identifying information is removed, ensuring the data is anonymous. This processed data is encrypted using a communication protocol for secure transmission to the server.

[0096] Step 3:

[0097] The server analyzes the anonymized data received from the terminal. Using a generative AI model as input, it calculates the amount of air pollutants emitted by the user's activities. As a result of this calculation, specific numerical values, such as carbon dioxide emissions, are output.

[0098] Step 4:

[0099] Based on the analysis results, the server generates behavioral improvement strategies tailored to the user's lifestyle. This generating AI model considers the user's past data and patterns to propose the optimal improvement strategy. In this process, the optimal action plan derived from the analysis results is output, which then serves as the input for the next step.

[0100] Step 5:

[0101] The server creates data to visualize the generated improvement measures and sends it to the terminal. It converts this data into a display format, such as graphs and charts, that allows the user to intuitively understand what improvements are possible.

[0102] Step 6:

[0103] The terminal notifies the user of visualization data received from the server and displays specific suggestions for actionable improvements. The user can then choose actions that reduce their environmental impact by following the suggested improvements.

[0104] Step 7:

[0105] The server aggregates user environmental contribution data and converts it into a format that can be shared on a digital platform. Users can use this data to compare their environmental contributions with others and receive feedback.

[0106] (Application Example 1)

[0107] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0108] Achieving a sustainable lifestyle requires understanding the environmental impact of individual actions and proactively changing those actions. However, it is not easy to grasp the specific carbon dioxide emissions generated by everyday modes of transportation and consumption. Furthermore, opportunities to participate in environmental activities through collaboration with local communities are often difficult to obtain. To address these challenges, it is necessary to provide users with a way to easily evaluate their own actions, take appropriate reduction measures, and participate in environmental contribution activities in their local communities.

[0109] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0110] In this invention, the server includes means for collecting activity data from users, means for analyzing the data to calculate and visualize carbon dioxide emissions, means for generating reduction measures based on the analysis results and presenting them to users, means for providing information on environmental activities and encouraging users to participate, means for sharing users' environmental contributions on the network, and means for providing information on local activities and supporting participation in community activities. As a result, users are encouraged to understand the environmental impact of their own actions, actively make environmentally friendly choices, and participate in further environmental protection activities through cooperation with local communities.

[0111] A "user" refers to an entity that uses this system to provide its own activity data and participate in environmental contribution activities.

[0112] "Activity data" refers to data that includes information related to the user's daily activities, such as means of transportation, power consumption, and purchased products.

[0113] "Means for calculating and visualizing carbon dioxide emissions" refers to a function that calculates carbon dioxide emissions based on data collected from users and displays them in an intuitively understandable format.

[0114] "A means of generating and presenting reduction measures to users" refers to a system that, based on analyzed data, devises carbon dioxide reduction methods suited to the user's lifestyle and communicates them in an easy-to-understand manner.

[0115] "Means of providing information on environmental activities and encouraging user participation" refers to a function that filters information on environmental protection activities obtained from the internet and external databases, communicates it to users, and encourages their participation.

[0116] "A means of sharing users' environmental contributions over the network" refers to a function that aggregates the results of users' daily environmental improvement activities and displays them in a way that can be shared with society.

[0117] "A means of providing information on local activities and supporting participation in community activities" refers to a function that presents users with information on environmentally related events held in the local area and supports their participation.

[0118] This system is designed to efficiently collect, analyze, and visualize users' daily activity data. The devices used by users are everyday portable information devices such as smartphones and smart glasses. These devices record user activity data (distance traveled, mode of transportation used, power consumption, etc.) and transmit it securely and anonymously to the server.

[0119] The server processes received data in real time and calculates the user's carbon dioxide emissions. The calculation is performed based on pre-set emission factors, and the results are visualized as graphs and numerical information. This allows users to intuitively understand their environmental impact.

[0120] Furthermore, the server uses machine learning algorithms to analyze the user's activity history and generate carbon dioxide reduction strategies tailored to their individual lifestyle. These strategies are then communicated to the user's device as concise and specific suggestions, such as "commute by bicycle once a week." The server also collects information on local environmental events and filters and directs users to events that might interest them, making it easier for them to participate in community activities.

[0121] Finally, users' environmental contributions will be tallied and made shareable on social networks. This feature will allow users to share their efforts with others and contribute to raising environmental awareness throughout the community.

[0122] For example, if a user enters their transportation data for the month into the app, the server might send a notification saying, "Your total emissions this month are 100kg, and it seems you haven't implemented many reduction measures. Try using public transport once a week next month." This notification helps users consciously change their behavior.

[0123] An example of a prompt message when using a generative AI model might be: "My distance traveled today was 10 kilometers, and my electricity consumption was 5 kilowatts. Based on this, please suggest appropriate CO2 reduction measures."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The device collects data on the user's daily activities. This data includes the mode of transportation, daily distance traveled, and power consumption. The collected data is processed into a secure and anonymous format and sent to the server.

[0127] Step 2:

[0128] The server calculates the user's carbon dioxide emissions based on the received activity data. Here, data calculations are performed using pre-configured emission factors. If the input data is transportation or electricity consumption, the corresponding emissions are added together to output the total emissions. The results are visualized in real time and sent to the terminal as numerical values ​​and graphs.

[0129] Step 3:

[0130] The server generates personalized reduction strategies based on calculated carbon dioxide emissions and past activity history. It uses machine learning algorithms to perform data analysis. This process analyzes the user's existing activity patterns as input and notifies the device of the optimal reduction strategy, such as "commuting by bicycle once a week."

[0131] Step 4:

[0132] The server organizes information on environmental activities and events obtained from the internet and external databases. In this step, the collected information is filtered based on the user's interests and location, and information on events they can participate in is output. The terminal notifies the user of this information and encourages them to participate in the activities.

[0133] Step 5:

[0134] Users adjust their actual actions based on carbon reduction measures and local activity information displayed on their devices. In this step, they review their lifestyle in accordance with the entered reduction measures and improve their environmental contribution.

[0135] Step 6:

[0136] The server will collect user environmental contribution data and make it shareable on social networks. Specifically, it will quantify the contribution based on the input activity data and provide this information as output. Users can easily share this information on the network from their own devices, contributing to raising environmental awareness within the community.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention combines a system that analyzes carbon dioxide emissions based on user activity data and proposes reduction measures with an emotion engine. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and timing of suggestions, thereby promoting behavioral change in the user.

[0139] Users provide activity data using their devices, and emotional data is also collected through voice and image analysis. The emotion engine analyzes the user's voice tone, facial expressions, and entered text to determine their current emotional state. For example, if the system determines that the user is calm, it will suggest detailed reduction measures.

[0140] The server receives both emotional and activity data and calculates carbon dioxide emissions. The calculation results are visualized in a way that aligns with the user's emotional state and are adjusted to be easily understood visually. For example, a relaxed user can be shown a step-by-step action plan.

[0141] Next, the server utilizes an emotion engine to generate appropriate mitigation measures. The emotion engine generates and notifies the user with a message in a tone that matches their emotions. In this process, a short message is added for excited emotions, and a detailed explanation is added for cautious emotions.

[0142] Furthermore, the emotion engine further encourages participation in environmentally related activities based on the user's emotional state. For example, if the user is experiencing positive emotions, it will recommend participation in challenging volunteer activities.

[0143] Ultimately, the server translates the user's environmental contribution into an emotionally resonant message and suggests sharing it on social networks. This allows users to communicate their contributions in a way that resonates with their emotions, thereby raising environmental awareness throughout the community.

[0144] As a concrete example, suppose a user makes a short comment in a tired voice at the end of a busy workday. The emotion engine recognizes this as "fatigue," and the server generates a short, simple suggestion, such as, "You must be tired today. Why not consider volunteering at a nearby event while taking a break?" This entire process makes it possible to encourage sustainable behavior in a way that is most relatable to the user.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] Users input their activity data (such as mode of transportation, distance, and power consumption) using their devices. They also capture their voice and facial expressions via microphones and cameras to collect emotional data.

[0148] Step 2:

[0149] The device anonymizes the collected activity and sentiment data and sends it to the server using a secure communication protocol. This transmission is instantaneous and privacy is protected.

[0150] Step 3:

[0151] The server analyzes the received activity data to calculate carbon dioxide emissions based on the user's activities. Different emission factors are used for each mode of transportation in the calculation.

[0152] Step 4:

[0153] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. For example, it identifies emotions such as "joy," "sadness," and "fatigue" through voice tone and facial expression analysis.

[0154] Step 5:

[0155] The server visualizes the calculated carbon dioxide emissions and adjusts the display format according to the user's emotional state. For example, if the user is relaxed, it will show detailed data in stages.

[0156] Step 6:

[0157] The server generates reduction strategies based on the emotional state using machine learning algorithms. In doing so, it prepares proposals with varying approaches, tailored to the appropriate tone and content for each emotion.

[0158] Step 7:

[0159] The generated reduction measures are notified to the device as emotionally resonant messages. Users are presented with suggestions in an easy-to-understand format at the appropriate time.

[0160] Step 8:

[0161] The server retrieves local environmental event information from the network and external databases, and determines the optimal timing to encourage user participation based on their sentiment.

[0162] Step 9:

[0163] The device provides users with information about environmental events through notifications and suggests activities that match their mood. For example, it might suggest challenging activities to users who are feeling energetic.

[0164] Step 10:

[0165] The server calculates the user's environmental contribution and suggests social networking messages using emotionally appropriate language. This allows users to share their contributions in a way that reflects their own personality.

[0166] Step 11:

[0167] Users review the suggested messages on their devices and post them to social networks. This contributes to raising environmental awareness throughout the community.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] Conventional carbon dioxide emission reduction systems only calculated emissions based on user activity data and presented standard reduction measures. However, this approach offers uniform suggestions without considering each user's emotional state, limiting its effectiveness in user acceptance and promoting behavioral change. Furthermore, it fails to present optimal reduction measures tailored to individual emotional states, resulting in insufficient efforts to raise environmental awareness.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for collecting activity data and emotional state data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for generating and presenting reduction measures adjusted according to the emotional state to the user. This makes it possible to present acceptable and effective reduction measures that take into account the user's emotional state.

[0173] "Activity data" refers to information that indicates a user's physical activity and behavior, including data such as steps taken, distance traveled, and calories burned.

[0174] "Emotional state data" refers to information that indicates a user's emotions and psychological state, extracted from the user's voice tone, facial expressions, text input, etc.

[0175] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted as a result of the user's daily activities, expressed numerically.

[0176] "Visualization" refers to displaying analyzed information in an easy-to-understand format, such as diagrams or graphs, to make it easily comprehensible to users.

[0177] "Reduction measures" refer to specific action plans and methods proposed to reduce a user's carbon dioxide emissions.

[0178] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to perform generation tasks based on specific prompt sentences.

[0179] A "prompt statement" refers to an instruction or question that is input to a generative AI model to prompt it to produce a specific output.

[0180] "Environmental activities" refer to various actions and projects undertaken with the aim of protecting or improving the environment.

[0181] "Environmental contribution" is an indicator or evaluation that shows how much a user's activities have contributed to the environment.

[0182] A "social network" refers to an online platform where users can share information and interact with each other.

[0183] This invention relates to a system that analyzes carbon dioxide emissions and proposes reduction measures using user activity data and emotional state data. This system mainly consists of three elements: the user, the terminal, and the server.

[0184] The terminal is a device that collects data from users using smartphones or wearable devices. Specifically, the terminal collects data on the user's daily physical activity (such as steps taken, distance traveled, and calories burned) as well as emotional state data such as voice tone, facial expressions, and text input.

[0185] The server receives data transmitted from the terminal and calculates carbon dioxide emissions based on it. The server has multiple analysis algorithms and calculates emissions by applying emission factors based on the user's activity data. In addition, the server uses an emotion engine to analyze the user's emotional state in real time and optimize reduction measures.

[0186] For example, the server can present a specific, step-by-step action plan to a user who is detected as relaxed. In this way, the content and timing of the reduction measures are adjusted according to the user's emotions. At that time, the server uses a generative AI model to generate the optimal suggestion based on the prompt text.

[0187] As a concrete example, consider a case where a user leaves a short comment in a tired voice. In this case, the server generates a short and appropriate message such as, "Thank you for your hard work today. Why not consider volunteering at a nearby event while taking a break?" and notifies the user.

[0188] An example of a prompt for the generation AI model is, "If the user's voice tone indicates fatigue, generate a short, concise volunteer activity suggestion." Based on such prompts, the suggestions are effectively optimized.

[0189] As described above, the present invention enables the presentation of flexible reduction measures tailored to the user's emotional state and promotes sustainable behavioral change.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The device collects user data.

[0193] Activity data (e.g., steps taken, distance traveled, calories burned) is acquired as input from the user's smartphone or wearable device. Emotional state data is also collected through voice tone, facial expressions, and entered text.

[0194] In operation, the terminal collects and records this data in real time and then prepares it for transmission to the server. The output is the basic dataset for analysis.

[0195] Step 2:

[0196] The terminal sends data to the server.

[0197] The input consists of activity data and emotional state data collected in Step 1.

[0198] The process involves the terminal encrypting this data and sending it to the server via the secure internet. The output is the securely transmitted data packet.

[0199] Step 3:

[0200] The server analyzes the data.

[0201] The input consists of activity data and emotional state data transmitted from the device.

[0202] In operation, the server uses an analysis algorithm to calculate carbon dioxide emissions and an emotion engine to identify the current emotional state. The server applies emission factors to activity data and analyzes the user's psychological state based on their emotional state. The output is the user's emission figures and an evaluation of their emotional state.

[0203] Step 4:

[0204] The server generates reduction measures.

[0205] The inputs include analyzed carbon dioxide emissions and the results of an assessment of emotional state.

[0206] In operation, the server uses a generative AI model to generate mitigation strategies based on the prompt text. Specifically, suggestions are created that take into account the tone and level of detail corresponding to the emotional state. For example, if the user is analyzed as "relaxed," a step-by-step action plan is generated. The output is a list of mitigation strategies suggested to the user.

[0207] Step 5:

[0208] The server notifies the user of the suggestions it has generated.

[0209] The input consists of the proposed reduction measures generated in step 4.

[0210] The process involves the server formatting the suggestion into a message appropriate to the user's emotional state and sending it to the terminal. The terminal then notifies the user and prompts them to confirm the suggestion. The output is the notification message displayed on the user's terminal.

[0211] Step 6:

[0212] Users receive suggestions and provide feedback on their actions.

[0213] The input consists of proposed reduction measures notified by the server.

[0214] The process involves the user acting on a suggestion and providing feedback by inputting the result into a terminal. The terminal then sends this feedback back to the server, which is used to optimize future suggestions. The output is the user's feedback data.

[0215] (Application Example 2)

[0216] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0217] To achieve a sustainable society, it is crucial for individual users to reduce their carbon dioxide emissions. However, conventional methods have the drawback of offering uniform suggestions, failing to consider users' emotional states, and making effective behavioral change difficult.

[0218] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0219] In this invention, the server includes means for collecting activity data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for analyzing the user's emotional state and adjusting the content and timing of suggestions based on the analysis. This makes it possible to present reduction measures tailored to each user's emotions, thereby promoting more effective behavioral change.

[0220] A "user" is an individual or organization that uses the system to provide data related to reducing carbon dioxide emissions.

[0221] "Activity data" refers to information about users' daily behaviors and consumption patterns.

[0222] "Carbon dioxide emissions" is a numerical representation of the total amount of carbon dioxide released into the environment by user activities.

[0223] "Visualizing" refers to displaying analyzed data in a visually easy-to-understand format.

[0224] "Reduction measures" refer to specific methods and proposals for reducing carbon dioxide emissions.

[0225] "Emotional state" refers to information that indicates the user's mental and emotional condition, and is obtained from sources such as voice and facial expressions.

[0226] A "social network" refers to an online platform on the internet where people share information and interact with each other.

[0227] "Environmental activities" refer to activities and events aimed at environmental protection and improving sustainability.

[0228] This invention is a system realized by collecting activity data through the user's mobile device and having a server analyze it. The user uses a smartphone with the application installed to record activity data and provides emotional states using voice input and camera functions. This data is sent to the server, where the following processing is performed.

[0229] The server first analyzes the activity data sent by the user to calculate the carbon dioxide emissions. This process uses the Carbon Footprint API to calculate emissions related to each activity item in real time. Furthermore, the Emotion Recognition API is used for emotion recognition, analyzing voice tone, facial expressions, and text input to determine the user's emotional state.

[0230] Next, the suggested reduction measures are adjusted based on the sentiment analysis results from the generative AI model. Specifically, suggestions are created in a tone that matches the user's emotional state and are notified to the user via Firebase Cloud Messaging. For example, if the system determines that the user is feeling "tired," it might suggest, "Why not try some eco-friendly activities that can help you relax in a short amount of time?"

[0231] A key feature of this system is that not only the content of the suggestions, but also the timing and tone of the messages are dynamically adjusted to match the user's emotions. Furthermore, with the user's consent, it is possible to share the collected emotional data and environmental contribution on social networks to raise awareness throughout the community.

[0232] In this way, a support system is provided that enables users to easily take sustainable actions in their daily lives. An example of a prompt message is: "Please consider appropriate content to present suggestions for reducing carbon dioxide emissions, customized according to the user's emotional state (e.g., fatigue, excitement, calmness, etc.)."

[0233] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0234] Step 1:

[0235] The user enters activity data into their smartphone. The user inputs information such as mode of transportation, distance, and type of goods consumed into the application. The entered data is temporarily stored on the user's device.

[0236] Step 2:

[0237] The device collects user emotional data using voice input and camera functions. Users provide voice messages and selfies, and their emotional state is determined based on these. This data is also stored on the device and prepared for the next processing step.

[0238] Step 3:

[0239] The device collects activity and sentiment data and sends it to the server. The transmitted data is filtered and encrypted to ensure user anonymity. The data reaches the server securely via the internet.

[0240] Step 4:

[0241] The server calculates carbon dioxide emissions based on the activity data it receives. Using the Carbon Footprint API, it calculates the total emissions using the emission factors assigned to each activity item. Once this calculation result is obtained, the process moves on to the next step.

[0242] Step 5:

[0243] The server analyzes the received emotion data. Using the Emotion Recognition API, it determines the emotional state from voice tone and images. If the analysis determines that the emotion is "fatigue," that information is reflected in the next suggestion generation step.

[0244] Step 6:

[0245] The server uses a generated AI model to propose reduction measures. The suggestions are customized based on the user's emotional state. For example, if the user is judged to be "fatigued," simple, quick reduction measures will be suggested. The messages generated here are used in the next step.

[0246] Step 7:

[0247] The server notifies the user with customized suggestions via Firebase Cloud Messaging. These notifications are delivered in a tone that reflects the user's emotions and are designed to encourage action. The user receives and reviews these notifications on their device.

[0248] Step 8:

[0249] Users who receive notifications will take action based on the suggestions. They will implement the suggested reduction measures and, if necessary, input the results as feedback into the application. This will allow for continued data collection and analysis.

[0250] 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.

[0251] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0252] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0253] [Second Embodiment]

[0254] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0255] 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.

[0256] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0257] 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.

[0258] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0259] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0260] 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.

[0261] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0262] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0263] The 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.

[0264] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0265] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0266] The system of this invention collects and analyzes activity data from users, makes suggestions for environmental improvement, and provides support for users to take sustainable actions.

[0267] In this system, users first provide data to the system either by entering their activity data or through automated collection by a terminal. This activity data includes information such as mode of transportation, power consumption, and purchased products. The terminal transmits this data to the server in a secure and anonymized form.

[0268] The server calculates the user's carbon dioxide emissions based on the received data and visualizes the results in real time. This visualized information is displayed on the user's device in a format that is easy for them to understand. For example, it can be viewed as a graph showing the trend of daily emissions.

[0269] Next, the server generates carbon dioxide reduction strategies tailored to the user's lifestyle based on the analyzed data. This uses machine learning algorithms to generate optimal suggestions, taking into account the user's activity patterns and past data. These reduction strategies are then communicated to the user's device as concise and easy-to-understand messages.

[0270] Furthermore, the system provides information on local environmental activities and events that users can participate in. The server collects information from the internet and external databases and filters events to match the user's interests. The terminal notifies the user and encourages them to participate.

[0271] Finally, a system is provided that aggregates the environmental contributions of users' activities and allows for easy sharing of this data on social networks. Users can share their daily contributions with other network members and contribute to raising environmental awareness within the community.

[0272] As a concrete example, when a user uses the system during their commute, they input their daily commute distance and mode of transportation into the terminal. The server then calculates the carbon dioxide emissions from their commute and suggests "commuting by bicycle once a week" as a concrete reduction measure. In response to this suggestion, the user changes their commuting style, and as a result, the system encourages conscious behavioral change.

[0273] The following describes the processing flow.

[0274] Step 1:

[0275] The user inputs their own activity data into the terminal or automatically records activities using the sensor data of smart devices. This includes recording the user's means of transportation and distance by using location information services.

[0276] Step 2:

[0277] The terminal anonymizes the collected activity data and sends it to the server using a secure protocol. Anonymization is performed to protect the user's privacy.

[0278] Step 3:

[0279] The server analyzes the received data and calculates the carbon dioxide emissions based on the user's activities. Emission factors related to means of transportation and power consumption are used in this calculation.

[0280] Step 4:

[0281] The server visualizes the calculated carbon dioxide emissions and generates them as understandable graphs and charts. This visualized information helps the user intuitively grasp their environmental impact.

[0282] Step 5:

[0283] The visualized information is sent from the server to the terminal, and the user can view the results in real time. This allows the user to confirm the impact of their daily activities on the environment.

[0284] Step 6:

[0285] Based on the analysis results, the server uses machine learning algorithms to generate specific carbon dioxide reduction measures for the user. This proposal includes specific behavior changes (e.g., using public transportation).

[0286] Step 7:

[0287] The server translates the generated reduction suggestions into a message in natural language and sends it to the user's device. The user receives this notification and can review their daily actions.

[0288] Step 8:

[0289] The server retrieves information on local environmental activities from the network and external databases, and filters it based on the user's interests.

[0290] Step 9:

[0291] Filtered activity information is sent from the server to the terminal and notified to the user. This makes it easier for users to participate in activities that interest them.

[0292] Step 10:

[0293] The server aggregates the environmental contribution of users' activities and proposes this data as a post to social networks using a standardized template.

[0294] Step 11:

[0295] Users post information about their proposed environmental contributions to social media via their devices. This allows users to widely inform the community about their contributions and help raise environmental awareness.

[0296] (Example 1)

[0297] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0298] In modern society, it is difficult to quantitatively visualize the impact of individual activities on the environment and encourage concrete behavioral improvements. Furthermore, there is a lack of mechanisms to intuitively understand one's contribution to the environment and raise awareness by sharing it with others. In addition, there is a need to provide highly relevant environmental information while securely handling users' personal information. A comprehensive system is needed to address these challenges.

[0299] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0300] In this invention, the server includes means for collecting activity-related information from the user, means for analyzing the information to calculate and visualize air pollutant emissions, and means for generating and presenting action improvement measures to the user based on the analysis results. This enables individuals to understand the environmental impact of their daily activities and implement concrete and effective improvement measures. Furthermore, sharing environmental contributions through a digital platform is expected to raise awareness throughout society.

[0301] A "user" is an individual or group that uses the system to provide information about their activities and to understand their impact on the environment.

[0302] "Information" refers to data related to a user's activities, including data on means of transportation, power consumption, and purchased products.

[0303] "Air pollutant emissions" refers to the amount of environmentally harmful substances emitted by user activities, and mainly includes carbon dioxide and other greenhouse gases.

[0304] "Visualization" is a method of representing numerical data as graphs and charts so that users can intuitively understand the environmental impact of their activities.

[0305] "Action improvement measures" refer to measures that show specific proposals and guidelines for reducing the impact on the environment based on users' activity data.

[0306] "Digital platform" refers to a foundation for sharing and operating information using online systems and services.

[0307] "Sharing" refers to an act in which users exchange information about their contribution to the environment and their activity content with others, contributing to the improvement of social awareness.

[0308] The embodiments for implementing the present invention will be described. This system visualizes the impact on the environment based on the activity information provided by users and proposes action improvement measures.

[0309] Hardware and Software

[0310] The terminal functions as a device for users to input activity information. Smartphones and tablets are specific examples, and these terminals have GPS, Bluetooth, and Wi-Fi functions.

[0311] The server operates as a central processing unit for processing and analyzing large amounts of data. It is often constructed with a cloud-based data processing system, and an AI model is used for data analysis.

[0312] The generated AI model is used to analyze users' activity data, calculate the emissions of air pollutants due to activities, and generate optimal action improvement measures.

[0313] Data Processing and Data Calculation

[0314] The terminal anonymizes the information input by the user considering security and transmits it to the server through the communication network.

[0315] The server analyzes the received data using an AI model. The data analyzed includes transportation methods and power consumption, and based on this, it calculates air pollutant emissions.

[0316] Visualization involves sending visual data generated by a server to a terminal and displaying it in a format that is easy for the user to understand. This visualization uses graphs and charts based on the data.

[0317] Specific example

[0318] When a user uses their smartphone to input their commute distance from home to work and their mode of transportation, the device sends this information to a server. The server processes the data and calculates the amount of air pollutants emitted by the user's commute. Based on the calculation, it generates a behavioral improvement suggestion, such as "We suggest you try cycling to work once a week," and notifies the user of this suggestion on their device.

[0319] Example of a prompt

[0320] "Consider data on users' commuting activities and propose more environmentally friendly commuting methods."

[0321] This invention enables users to recognize the specific impact their personal activities have on the environment and to transform their behavior into a more sustainable one.

[0322] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0323] Step 1:

[0324] Users input information about their daily activities into the device. Specifically, they use a smartphone app to provide information about their mode of transportation, distance, power consumption, and purchased products. This information is processed as input to the device.

[0325] Step 2:

[0326] The device receives information provided by the user and processes the data for anonymization. Identifying information is removed, ensuring the data is anonymous. This processed data is encrypted using a communication protocol for secure transmission to the server.

[0327] Step 3:

[0328] The server analyzes the anonymized data received from the terminal. Using a generative AI model as input, it calculates the amount of air pollutants emitted by the user's activities. As a result of this calculation, specific numerical values, such as carbon dioxide emissions, are output.

[0329] Step 4:

[0330] Based on the analysis results, the server generates behavioral improvement strategies tailored to the user's lifestyle. This generating AI model considers the user's past data and patterns to propose the optimal improvement strategy. In this process, the optimal action plan derived from the analysis results is output, which then serves as the input for the next step.

[0331] Step 5:

[0332] The server creates data to visualize the generated improvement measures and sends it to the terminal. It converts this data into a display format, such as graphs and charts, that allows the user to intuitively understand what improvements are possible.

[0333] Step 6:

[0334] The terminal notifies the user of visualization data received from the server and displays specific suggestions for actionable improvements. The user can then choose actions that reduce their environmental impact by following the suggested improvements.

[0335] Step 7:

[0336] The server aggregates user environmental contribution data and converts it into a format that can be shared on a digital platform. Users can use this data to compare their environmental contributions with others and receive feedback.

[0337] (Application Example 1)

[0338] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0339] Achieving a sustainable lifestyle requires understanding the environmental impact of individual actions and proactively changing those actions. However, it is not easy to grasp the specific carbon dioxide emissions generated by everyday modes of transportation and consumption. Furthermore, opportunities to participate in environmental activities through collaboration with local communities are often difficult to obtain. To address these challenges, it is necessary to provide users with a way to easily evaluate their own actions, take appropriate reduction measures, and participate in environmental contribution activities in their local communities.

[0340] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0341] In this invention, the server includes means for collecting activity data from users, means for analyzing the data to calculate and visualize carbon dioxide emissions, means for generating reduction measures based on the analysis results and presenting them to users, means for providing information on environmental activities and encouraging users to participate, means for sharing users' environmental contributions on the network, and means for providing information on local activities and supporting participation in community activities. As a result, users are encouraged to understand the environmental impact of their own actions, actively make environmentally friendly choices, and participate in further environmental protection activities through cooperation with local communities.

[0342] A "user" refers to an entity that uses this system to provide its own activity data and participate in environmental contribution activities.

[0343] "Activity data" refers to data that includes information related to the user's daily activities, such as means of transportation, power consumption, and purchased products.

[0344] "Means for calculating and visualizing carbon dioxide emissions" refers to a function that calculates carbon dioxide emissions based on data collected from users and displays them in an intuitively understandable format.

[0345] "A means of generating and presenting reduction measures to users" refers to a system that, based on analyzed data, devises carbon dioxide reduction methods suited to the user's lifestyle and communicates them in an easy-to-understand manner.

[0346] "Means of providing information on environmental activities and encouraging user participation" refers to a function that filters information on environmental protection activities obtained from the internet and external databases, communicates it to users, and encourages their participation.

[0347] "A means of sharing users' environmental contributions over the network" refers to a function that aggregates the results of users' daily environmental improvement activities and displays them in a way that can be shared with society.

[0348] "A means of providing information on local activities and supporting participation in community activities" refers to a function that presents users with information on environmentally related events held in the local area and supports their participation.

[0349] This system is designed to efficiently collect, analyze, and visualize users' daily activity data. The devices used by users are everyday portable information devices such as smartphones and smart glasses. These devices record user activity data (distance traveled, mode of transportation used, power consumption, etc.) and transmit it securely and anonymously to the server.

[0350] The server processes received data in real time and calculates the user's carbon dioxide emissions. The calculation is performed based on pre-set emission factors, and the results are visualized as graphs and numerical information. This allows users to intuitively understand their environmental impact.

[0351] Furthermore, the server uses machine learning algorithms to analyze the user's activity history and generate carbon dioxide reduction strategies tailored to their individual lifestyle. These strategies are then communicated to the user's device as concise and specific suggestions, such as "commute by bicycle once a week." The server also collects information on local environmental events and filters and directs users to events that might interest them, making it easier for them to participate in community activities.

[0352] Finally, users' environmental contributions will be tallied and made shareable on social networks. This feature will allow users to share their efforts with others and contribute to raising environmental awareness throughout the community.

[0353] For example, if a user enters their transportation data for the month into the app, the server might send a notification saying, "Your total emissions this month are 100kg, and it seems you haven't implemented many reduction measures. Try using public transport once a week next month." This notification helps users consciously change their behavior.

[0354] An example of a prompt message when using a generative AI model might be: "My distance traveled today was 10 kilometers, and my electricity consumption was 5 kilowatts. Based on this, please suggest appropriate CO2 reduction measures."

[0355] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0356] Step 1:

[0357] The device collects data on the user's daily activities. This data includes the mode of transportation, daily distance traveled, and power consumption. The collected data is processed into a secure and anonymous format and sent to the server.

[0358] Step 2:

[0359] The server calculates the user's carbon dioxide emissions based on the received activity data. Here, data calculations are performed using pre-configured emission factors. If the input data is transportation or electricity consumption, the corresponding emissions are added together to output the total emissions. The results are visualized in real time and sent to the terminal as numerical values ​​and graphs.

[0360] Step 3:

[0361] The server generates personalized reduction strategies based on calculated carbon dioxide emissions and past activity history. It uses machine learning algorithms to perform data analysis. This process analyzes the user's existing activity patterns as input and notifies the device of the optimal reduction strategy, such as "commuting by bicycle once a week."

[0362] Step 4:

[0363] The server organizes information on environmental activities and events obtained from the internet and external databases. In this step, the collected information is filtered based on the user's interests and location, and information on events they can participate in is output. The terminal notifies the user of this information and encourages them to participate in the activities.

[0364] Step 5:

[0365] Users adjust their actual actions based on carbon reduction measures and local activity information displayed on their devices. In this step, they review their lifestyle in accordance with the entered reduction measures and improve their environmental contribution.

[0366] Step 6:

[0367] The server will collect user environmental contribution data and make it shareable on social networks. Specifically, it will quantify the contribution based on the input activity data and provide this information as output. Users can easily share this information on the network from their own devices, contributing to raising environmental awareness within the community.

[0368] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0369] This invention combines a system that analyzes carbon dioxide emissions based on user activity data and proposes reduction measures with an emotion engine. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and timing of suggestions, thereby promoting behavioral change in the user.

[0370] Users provide activity data using their devices, and emotional data is also collected through voice and image analysis. The emotion engine analyzes the user's voice tone, facial expressions, and entered text to determine their current emotional state. For example, if the system determines that the user is calm, it will suggest detailed reduction measures.

[0371] The server receives both emotional and activity data and calculates carbon dioxide emissions. The calculation results are visualized in a way that aligns with the user's emotional state and are adjusted to be easily understood visually. For example, a relaxed user can be shown a step-by-step action plan.

[0372] Next, the server utilizes an emotion engine to generate appropriate mitigation measures. The emotion engine generates and notifies the user with a message in a tone that matches their emotions. In this case, a short message is added for excited emotions, and a detailed explanation is added for cautious emotions.

[0373] Furthermore, the emotion engine further encourages participation in environmentally related activities based on the user's emotional state. For example, if the user is experiencing positive emotions, it will recommend participation in challenging volunteer activities.

[0374] Ultimately, the server translates the user's environmental contribution into an emotionally resonant message and suggests sharing it on social networks. This allows users to communicate their contributions in a way that resonates with their emotions, thereby raising environmental awareness throughout the community.

[0375] As a concrete example, suppose a user leaves a short comment in a tired voice at the end of a busy workday. The emotion engine recognizes this as "fatigue," and the server generates a short, simple suggestion, such as, "You must be tired today. Why not consider volunteering at a nearby event while taking a break?" This entire process makes it possible to encourage sustainable behavior in a way that is most relatable to the user.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] Users input their activity data (such as mode of transportation, distance, and power consumption) using their device. They also capture their voice and facial expressions via microphones and cameras to collect emotional data.

[0379] Step 2:

[0380] The device anonymizes the collected activity and sentiment data and sends it to the server using a secure communication protocol. This transmission is instantaneous and privacy is protected.

[0381] Step 3:

[0382] The server analyzes the received activity data to calculate carbon dioxide emissions based on the user's activities. Different emission factors are used for each mode of transportation in the calculation.

[0383] Step 4:

[0384] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. For example, it identifies emotions such as "joy," "sadness," and "fatigue" through voice tone and facial expression analysis.

[0385] Step 5:

[0386] The server visualizes the calculated carbon dioxide emissions and adjusts the display format according to the user's emotional state. For example, if the user is relaxed, it will show detailed data in stages.

[0387] Step 6:

[0388] The server uses machine learning algorithms to generate reduction strategies tailored to the emotional state. In doing so, it prepares proposals with varying approaches, using tones and content appropriate to the emotional state.

[0389] Step 7:

[0390] The generated reduction measures are notified to the device as emotionally resonant messages. Users are presented with suggestions in an easy-to-understand format at the appropriate time.

[0391] Step 8:

[0392] The server retrieves local environmental event information from the network and external databases, and determines the optimal timing to encourage user participation based on their sentiment.

[0393] Step 9:

[0394] The device provides users with information about environmental events through notifications and suggests activities that match their mood. For example, it might suggest challenging activities to users who are feeling energetic.

[0395] Step 10:

[0396] The server calculates the user's environmental contribution and suggests social networking messages using emotionally appropriate language. This allows users to share their contributions in a way that reflects their own personality.

[0397] Step 11:

[0398] Users review the suggested messages on their devices and post them to social networks. This contributes to raising environmental awareness throughout the community.

[0399] (Example 2)

[0400] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0401] Conventional carbon dioxide emission reduction systems only calculated emissions based on user activity data and presented standard reduction measures. However, this approach offers uniform suggestions without considering each user's emotional state, limiting its effectiveness in user acceptance and promoting behavioral change. Furthermore, it fails to present optimal reduction measures tailored to individual emotional states, resulting in insufficient efforts to raise environmental awareness.

[0402] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0403] In this invention, the server includes means for collecting activity data and emotional state data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for generating and presenting reduction measures adjusted according to the emotional state to the user. This makes it possible to present acceptable and effective reduction measures that take into account the user's emotional state.

[0404] "Activity data" refers to information that indicates a user's physical activity and behavior, including data such as steps taken, distance traveled, and calories burned.

[0405] "Emotional state data" refers to information that indicates a user's emotions and psychological state, extracted from the user's voice tone, facial expressions, text input, etc.

[0406] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted as a result of the user's daily activities, expressed numerically.

[0407] "Visualization" refers to displaying analyzed information in an easy-to-understand format, such as diagrams or graphs, so that users can easily comprehend it.

[0408] "Reduction measures" refer to specific action plans and methods proposed to reduce a user's carbon dioxide emissions.

[0409] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to perform generation tasks based on specific prompt sentences.

[0410] A "prompt statement" refers to an instruction or question that is input to a generative AI model to prompt it to produce a specific output.

[0411] "Environmental activities" refer to various actions and projects undertaken with the aim of protecting or improving the environment.

[0412] "Environmental contribution" is an indicator or evaluation that shows how much a user's activities have contributed to the environment.

[0413] A "social network" refers to an online platform where users can share information and interact with each other.

[0414] This invention relates to a system that analyzes carbon dioxide emissions and proposes reduction measures using user activity data and emotional state data. This system mainly consists of three elements: the user, the terminal, and the server.

[0415] The terminal is a device that collects data from users using smartphones or wearable devices. Specifically, the terminal collects data on the user's daily physical activity (such as steps taken, distance traveled, and calories burned) as well as emotional state data such as voice tone, facial expressions, and text input.

[0416] The server receives data transmitted from the terminal and calculates carbon dioxide emissions based on it. The server has multiple analysis algorithms and calculates emissions by applying emission factors based on the user's activity data. In addition, the server uses an emotion engine to analyze the user's emotional state in real time and optimize reduction measures.

[0417] For example, the server can present a specific, step-by-step action plan to a user who is detected as relaxed. In this way, the content and timing of the reduction measures are adjusted according to the user's emotions. At that time, the server uses a generative AI model to generate the optimal suggestion based on the prompt text.

[0418] As a concrete example, consider a case where a user leaves a short comment in a tired voice. In this case, the server generates a short and appropriate message such as, "Thank you for your hard work today. Why not consider volunteering at a nearby event while taking a break?" and notifies the user.

[0419] An example of a prompt for the generation AI model is, "If the user's voice tone indicates fatigue, generate a short, concise volunteer activity suggestion." Based on such prompts, the suggestions are effectively optimized.

[0420] As described above, the present invention enables the presentation of flexible reduction measures tailored to the user's emotional state and promotes sustainable behavioral change.

[0421] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0422] Step 1:

[0423] The device collects user data.

[0424] Activity data (e.g., steps taken, distance traveled, calories burned) is acquired as input from the user's smartphone or wearable device. Emotional state data is also collected through voice tone, facial expressions, and entered text.

[0425] In operation, the terminal collects and records this data in real time and then prepares it for transmission to the server. The output is the basic dataset for analysis.

[0426] Step 2:

[0427] The terminal sends data to the server.

[0428] The input consists of activity data and emotional state data collected in Step 1.

[0429] The process involves the terminal encrypting this data and sending it to the server via the secure internet. The output is the securely transmitted data packet.

[0430] Step 3:

[0431] The server analyzes the data.

[0432] The input consists of activity data and emotional state data transmitted from the device.

[0433] In operation, the server uses an analysis algorithm to calculate carbon dioxide emissions and an emotion engine to identify the user's current emotional state. The server applies emission factors to activity data and analyzes the user's psychological state based on their emotional state. The output is the user's emission figures and an evaluation of their emotional state.

[0434] Step 4:

[0435] The server generates reduction measures.

[0436] The inputs include analyzed carbon dioxide emissions and the results of an assessment of emotional state.

[0437] In operation, the server uses a generative AI model to generate mitigation strategies based on the prompt text. Specifically, suggestions are created that take into account the tone and level of detail corresponding to the emotional state. For example, if the user is analyzed as "relaxed," a step-by-step action plan is generated. The output is a list of mitigation strategies suggested to the user.

[0438] Step 5:

[0439] The server notifies the user of the suggestions it has generated.

[0440] The input consists of the proposed reduction measures generated in step 4.

[0441] The process involves the server formatting the suggestion into a message appropriate to the user's emotional state and sending it to the terminal. The terminal then notifies the user and prompts them to confirm the suggestion. The output is the notification message displayed on the user's terminal.

[0442] Step 6:

[0443] Users receive suggestions and provide feedback on their actions.

[0444] The input consists of proposed reduction measures notified by the server.

[0445] The process involves the user acting on a suggestion and providing feedback by inputting the result into a terminal. The terminal then sends this feedback back to the server, which is used to optimize future suggestions. The output is the user's feedback data.

[0446] (Application Example 2)

[0447] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0448] To achieve a sustainable society, it is crucial for individual users to reduce their carbon dioxide emissions. However, conventional methods have the drawback of offering uniform suggestions, failing to consider users' emotional states, and making effective behavioral change difficult.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0450] In this invention, the server includes means for collecting activity data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for analyzing the user's emotional state and adjusting the content and timing of suggestions based on the analysis. This makes it possible to present reduction measures tailored to each user's emotions, thereby promoting more effective behavioral change.

[0451] A "user" is an individual or organization that uses the system to provide data related to reducing carbon dioxide emissions.

[0452] "Activity data" refers to information about users' daily behaviors and consumption patterns.

[0453] "Carbon dioxide emissions" is a numerical representation of the total amount of carbon dioxide released into the environment by user activities.

[0454] "Visualizing" refers to displaying analyzed data in a visually easy-to-understand format.

[0455] "Reduction measures" refer to specific methods and proposals for reducing carbon dioxide emissions.

[0456] "Emotional state" refers to information that indicates the user's mental and emotional condition, and is obtained from sources such as voice and facial expressions.

[0457] A "social network" refers to an online platform on the internet where people share information and interact with each other.

[0458] "Environmental activities" refer to activities and events aimed at environmental protection and improving sustainability.

[0459] This invention is a system realized by collecting activity data through the user's mobile device and having a server analyze it. The user uses a smartphone with the application installed to record activity data and provides emotional states using voice input and camera functions. This data is sent to the server, where the following processing is performed.

[0460] The server first analyzes the activity data sent by the user to calculate the carbon dioxide emissions. This process uses the Carbon Footprint API to calculate emissions related to each activity item in real time. Furthermore, the Emotion Recognition API is used for emotion recognition, analyzing voice tone, facial expressions, and text input to determine the user's emotional state.

[0461] Next, the suggested reduction measures are adjusted based on the sentiment analysis results from the generative AI model. Specifically, suggestions are created in a tone that matches the user's emotional state and are notified to the user via Firebase Cloud Messaging. For example, if the system determines that the user is feeling "tired," it might suggest, "Why not try some eco-friendly activities that can help you relax in a short amount of time?"

[0462] A key feature of this system is that not only the content of the suggestions, but also the timing and tone of the messages are dynamically adjusted to match the user's emotions. Furthermore, with the user's consent, it is possible to share the collected emotional data and environmental contribution on social networks to raise awareness throughout the community.

[0463] In this way, a support system is provided that enables users to easily take sustainable actions in their daily lives. An example of a prompt message is: "Please consider appropriate content to present suggestions for reducing carbon dioxide emissions, customized according to the user's emotional state (e.g., fatigue, excitement, calmness, etc.)."

[0464] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0465] Step 1:

[0466] The user enters activity data into their smartphone. The user inputs information such as mode of transportation, distance, and type of goods consumed into the application. The entered data is temporarily stored on the user's device.

[0467] Step 2:

[0468] The device collects user emotional data using voice input and camera functions. Users provide voice messages and selfies, and their emotional state is determined based on these. This data is also stored on the device and prepared for the next processing step.

[0469] Step 3:

[0470] The device collects activity and sentiment data and sends it to the server. The transmitted data is filtered and encrypted to ensure user anonymity. The data reaches the server securely via the internet.

[0471] Step 4:

[0472] The server calculates carbon dioxide emissions based on the activity data it receives. Using the Carbon Footprint API, it calculates the total emissions using the emission factors assigned to each activity item. Once this calculation result is obtained, the process moves on to the next step.

[0473] Step 5:

[0474] The server analyzes the received emotion data. Using the Emotion Recognition API, it determines the emotional state from voice tone and images. If the analysis determines that the emotion is "fatigue," that information is reflected in the next suggestion generation step.

[0475] Step 6:

[0476] The server uses a generated AI model to propose reduction measures. The suggestions are customized based on the user's emotional state. For example, if the user is judged to be "fatigued," simple, quick reduction measures will be suggested. The messages generated here are used in the next step.

[0477] Step 7:

[0478] The server notifies the user with customized suggestions via Firebase Cloud Messaging. These notifications are delivered in a tone that reflects the user's emotions and are designed to encourage action. The user receives and reviews these notifications on their device.

[0479] Step 8:

[0480] Users who receive notifications will take action based on the suggestions. They will implement the suggested reduction measures and, if necessary, input the results as feedback into the application. This will allow for continued data collection and analysis.

[0481] 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.

[0482] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0483] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0484] [Third Embodiment]

[0485] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0486] 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.

[0487] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0488] 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.

[0489] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0490] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0491] 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.

[0492] 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.

[0493] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0494] The 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.

[0495] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0496] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0497] The system of this invention collects and analyzes activity data from users, makes suggestions for environmental improvement, and provides support for users to take sustainable actions.

[0498] In this system, users first provide data to the system either by entering their activity data or through automated collection by a terminal. This activity data includes information such as mode of transportation, power consumption, and purchased products. The terminal transmits this data to the server in a secure and anonymized form.

[0499] The server calculates the user's carbon dioxide emissions based on the received data and visualizes the results in real time. This visualized information is displayed on the user's device in a format that is easy for them to understand. For example, it can be viewed as a graph showing the trend of daily emissions.

[0500] Next, the server generates carbon dioxide reduction strategies tailored to the user's lifestyle based on the analyzed data. This uses machine learning algorithms to generate optimal suggestions, taking into account the user's activity patterns and past data. These reduction strategies are then communicated to the user's device as concise and easy-to-understand messages.

[0501] Furthermore, the system provides information on local environmental activities and events that users can participate in. The server collects information from the internet and external databases and filters events to match the user's interests. The terminal notifies the user and encourages them to participate.

[0502] Finally, a system is provided that aggregates the environmental contributions of users' activities and allows for easy sharing of this data on social networks. Users can share their daily contributions with other network members and contribute to raising environmental awareness within the community.

[0503] As a concrete example, when a user uses the system during their commute, they input their daily commute distance and mode of transportation into the terminal. The server then calculates the carbon dioxide emissions from their commute and suggests "commuting by bicycle once a week" as a concrete reduction measure. In response to this suggestion, the user changes their commuting style, and as a result, the system encourages conscious behavioral change.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] Users can either input their activity data into their device or have their activities automatically recorded using sensor data from their smart devices. This includes recording the user's means of transportation and distance traveled through the use of location services.

[0507] Step 2:

[0508] The device anonymizes the collected activity data and sends it to the server using a secure protocol. Anonymization is performed to protect user privacy.

[0509] Step 3:

[0510] The server analyzes the received data and calculates carbon dioxide emissions based on the user's activities. This calculation uses emission factors related to the mode of transportation and power consumption.

[0511] Step 4:

[0512] The server visualizes the calculated carbon dioxide emissions and generates them as easy-to-understand graphs and charts. This visualized information helps users intuitively understand their own environmental impact.

[0513] Step 5:

[0514] The visualized information is sent from the server to the terminal, and users can view the results in real time. This allows users to see the impact their daily activities have on the environment.

[0515] Step 6:

[0516] Based on the analysis results, the server uses machine learning algorithms to generate specific carbon dioxide reduction measures for the user. These suggestions may include specific behavioral changes (e.g., using public transportation).

[0517] Step 7:

[0518] The server translates the generated reduction suggestions into a message in natural language and sends it to the user's device. The user receives this notification and can review their daily actions.

[0519] Step 8:

[0520] The server retrieves information on local environmental activities from the network and external databases, and filters it based on the user's interests.

[0521] Step 9:

[0522] Filtered activity information is sent from the server to the terminal and notified to the user. This makes it easier for users to participate in activities that interest them.

[0523] Step 10:

[0524] The server aggregates the environmental contribution of users' activities and proposes this data as a post to social networks using a standardized template.

[0525] Step 11:

[0526] Users post information about their proposed environmental contributions to social media via their devices. This allows users to widely inform the community about their contributions and help raise environmental awareness.

[0527] (Example 1)

[0528] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0529] In modern society, it is difficult to quantitatively visualize the impact of individual activities on the environment and encourage concrete behavioral improvements. Furthermore, there is a lack of mechanisms to intuitively understand one's contribution to the environment and raise awareness by sharing it with others. In addition, there is a need to provide highly relevant environmental information while securely handling users' personal information. A comprehensive system is needed to address these challenges.

[0530] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0531] In this invention, the server includes means for collecting activity-related information from the user, means for analyzing the information to calculate and visualize air pollutant emissions, and means for generating and presenting action improvement measures to the user based on the analysis results. This enables individuals to understand the environmental impact of their daily activities and implement concrete and effective improvement measures. Furthermore, sharing environmental contributions through a digital platform is expected to raise awareness throughout society.

[0532] A "user" is an individual or group that uses the system to provide information about their activities and to understand their impact on the environment.

[0533] "Information" refers to data related to a user's activities, including data on means of transportation, power consumption, and purchased products.

[0534] "Air pollutant emissions" refers to the amount of environmentally harmful substances emitted by user activities, and mainly includes carbon dioxide and other greenhouse gases.

[0535] "Visualization" is a method of representing numerical data as graphs and charts so that users can intuitively understand the environmental impact of their activities.

[0536] "Behavioral improvement measures" are actions that, based on user activity data, provide specific suggestions and policies to reduce the environmental impact.

[0537] A "digital platform" is a foundation for sharing and manipulating information using online systems and services.

[0538] "Sharing" refers to the act of users exchanging information with others about their contributions to the environment and their activities, thereby contributing to raising social awareness.

[0539] The embodiments for carrying out the present invention will now be described. This system visualizes the environmental impact based on activity information provided by the user and proposes measures to improve behavior.

[0540] Hardware and software

[0541] The device functions as a device for users to input activity information. Smartphones and tablets are specific examples, and these devices have GPS, Bluetooth, and Wi-Fi capabilities.

[0542] Servers act as central processing units for processing and analyzing large amounts of data. They are often built on cloud-based data processing systems and utilize AI models for data analysis.

[0543] The generative AI model is used to analyze user activity data, calculate air pollutant emissions from those activities, and generate optimal behavioral improvement strategies.

[0544] Data processing and data calculation

[0545] The terminal anonymizes the information entered by the user for security reasons and transmits it to the server via the communication network.

[0546] The server analyzes the received data using an AI model. The data analyzed includes transportation methods and power consumption, and based on this, it calculates air pollutant emissions.

[0547] Visualization involves sending visual data generated by a server to a terminal and displaying it in a format that is easy for the user to understand. This visualization uses graphs and charts based on the data.

[0548] Specific example

[0549] When a user uses their smartphone to input their commute distance from home to work and their mode of transportation, the device sends this information to a server. The server processes the data and calculates the amount of air pollutants emitted by the user's commute. Based on the calculation, it generates a behavioral improvement suggestion, such as "We suggest you try cycling to work once a week," and notifies the user of this suggestion on their device.

[0550] Example of a prompt

[0551] "Consider data on users' commuting activities and propose more environmentally friendly commuting methods."

[0552] This invention enables users to recognize the specific impact their personal activities have on the environment and to transform their behavior into a more sustainable one.

[0553] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0554] Step 1:

[0555] Users input information about their daily activities into the device. Specifically, they use a smartphone app to provide information about their mode of transportation, distance, power consumption, and purchased products. This information is processed as input to the device.

[0556] Step 2:

[0557] The device receives information provided by the user and processes the data for anonymization. Identifying information is removed, ensuring the data is anonymous. This processed data is encrypted using a communication protocol for secure transmission to the server.

[0558] Step 3:

[0559] The server analyzes the anonymized data received from the terminal. Using a generative AI model as input, it calculates the amount of air pollutants emitted by the user's activities. As a result of this calculation, specific numerical values, such as carbon dioxide emissions, are output.

[0560] Step 4:

[0561] Based on the analysis results, the server generates behavioral improvement strategies tailored to the user's lifestyle. This generating AI model considers the user's past data and patterns to propose the optimal improvement strategy. In this process, the optimal action plan derived from the analysis results is output, which then serves as the input for the next step.

[0562] Step 5:

[0563] The server creates data to visualize the generated improvement measures and sends it to the terminal. It converts this data into a display format, such as graphs and charts, that allows the user to intuitively understand what improvements are possible.

[0564] Step 6:

[0565] The terminal notifies the user of visualization data received from the server and displays specific suggestions for actionable improvements. The user can then choose actions that reduce their environmental impact by following the suggested improvements.

[0566] Step 7:

[0567] The server aggregates user environmental contribution data and converts it into a format that can be shared on a digital platform. Users can use this data to compare their environmental contributions with others and receive feedback.

[0568] (Application Example 1)

[0569] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0570] Achieving a sustainable lifestyle requires understanding the environmental impact of individual actions and proactively changing those actions. However, it is not easy to grasp the specific carbon dioxide emissions generated by everyday modes of transportation and consumption. Furthermore, opportunities to participate in environmental activities through collaboration with local communities are often difficult to obtain. To address these challenges, it is necessary to provide users with a way to easily evaluate their own actions, take appropriate reduction measures, and participate in environmental contribution activities in their local communities.

[0571] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0572] In this invention, the server includes means for collecting activity data from users, means for analyzing the data to calculate and visualize carbon dioxide emissions, means for generating reduction measures based on the analysis results and presenting them to users, means for providing information on environmental activities and encouraging users to participate, means for sharing users' environmental contributions on the network, and means for providing information on local activities and supporting participation in community activities. As a result, users are encouraged to understand the environmental impact of their own actions, actively make environmentally friendly choices, and participate in further environmental protection activities through cooperation with local communities.

[0573] A "user" refers to an entity that uses this system to provide its own activity data and participate in environmental contribution activities.

[0574] "Activity data" refers to data that includes information related to the user's daily activities, such as means of transportation, power consumption, and purchased products.

[0575] "Means for calculating and visualizing carbon dioxide emissions" refers to a function that calculates carbon dioxide emissions based on data collected from users and displays them in an intuitively understandable format.

[0576] "A means of generating and presenting reduction measures to users" refers to a system that, based on analyzed data, devises carbon dioxide reduction methods suited to the user's lifestyle and communicates them in an easy-to-understand manner.

[0577] "Means of providing information on environmental activities and encouraging user participation" refers to a function that filters information on environmental protection activities obtained from the internet and external databases, communicates it to users, and encourages their participation.

[0578] "A means of sharing users' environmental contributions over the network" refers to a function that aggregates the results of users' daily environmental improvement activities and displays them in a way that can be shared with society.

[0579] "A means of providing information on local activities and supporting participation in community activities" refers to a function that presents users with information on environmentally related events held in the local area and supports their participation.

[0580] This system is designed to efficiently collect, analyze, and visualize users' daily activity data. The devices used by users are everyday portable information devices such as smartphones and smart glasses. These devices record user activity data (distance traveled, mode of transportation used, power consumption, etc.) and transmit it securely and anonymously to the server.

[0581] The server processes received data in real time and calculates the user's carbon dioxide emissions. The calculation is performed based on pre-set emission factors, and the results are visualized as graphs and numerical information. This allows users to intuitively understand their environmental impact.

[0582] Furthermore, the server uses machine learning algorithms to analyze the user's activity history and generate carbon dioxide reduction strategies tailored to their individual lifestyle. These strategies are then communicated to the user's device as concise and specific suggestions, such as "commute by bicycle once a week." The server also collects information on local environmental events and filters and directs users to events that might interest them, making it easier for them to participate in community activities.

[0583] Finally, users' environmental contributions will be tallied and made shareable on social networks. This feature will allow users to share their efforts with others and contribute to raising environmental awareness throughout the community.

[0584] For example, if a user enters their transportation data for the month into the app, the server might send a notification saying, "Your total emissions this month are 100kg, and it seems you haven't implemented many reduction measures. Try using public transport once a week next month." This notification helps users consciously change their behavior.

[0585] An example of a prompt message when using a generative AI model might be: "My distance traveled today was 10 kilometers, and my electricity consumption was 5 kilowatts. Based on this, please suggest appropriate CO2 reduction measures."

[0586] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0587] Step 1:

[0588] The device collects data on the user's daily activities. This data includes the mode of transportation, daily distance traveled, and power consumption. The collected data is processed into a secure and anonymous format and sent to the server.

[0589] Step 2:

[0590] The server calculates the user's carbon dioxide emissions based on the received activity data. Here, data calculations are performed using pre-configured emission factors. If the input data is transportation or electricity consumption, the corresponding emissions are added together to output the total emissions. The results are visualized in real time and sent to the terminal as numerical values ​​and graphs.

[0591] Step 3:

[0592] The server generates personalized reduction strategies based on calculated carbon dioxide emissions and past activity history. It uses machine learning algorithms to perform data analysis. This process analyzes the user's existing activity patterns as input and notifies the device of the optimal reduction strategy, such as "commuting by bicycle once a week."

[0593] Step 4:

[0594] The server organizes information on environmental activities and events obtained from the internet and external databases. In this step, the collected information is filtered based on the user's interests and location, and information on events they can participate in is output. The terminal notifies the user of this information and encourages them to participate in the activities.

[0595] Step 5:

[0596] Users adjust their actual actions based on carbon reduction measures and local activity information displayed on their devices. In this step, they review their lifestyle in accordance with the entered reduction measures and improve their environmental contribution.

[0597] Step 6:

[0598] The server will collect user environmental contribution data and make it shareable on social networks. Specifically, it will quantify the contribution based on the input activity data and provide this information as output. Users can easily share this information on the network from their own devices, contributing to raising environmental awareness within the community.

[0599] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0600] This invention combines a system that analyzes carbon dioxide emissions based on user activity data and proposes reduction measures with an emotion engine. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and timing of suggestions, thereby promoting behavioral change in the user.

[0601] Users provide activity data using their devices, and emotional data is also collected through voice and image analysis. The emotion engine analyzes the user's voice tone, facial expressions, and entered text to determine their current emotional state. For example, if the system determines that the user is calm, it will suggest detailed reduction measures.

[0602] The server receives both emotional and activity data and calculates carbon dioxide emissions. The calculation results are visualized in a way that aligns with the user's emotional state and are adjusted to be easily understood visually. For example, a relaxed user can be shown a step-by-step action plan.

[0603] Next, the server utilizes an emotion engine to generate appropriate mitigation measures. The emotion engine generates and notifies the user with a message in a tone that matches their emotions. In this process, a short message is added for excited emotions, and a detailed explanation is added for cautious emotions.

[0604] Furthermore, the emotion engine further encourages participation in environmentally related activities based on the user's emotional state. For example, if the user is experiencing positive emotions, it will recommend participation in challenging volunteer activities.

[0605] Ultimately, the server translates the user's environmental contribution into an emotionally resonant message and suggests sharing it on social networks. This allows users to communicate their contributions in a way that resonates with their emotions, thereby raising environmental awareness throughout the community.

[0606] As a concrete example, suppose a user makes a short comment in a tired voice at the end of a busy workday. The emotion engine recognizes this as "fatigue," and the server generates a short, simple suggestion, such as, "You must be tired today. Why not consider volunteering at a nearby event while taking a break?" This entire process makes it possible to encourage sustainable behavior in a way that is most relatable to the user.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] Users input their activity data (such as mode of transportation, distance, and power consumption) using their devices. They also capture their voice and facial expressions via microphones and cameras to collect emotional data.

[0610] Step 2:

[0611] The device anonymizes the collected activity and sentiment data and sends it to the server using a secure communication protocol. This transmission is instantaneous and privacy is protected.

[0612] Step 3:

[0613] The server analyzes the received activity data to calculate carbon dioxide emissions based on the user's activities. Different emission factors are used for each mode of transportation in the calculation.

[0614] Step 4:

[0615] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. For example, it identifies emotions such as "joy," "sadness," and "fatigue" through voice tone and facial expression analysis.

[0616] Step 5:

[0617] The server visualizes the calculated carbon dioxide emissions and adjusts the display format according to the user's emotional state. For example, if the user is relaxed, it will show detailed data in stages.

[0618] Step 6:

[0619] The server generates reduction strategies based on the emotional state using machine learning algorithms. In doing so, it prepares proposals with varying approaches, tailored to the appropriate tone and content for each emotion.

[0620] Step 7:

[0621] The generated reduction measures are notified to the device as emotionally resonant messages. Users are presented with suggestions in an easy-to-understand format at the appropriate time.

[0622] Step 8:

[0623] The server retrieves local environmental event information from the network and external databases, and determines the optimal timing to encourage user participation based on their sentiment.

[0624] Step 9:

[0625] The device provides users with information about environmental events through notifications and suggests activities that match their mood. For example, it might suggest challenging activities to users who are feeling energetic.

[0626] Step 10:

[0627] The server calculates the user's environmental contribution and suggests social networking messages using emotionally appropriate language. This allows users to share their contributions in a way that reflects their own personality.

[0628] Step 11:

[0629] Users review the suggested messages on their devices and post them to social networks. This contributes to raising environmental awareness throughout the community.

[0630] (Example 2)

[0631] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0632] Conventional carbon dioxide emission reduction systems only calculated emissions based on user activity data and presented standard reduction measures. However, this approach offers uniform suggestions without considering each user's emotional state, limiting its effectiveness in user acceptance and promoting behavioral change. Furthermore, it fails to present optimal reduction measures tailored to individual emotional states, resulting in insufficient efforts to raise environmental awareness.

[0633] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0634] In this invention, the server includes means for collecting activity data and emotional state data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for generating and presenting reduction measures adjusted according to the emotional state to the user. This makes it possible to present acceptable and effective reduction measures that take into account the user's emotional state.

[0635] "Activity data" refers to information that indicates a user's physical activity and behavior, including data such as steps taken, distance traveled, and calories burned.

[0636] "Emotional state data" refers to information that indicates a user's emotions and psychological state, extracted from the user's voice tone, facial expressions, text input, etc.

[0637] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted as a result of the user's daily activities, expressed numerically.

[0638] "Visualization" refers to displaying analyzed information in an easy-to-understand format, such as diagrams or graphs, to make it easily comprehensible to users.

[0639] "Reduction measures" refer to specific action plans and methods proposed to reduce a user's carbon dioxide emissions.

[0640] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to perform generation tasks based on specific prompt sentences.

[0641] A "prompt statement" refers to an instruction or question that is input to a generative AI model to prompt it to produce a specific output.

[0642] "Environmental activities" refer to various actions and projects undertaken with the aim of protecting or improving the environment.

[0643] "Environmental contribution" is an indicator or evaluation that shows how much a user's activities have contributed to the environment.

[0644] A "social network" refers to an online platform where users can share information and interact with each other.

[0645] This invention relates to a system that analyzes carbon dioxide emissions and proposes reduction measures using user activity data and emotional state data. This system mainly consists of three elements: the user, the terminal, and the server.

[0646] The terminal is a device that collects data from users using smartphones or wearable devices. Specifically, the terminal collects data on the user's daily physical activity (such as steps taken, distance traveled, and calories burned) as well as emotional state data such as voice tone, facial expressions, and text input.

[0647] The server receives data transmitted from the terminal and calculates carbon dioxide emissions based on it. The server has multiple analysis algorithms and calculates emissions by applying emission factors based on the user's activity data. In addition, the server uses an emotion engine to analyze the user's emotional state in real time and optimize reduction measures.

[0648] For example, the server can present a specific, step-by-step action plan to a user who is detected as relaxed. In this way, the content and timing of the reduction measures are adjusted according to the user's emotions. At that time, the server uses a generative AI model to generate the optimal suggestion based on the prompt text.

[0649] As a concrete example, consider a case where a user leaves a short comment in a tired voice. In this case, the server generates a short and appropriate message such as, "Thank you for your hard work today. Why not consider volunteering at a nearby event while taking a break?" and notifies the user.

[0650] An example of a prompt for the generation AI model is, "If the user's voice tone indicates fatigue, generate a short, concise volunteer activity suggestion." Based on such prompts, the suggestions are effectively optimized.

[0651] As described above, the present invention enables the presentation of flexible reduction measures tailored to the user's emotional state and promotes sustainable behavioral change.

[0652] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0653] Step 1:

[0654] The device collects user data.

[0655] Activity data (e.g., steps taken, distance traveled, calories burned) is acquired as input from the user's smartphone or wearable device. Emotional state data is also collected through voice tone, facial expressions, and entered text.

[0656] In operation, the terminal collects and records this data in real time and then prepares it for transmission to the server. The output is the basic dataset for analysis.

[0657] Step 2:

[0658] The terminal sends data to the server.

[0659] The input consists of activity data and emotional state data collected in Step 1.

[0660] The process involves the terminal encrypting this data and sending it to the server via the secure internet. The output is the securely transmitted data packet.

[0661] Step 3:

[0662] The server analyzes the data.

[0663] The input consists of activity data and emotional state data transmitted from the device.

[0664] In operation, the server uses an analysis algorithm to calculate carbon dioxide emissions and an emotion engine to identify the current emotional state. The server applies emission factors to activity data and analyzes the user's psychological state based on their emotional state. The output is the user's emission figures and an evaluation of their emotional state.

[0665] Step 4:

[0666] The server generates reduction measures.

[0667] The inputs include analyzed carbon dioxide emissions and the results of an assessment of emotional state.

[0668] In operation, the server uses a generative AI model to generate mitigation strategies based on the prompt text. Specifically, suggestions are created that take into account the tone and level of detail corresponding to the emotional state. For example, if the user is analyzed as "relaxed," a step-by-step action plan is generated. The output is a list of mitigation strategies suggested to the user.

[0669] Step 5:

[0670] The server notifies the user of the suggestions it has generated.

[0671] The input consists of the proposed reduction measures generated in step 4.

[0672] The process involves the server formatting the suggestion into a message appropriate to the user's emotional state and sending it to the terminal. The terminal then notifies the user and prompts them to confirm the suggestion. The output is the notification message displayed on the user's terminal.

[0673] Step 6:

[0674] Users receive suggestions and provide feedback on their actions.

[0675] The input consists of proposed reduction measures notified by the server.

[0676] The process involves the user acting on a suggestion and providing feedback by inputting the result into a terminal. The terminal then sends this feedback back to the server, which is used to optimize future suggestions. The output is the user's feedback data.

[0677] (Application Example 2)

[0678] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0679] To achieve a sustainable society, it is crucial for individual users to reduce their carbon dioxide emissions. However, conventional methods have the drawback of offering uniform suggestions, failing to consider users' emotional states, and making effective behavioral change difficult.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0681] In this invention, the server includes means for collecting activity data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for analyzing the user's emotional state and adjusting the content and timing of suggestions based on the analysis. This makes it possible to present reduction measures tailored to each user's emotions, thereby promoting more effective behavioral change.

[0682] A "user" is an individual or organization that uses the system to provide data related to reducing carbon dioxide emissions.

[0683] "Activity data" refers to information about users' daily behaviors and consumption patterns.

[0684] "Carbon dioxide emissions" is a numerical representation of the total amount of carbon dioxide released into the environment by user activities.

[0685] "Visualizing" refers to displaying analyzed data in a visually easy-to-understand format.

[0686] "Reduction measures" refer to specific methods and proposals for reducing carbon dioxide emissions.

[0687] "Emotional state" refers to information that indicates the user's mental and emotional condition, and is obtained from sources such as voice and facial expressions.

[0688] A "social network" refers to an online platform on the internet where people share information and interact with each other.

[0689] "Environmental activities" refer to activities and events aimed at environmental protection and improving sustainability.

[0690] This invention is a system realized by collecting activity data through the user's mobile device and having a server analyze it. The user uses a smartphone with the application installed to record activity data and provides emotional states using voice input and camera functions. This data is sent to the server, where the following processing is performed.

[0691] The server first analyzes the activity data sent by the user to calculate the carbon dioxide emissions. This process uses the Carbon Footprint API to calculate emissions related to each activity item in real time. Furthermore, the Emotion Recognition API is used for emotion recognition, analyzing voice tone, facial expressions, and text input to determine the user's emotional state.

[0692] Next, the suggested reduction measures are adjusted based on the sentiment analysis results from the generative AI model. Specifically, suggestions are created in a tone that matches the user's emotional state and are notified to the user via Firebase Cloud Messaging. For example, if the system determines that the user is feeling "tired," it might suggest, "Why not try some eco-friendly activities that can help you relax in a short amount of time?"

[0693] A key feature of this system is that not only the content of the suggestions, but also the timing and tone of the messages are dynamically adjusted to match the user's emotions. Furthermore, with the user's consent, it is possible to share the collected emotional data and environmental contribution on social networks to raise awareness throughout the community.

[0694] In this way, a support system is provided that enables users to easily take sustainable actions in their daily lives. An example of a prompt message is: "Please consider appropriate content to present suggestions for reducing carbon dioxide emissions, customized according to the user's emotional state (e.g., fatigue, excitement, calmness, etc.)."

[0695] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0696] Step 1:

[0697] The user enters activity data into their smartphone. The user inputs information such as mode of transportation, distance, and type of goods consumed into the application. The entered data is temporarily stored on the user's device.

[0698] Step 2:

[0699] The device collects user emotional data using voice input and camera functions. Users provide voice messages and selfies, and their emotional state is determined based on these. This data is also stored on the device and prepared for the next processing step.

[0700] Step 3:

[0701] The device collects activity and sentiment data and sends it to the server. The transmitted data is filtered and encrypted to ensure user anonymity. The data reaches the server securely via the internet.

[0702] Step 4:

[0703] The server calculates carbon dioxide emissions based on the activity data it receives. Using the Carbon Footprint API, it calculates the total emissions using the emission factors assigned to each activity item. Once this calculation result is obtained, the process moves on to the next step.

[0704] Step 5:

[0705] The server analyzes the received emotion data. Using the Emotion Recognition API, it determines the emotional state from voice tone and images. If the analysis determines that the emotion is "fatigue," that information is reflected in the next suggestion generation step.

[0706] Step 6:

[0707] The server uses a generated AI model to propose reduction measures. The suggestions are customized based on the user's emotional state. For example, if the user is judged to be "fatigued," simple, quick reduction measures will be suggested. The messages generated here are used in the next step.

[0708] Step 7:

[0709] The server notifies the user with customized suggestions via Firebase Cloud Messaging. These notifications are delivered in a tone that reflects the user's emotions and are designed to encourage action. The user receives and reviews these notifications on their device.

[0710] Step 8:

[0711] Users who receive notifications will take action based on the suggestions. They will implement the suggested reduction measures and, if necessary, input the results as feedback into the application. This will allow for continued data collection and analysis.

[0712] 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.

[0713] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0714] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0715] [Fourth Embodiment]

[0716] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0717] 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.

[0718] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0719] 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.

[0720] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0721] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0722] 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.

[0723] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0724] 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.

[0725] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0726] The 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.

[0727] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0728] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] The system of this invention collects and analyzes activity data from users, makes suggestions for environmental improvement, and provides support for users to take sustainable actions.

[0730] In this system, users first provide data to the system either by entering their activity data or through automated collection by a terminal. This activity data includes information such as mode of transportation, power consumption, and purchased products. The terminal transmits this data to the server in a secure and anonymized form.

[0731] The server calculates the user's carbon dioxide emissions based on the received data and visualizes the results in real time. This visualized information is displayed on the user's device in a format that is easy for them to understand. For example, it can be viewed as a graph showing the trend of daily emissions.

[0732] Next, the server generates carbon dioxide reduction strategies tailored to the user's lifestyle based on the analyzed data. This uses machine learning algorithms to generate optimal suggestions, taking into account the user's activity patterns and past data. These reduction strategies are then communicated to the user's device as concise and easy-to-understand messages.

[0733] Furthermore, the system provides information on local environmental activities and events that users can participate in. The server collects information from the internet and external databases and filters events to match the user's interests. The terminal notifies the user and encourages them to participate.

[0734] Finally, a system is provided that aggregates the environmental contributions of users' activities and allows for easy sharing of this data on social networks. Users can share their daily contributions with other network members and contribute to raising environmental awareness within the community.

[0735] As a concrete example, when a user uses the system during their commute, they input their daily commute distance and mode of transportation into the terminal. The server then calculates the carbon dioxide emissions from their commute and suggests "commuting by bicycle once a week" as a concrete reduction measure. In response to this suggestion, the user changes their commuting style, and as a result, the system encourages conscious behavioral change.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] Users can either input their activity data into their device or have their activities automatically recorded using sensor data from their smart devices. This includes recording the user's means of transportation and distance traveled through the use of location services.

[0739] Step 2:

[0740] The device anonymizes the collected activity data and sends it to the server using a secure protocol. Anonymization is performed to protect user privacy.

[0741] Step 3:

[0742] The server analyzes the received data and calculates carbon dioxide emissions based on the user's activities. This calculation uses emission factors related to the mode of transportation and power consumption.

[0743] Step 4:

[0744] The server visualizes the calculated carbon dioxide emissions and generates them as easy-to-understand graphs and charts. This visualized information helps users intuitively understand their own environmental impact.

[0745] Step 5:

[0746] The visualized information is sent from the server to the terminal, and users can view the results in real time. This allows users to see the impact their daily activities have on the environment.

[0747] Step 6:

[0748] Based on the analysis results, the server uses machine learning algorithms to generate specific carbon dioxide reduction measures for the user. These suggestions may include specific behavioral changes (e.g., using public transportation).

[0749] Step 7:

[0750] The server translates the generated reduction suggestions into a message in natural language and sends it to the user's device. The user receives this notification and can review their daily actions.

[0751] Step 8:

[0752] The server retrieves information on local environmental activities from the network and external databases, and filters it based on the user's interests.

[0753] Step 9:

[0754] Filtered activity information is sent from the server to the terminal and notified to the user. This makes it easier for users to participate in activities that interest them.

[0755] Step 10:

[0756] The server aggregates the environmental contribution of users' activities and proposes this data as a post to social networks using a standardized template.

[0757] Step 11:

[0758] Users post information about their proposed environmental contributions to social media via their devices. This allows users to widely inform the community about their contributions and help raise environmental awareness.

[0759] (Example 1)

[0760] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0761] In modern society, it is difficult to quantitatively visualize the impact of individual activities on the environment and encourage concrete behavioral improvements. Furthermore, there is a lack of mechanisms to intuitively understand one's contribution to the environment and raise awareness by sharing it with others. In addition, there is a need to provide highly relevant environmental information while securely handling users' personal information. A comprehensive system is needed to address these challenges.

[0762] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0763] In this invention, the server includes means for collecting activity-related information from the user, means for analyzing the information to calculate and visualize air pollutant emissions, and means for generating and presenting action improvement measures to the user based on the analysis results. This enables individuals to understand the environmental impact of their daily activities and implement concrete and effective improvement measures. Furthermore, sharing environmental contributions through a digital platform is expected to raise awareness throughout society.

[0764] A "user" is an individual or group that uses the system to provide information about their activities and to understand their impact on the environment.

[0765] "Information" refers to data related to a user's activities, including data on means of transportation, power consumption, and purchased products.

[0766] "Air pollutant emissions" refers to the amount of environmentally harmful substances emitted by user activities, and mainly includes carbon dioxide and other greenhouse gases.

[0767] "Visualization" is a method of representing numerical data as graphs and charts so that users can intuitively understand the environmental impact of their activities.

[0768] "Behavioral improvement measures" are actions that, based on user activity data, provide specific suggestions and policies to reduce the environmental impact.

[0769] A "digital platform" is a foundation for sharing and manipulating information using online systems and services.

[0770] "Sharing" refers to the act of users exchanging information with others about their contributions to the environment and their activities, thereby contributing to raising social awareness.

[0771] The embodiments for carrying out the present invention will now be described. This system visualizes the environmental impact based on activity information provided by the user and proposes measures to improve behavior.

[0772] Hardware and software

[0773] The device functions as a device for users to input activity information. Smartphones and tablets are specific examples, and these devices have GPS, Bluetooth, and Wi-Fi capabilities.

[0774] Servers act as central processing units for processing and analyzing large amounts of data. They are often built on cloud-based data processing systems and utilize AI models for data analysis.

[0775] The generative AI model is used to analyze user activity data, calculate air pollutant emissions from those activities, and generate optimal behavioral improvement strategies.

[0776] Data processing and data calculation

[0777] The terminal anonymizes the information entered by the user for security reasons and transmits it to the server via the communication network.

[0778] The server analyzes the received data using an AI model. The data analyzed includes transportation methods and power consumption, and based on this, it calculates air pollutant emissions.

[0779] Visualization involves sending visual data generated by a server to a terminal and displaying it in a format that is easy for the user to understand. This visualization uses graphs and charts based on the data.

[0780] Specific example

[0781] When a user uses their smartphone to input their commute distance from home to work and their mode of transportation, the device sends this information to a server. The server processes the data and calculates the amount of air pollutants emitted by the user's commute. Based on the calculation, it generates a behavioral improvement suggestion, such as "We suggest you try cycling to work once a week," and notifies the user of this suggestion on their device.

[0782] Example of a prompt

[0783] "Consider data on users' commuting activities and propose more environmentally friendly commuting methods."

[0784] This invention enables users to recognize the specific impact their personal activities have on the environment and to transform their behavior into a more sustainable one.

[0785] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0786] Step 1:

[0787] Users input information about their daily activities into the device. Specifically, they use a smartphone app to provide information about their mode of transportation, distance, power consumption, and purchased products. This information is processed as input to the device.

[0788] Step 2:

[0789] The device receives information provided by the user and processes the data for anonymization. Identifying information is removed, ensuring the data is anonymous. This processed data is encrypted using a communication protocol for secure transmission to the server.

[0790] Step 3:

[0791] The server analyzes the anonymized data received from the terminal. Using a generative AI model as input, it calculates the amount of air pollutants emitted by the user's activities. As a result of this calculation, specific numerical values, such as carbon dioxide emissions, are output.

[0792] Step 4:

[0793] Based on the analysis results, the server generates behavioral improvement strategies tailored to the user's lifestyle. This generating AI model considers the user's past data and patterns to propose the optimal improvement strategy. In this process, the optimal action plan derived from the analysis results is output, which then serves as the input for the next step.

[0794] Step 5:

[0795] The server creates data to visualize the generated improvement measures and sends it to the terminal. It converts this data into a display format, such as graphs and charts, that allows the user to intuitively understand what improvements are possible.

[0796] Step 6:

[0797] The terminal notifies the user of visualization data received from the server and displays specific suggestions for actionable improvements. The user can then choose actions that reduce their environmental impact by following the suggested improvements.

[0798] Step 7:

[0799] The server aggregates user environmental contribution data and converts it into a format that can be shared on a digital platform. Users can use this data to compare their environmental contributions with others and receive feedback.

[0800] (Application Example 1)

[0801] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0802] Achieving a sustainable lifestyle requires understanding the environmental impact of individual actions and proactively changing those actions. However, it is not easy to grasp the specific carbon dioxide emissions generated by everyday modes of transportation and consumption. Furthermore, opportunities to participate in environmental activities through collaboration with local communities are often difficult to obtain. To address these challenges, it is necessary to provide users with a way to easily evaluate their own actions, take appropriate reduction measures, and participate in environmental contribution activities in their local communities.

[0803] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0804] In this invention, the server includes means for collecting activity data from users, means for analyzing the data to calculate and visualize carbon dioxide emissions, means for generating reduction measures based on the analysis results and presenting them to users, means for providing information on environmental activities and encouraging users to participate, means for sharing users' environmental contributions on the network, and means for providing information on local activities and supporting participation in community activities. As a result, users are encouraged to understand the environmental impact of their own actions, actively make environmentally friendly choices, and participate in further environmental protection activities through cooperation with local communities.

[0805] A "user" refers to an entity that uses this system to provide its own activity data and participate in environmental contribution activities.

[0806] "Activity data" refers to data that includes information related to the user's daily activities, such as means of transportation, power consumption, and purchased products.

[0807] "Means for calculating and visualizing carbon dioxide emissions" refers to a function that calculates carbon dioxide emissions based on data collected from users and displays them in an intuitively understandable format.

[0808] "A means of generating and presenting reduction measures to users" refers to a system that, based on analyzed data, devises carbon dioxide reduction methods suited to the user's lifestyle and communicates them in an easy-to-understand manner.

[0809] "Means of providing information on environmental activities and encouraging user participation" refers to a function that filters information on environmental protection activities obtained from the internet and external databases, communicates it to users, and encourages their participation.

[0810] "A means of sharing users' environmental contributions over the network" refers to a function that aggregates the results of users' daily environmental improvement activities and displays them in a way that can be shared with society.

[0811] "A means of providing information on local activities and supporting participation in community activities" refers to a function that presents users with information on environmentally related events held in the local area and supports their participation.

[0812] This system is designed to efficiently collect, analyze, and visualize users' daily activity data. The devices used by users are everyday portable information devices such as smartphones and smart glasses. These devices record user activity data (distance traveled, mode of transportation used, power consumption, etc.) and transmit it securely and anonymously to the server.

[0813] The server processes received data in real time and calculates the user's carbon dioxide emissions. The calculation is performed based on pre-set emission factors, and the results are visualized as graphs and numerical information. This allows users to intuitively understand their environmental impact.

[0814] Furthermore, the server uses machine learning algorithms to analyze the user's activity history and generate carbon dioxide reduction strategies tailored to their individual lifestyle. These strategies are then communicated to the user's device as concise and specific suggestions, such as "commute by bicycle once a week." The server also collects information on local environmental events and filters and directs users to events that might interest them, making it easier for them to participate in community activities.

[0815] Finally, users' environmental contributions will be tallied and made shareable on social networks. This feature will allow users to share their efforts with others and contribute to raising environmental awareness throughout the community.

[0816] For example, if a user enters their transportation data for the month into the app, the server might send a notification saying, "Your total emissions this month are 100kg, and it seems you haven't implemented many reduction measures. Try using public transport once a week next month." This notification helps users consciously change their behavior.

[0817] An example of a prompt message when using a generative AI model might be: "My distance traveled today was 10 kilometers, and my electricity consumption was 5 kilowatts. Based on this, please suggest appropriate CO2 reduction measures."

[0818] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0819] Step 1:

[0820] The device collects data on the user's daily activities. This data includes the mode of transportation, daily distance traveled, and power consumption. The collected data is processed into a secure and anonymous format and sent to the server.

[0821] Step 2:

[0822] The server calculates the user's carbon dioxide emissions based on the received activity data. Here, data calculations are performed using pre-configured emission factors. If the input data is transportation or electricity consumption, the corresponding emissions are added together to output the total emissions. The results are visualized in real time and sent to the terminal as numerical values ​​and graphs.

[0823] Step 3:

[0824] The server generates personalized reduction strategies based on calculated carbon dioxide emissions and past activity history. It uses machine learning algorithms to perform data analysis. This process analyzes the user's existing activity patterns as input and notifies the device of the optimal reduction strategy, such as "commuting by bicycle once a week."

[0825] Step 4:

[0826] The server organizes information on environmental activities and events obtained from the internet and external databases. In this step, the collected information is filtered based on the user's interests and location, and information on events they can participate in is output. The terminal notifies the user of this information and encourages them to participate in the activities.

[0827] Step 5:

[0828] Users adjust their actual actions based on carbon reduction measures and local activity information displayed on their devices. In this step, they review their lifestyle in accordance with the entered reduction measures and improve their environmental contribution.

[0829] Step 6:

[0830] The server will collect user environmental contribution data and make it shareable on social networks. Specifically, it will quantify the contribution based on the input activity data and provide this information as output. Users can easily share this information on the network from their own devices, contributing to raising environmental awareness within the community.

[0831] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0832] This invention combines a system that analyzes carbon dioxide emissions based on user activity data and proposes reduction measures with an emotion engine. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and timing of suggestions, thereby promoting behavioral change in the user.

[0833] Users provide activity data using their devices, and emotional data is also collected through voice and image analysis. The emotion engine analyzes the user's voice tone, facial expressions, and entered text to determine their current emotional state. For example, if the system determines that the user is calm, it will suggest detailed reduction measures.

[0834] The server receives both emotional and activity data and calculates carbon dioxide emissions. The calculation results are visualized in a way that aligns with the user's emotional state and are adjusted to be easily understood visually. For example, a relaxed user can be shown a step-by-step action plan.

[0835] Next, the server utilizes an emotion engine to generate appropriate mitigation measures. The emotion engine generates and notifies the user with a message in a tone that matches their emotions. In this process, a short message is added for excited emotions, and a detailed explanation is added for cautious emotions.

[0836] Furthermore, the emotion engine further encourages participation in environmentally related activities based on the user's emotional state. For example, if the user is experiencing positive emotions, it will recommend participation in challenging volunteer activities.

[0837] Ultimately, the server translates the user's environmental contribution into an emotionally resonant message and suggests sharing it on social networks. This allows users to communicate their contributions in a way that resonates with their emotions, thereby raising environmental awareness throughout the community.

[0838] As a concrete example, suppose a user makes a short comment in a tired voice at the end of a busy workday. The emotion engine recognizes this as "fatigue," and the server generates a short, simple suggestion, such as, "You must be tired today. Why not consider volunteering at a nearby event while taking a break?" This entire process makes it possible to encourage sustainable behavior in a way that is most relatable to the user.

[0839] The following describes the processing flow.

[0840] Step 1:

[0841] Users input their activity data (such as mode of transportation, distance, and power consumption) using their devices. They also capture their voice and facial expressions via microphones and cameras to collect emotional data.

[0842] Step 2:

[0843] The device anonymizes the collected activity and sentiment data and sends it to the server using a secure communication protocol. This transmission is instantaneous and privacy is protected.

[0844] Step 3:

[0845] The server analyzes the received activity data to calculate carbon dioxide emissions based on the user's activities. Different emission factors are used for each mode of transportation in the calculation.

[0846] Step 4:

[0847] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. For example, it identifies emotions such as "joy," "sadness," and "fatigue" through voice tone and facial expression analysis.

[0848] Step 5:

[0849] The server visualizes the calculated carbon dioxide emissions and adjusts the display format according to the user's emotional state. For example, if the user is relaxed, it will show detailed data in stages.

[0850] Step 6:

[0851] The server generates reduction strategies based on the emotional state using machine learning algorithms. In doing so, it prepares proposals with varying approaches, tailored to the appropriate tone and content for each emotion.

[0852] Step 7:

[0853] The generated reduction measures are notified to the device as emotionally resonant messages. Users are presented with suggestions in an easy-to-understand format at the appropriate time.

[0854] Step 8:

[0855] The server retrieves local environmental event information from the network and external databases, and determines the optimal timing to encourage user participation based on their sentiment.

[0856] Step 9:

[0857] The device provides users with information about environmental events through notifications and suggests activities that match their mood. For example, it might suggest challenging activities to users who are feeling energetic.

[0858] Step 10:

[0859] The server calculates the user's environmental contribution and suggests social networking messages using emotionally appropriate language. This allows users to share their contributions in a way that reflects their own personality.

[0860] Step 11:

[0861] Users review the suggested messages on their devices and post them to social networks. This contributes to raising environmental awareness throughout the community.

[0862] (Example 2)

[0863] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0864] Conventional carbon dioxide emission reduction systems only calculated emissions based on user activity data and presented standard reduction measures. However, this approach offers uniform suggestions without considering each user's emotional state, limiting its effectiveness in user acceptance and promoting behavioral change. Furthermore, it fails to present optimal reduction measures tailored to individual emotional states, resulting in insufficient efforts to raise environmental awareness.

[0865] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0866] In this invention, the server includes means for collecting activity data and emotional state data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for generating and presenting reduction measures adjusted according to the emotional state to the user. This makes it possible to present acceptable and effective reduction measures that take into account the user's emotional state.

[0867] "Activity data" refers to information that indicates a user's physical activity and behavior, including data such as steps taken, distance traveled, and calories burned.

[0868] "Emotional state data" refers to information that indicates a user's emotions and psychological state, extracted from the user's voice tone, facial expressions, text input, etc.

[0869] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted as a result of the user's daily activities, expressed numerically.

[0870] "Visualization" refers to displaying analyzed information in an easy-to-understand format, such as diagrams or graphs, to make it easily comprehensible to users.

[0871] "Reduction measures" refer to specific action plans and methods proposed to reduce a user's carbon dioxide emissions.

[0872] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to perform generation tasks based on specific prompt sentences.

[0873] A "prompt statement" refers to an instruction or question that is input to a generative AI model to prompt it to produce a specific output.

[0874] "Environmental activities" refer to various actions and projects undertaken with the aim of protecting or improving the environment.

[0875] "Environmental contribution" is an indicator or evaluation that shows how much a user's activities have contributed to the environment.

[0876] A "social network" refers to an online platform where users can share information and interact with each other.

[0877] This invention relates to a system that analyzes carbon dioxide emissions and proposes reduction measures using user activity data and emotional state data. This system mainly consists of three elements: the user, the terminal, and the server.

[0878] The terminal is a device that collects data from users using smartphones or wearable devices. Specifically, the terminal collects data on the user's daily physical activity (such as steps taken, distance traveled, and calories burned) as well as emotional state data such as voice tone, facial expressions, and text input.

[0879] The server receives data transmitted from the terminal and calculates carbon dioxide emissions based on it. The server has multiple analysis algorithms and calculates emissions by applying emission factors based on the user's activity data. In addition, the server uses an emotion engine to analyze the user's emotional state in real time and optimize reduction measures.

[0880] For example, the server can present a specific, step-by-step action plan to a user who is detected as relaxed. In this way, the content and timing of the reduction measures are adjusted according to the user's emotions. At that time, the server uses a generative AI model to generate the optimal suggestion based on the prompt text.

[0881] As a concrete example, consider a case where a user leaves a short comment in a tired voice. In this case, the server generates a short and appropriate message such as, "Thank you for your hard work today. Why not consider volunteering at a nearby event while taking a break?" and notifies the user.

[0882] An example of a prompt for the generation AI model is, "If the user's voice tone indicates fatigue, generate a short, concise volunteer activity suggestion." Based on such prompts, the suggestions are effectively optimized.

[0883] As described above, the present invention enables the presentation of flexible reduction measures tailored to the user's emotional state and promotes sustainable behavioral change.

[0884] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0885] Step 1:

[0886] The device collects user data.

[0887] Activity data (e.g., steps taken, distance traveled, calories burned) is acquired as input from the user's smartphone or wearable device. Emotional state data is also collected through voice tone, facial expressions, and entered text.

[0888] In operation, the terminal collects and records this data in real time and then prepares it for transmission to the server. The output is the basic dataset for analysis.

[0889] Step 2:

[0890] The terminal sends data to the server.

[0891] The input consists of activity data and emotional state data collected in Step 1.

[0892] The process involves the terminal encrypting this data and sending it to the server via the secure internet. The output is the securely transmitted data packet.

[0893] Step 3:

[0894] The server analyzes the data.

[0895] The input consists of activity data and emotional state data transmitted from the device.

[0896] In operation, the server uses an analysis algorithm to calculate carbon dioxide emissions and an emotion engine to identify the current emotional state. The server applies emission factors to activity data and analyzes the user's psychological state based on their emotional state. The output is the user's emission figures and an evaluation of their emotional state.

[0897] Step 4:

[0898] The server generates reduction measures.

[0899] The inputs include analyzed carbon dioxide emissions and the results of an assessment of emotional state.

[0900] In operation, the server uses a generative AI model to generate mitigation strategies based on the prompt text. Specifically, suggestions are created that take into account the tone and level of detail corresponding to the emotional state. For example, if the user is analyzed as "relaxed," a step-by-step action plan is generated. The output is a list of mitigation strategies suggested to the user.

[0901] Step 5:

[0902] The server notifies the user of the suggestions it has generated.

[0903] The input consists of the proposed reduction measures generated in step 4.

[0904] The process involves the server formatting the suggestion into a message appropriate to the user's emotional state and sending it to the terminal. The terminal then notifies the user and prompts them to confirm the suggestion. The output is the notification message displayed on the user's terminal.

[0905] Step 6:

[0906] Users receive suggestions and provide feedback on their actions.

[0907] The input consists of proposed reduction measures notified by the server.

[0908] The process involves the user acting on a suggestion and providing feedback by inputting the result into a terminal. The terminal then sends this feedback back to the server, which is used to optimize future suggestions. The output is the user's feedback data.

[0909] (Application Example 2)

[0910] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0911] To achieve a sustainable society, it is crucial for individual users to reduce their carbon dioxide emissions. However, conventional methods have the drawback of offering uniform suggestions, failing to consider users' emotional states, and making effective behavioral change difficult.

[0912] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0913] In this invention, the server includes means for collecting activity data from the user, means for analyzing the data to calculate and visualize carbon dioxide emissions, and means for analyzing the user's emotional state and adjusting the content and timing of suggestions based on the analysis. This makes it possible to present reduction measures tailored to each user's emotions, thereby promoting more effective behavioral change.

[0914] A "user" is an individual or organization that uses the system to provide data related to reducing carbon dioxide emissions.

[0915] "Activity data" refers to information about users' daily behaviors and consumption patterns.

[0916] "Carbon dioxide emissions" is a numerical representation of the total amount of carbon dioxide released into the environment by user activities.

[0917] "Visualizing" refers to displaying analyzed data in a visually easy-to-understand format.

[0918] "Reduction measures" refer to specific methods and proposals for reducing carbon dioxide emissions.

[0919] "Emotional state" refers to information that indicates the user's mental and emotional condition, and is obtained from sources such as voice and facial expressions.

[0920] A "social network" refers to an online platform on the internet where people share information and interact with each other.

[0921] "Environmental activities" refer to activities and events aimed at environmental protection and improving sustainability.

[0922] This invention is a system realized by collecting activity data through the user's mobile device and having a server analyze it. The user uses a smartphone with the application installed to record activity data and provides emotional states using voice input and camera functions. This data is sent to the server, where the following processing is performed.

[0923] The server first analyzes the activity data sent by the user to calculate the carbon dioxide emissions. This process uses the Carbon Footprint API to calculate emissions related to each activity item in real time. Furthermore, the Emotion Recognition API is used for emotion recognition, analyzing voice tone, facial expressions, and text input to determine the user's emotional state.

[0924] Next, the suggested reduction measures are adjusted based on the sentiment analysis results from the generative AI model. Specifically, suggestions are created in a tone that matches the user's emotional state and are notified to the user via Firebase Cloud Messaging. For example, if the system determines that the user is feeling "tired," it might suggest, "Why not try some eco-friendly activities that can help you relax in a short amount of time?"

[0925] A key feature of this system is that not only the content of the suggestions, but also the timing and tone of the messages are dynamically adjusted to match the user's emotions. Furthermore, with the user's consent, it is possible to share the collected emotional data and environmental contribution on social networks to raise awareness throughout the community.

[0926] In this way, a support system is provided that enables users to easily take sustainable actions in their daily lives. An example of a prompt message is: "Please consider appropriate content to present suggestions for reducing carbon dioxide emissions, customized according to the user's emotional state (e.g., fatigue, excitement, calmness, etc.)."

[0927] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0928] Step 1:

[0929] The user enters activity data into their smartphone. The user inputs information such as mode of transportation, distance, and type of goods consumed into the application. The entered data is temporarily stored on the user's device.

[0930] Step 2:

[0931] The device collects user emotional data using voice input and camera functions. Users provide voice messages and selfies, and their emotional state is determined based on these. This data is also stored on the device and prepared for the next processing step.

[0932] Step 3:

[0933] The device collects activity and sentiment data and sends it to the server. The transmitted data is filtered and encrypted to ensure user anonymity. The data reaches the server securely via the internet.

[0934] Step 4:

[0935] The server calculates carbon dioxide emissions based on the activity data it receives. Using the Carbon Footprint API, it calculates the total emissions using the emission factors assigned to each activity item. Once this calculation result is obtained, the process moves on to the next step.

[0936] Step 5:

[0937] The server analyzes the received emotion data. Using the Emotion Recognition API, it determines the emotional state from voice tone and images. If the analysis determines that the emotion is "fatigue," that information is reflected in the next suggestion generation step.

[0938] Step 6:

[0939] The server uses a generated AI model to propose reduction measures. The suggestions are customized based on the user's emotional state. For example, if the user is judged to be "fatigued," simple, quick reduction measures will be suggested. The messages generated here are used in the next step.

[0940] Step 7:

[0941] The server notifies the user with customized suggestions via Firebase Cloud Messaging. These notifications are delivered in a tone that reflects the user's emotions and are designed to encourage action. The user receives and reviews these notifications on their device.

[0942] Step 8:

[0943] Users who receive notifications will take action based on the suggestions. They will implement the suggested reduction measures and, if necessary, input the results as feedback into the application. This will allow for continued data collection and analysis.

[0944] 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.

[0945] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0946] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0947] 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.

[0948] Figure 9 shows an 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.

[0949] 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.

[0950] 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.

[0951] 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, motorcycles, etc., 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, for example, based 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.

[0952] 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."

[0953] 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.

[0954] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0955] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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 the like 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.

[0964] 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.

[0965] The following is further disclosed regarding the embodiments described above.

[0966] (Claim 1)

[0967] Means of collecting activity data from users,

[0968] A means for analyzing the data to calculate and visualize carbon dioxide emissions,

[0969] A means of generating reduction measures based on analysis results and presenting them to the user,

[0970] A means of providing information on environmental activities and encouraging users to participate,

[0971] A means of sharing users' environmental contributions on social networks,

[0972] A system that includes this.

[0973] (Claim 2)

[0974] The system according to claim 1, which maintains the anonymity of collected data and transmits it securely.

[0975] (Claim 3)

[0976] The system according to claim 1, which provides relevant information based on the user's profile.

[0977] "Example 1"

[0978] (Claim 1)

[0979] Means of collecting information about user activities,

[0980] A means for analyzing the information to calculate and visualize the amount of air pollutants emitted,

[0981] A means of generating and presenting action improvement measures to the user based on the analysis results,

[0982] A means of providing information on local environmental events and encouraging user participation,

[0983] A means of sharing users' environmental contributions on a digital platform,

[0984] A system that includes this.

[0985] (Claim 2)

[0986] The system according to claim 1, which ensures the anonymity of collected information and securely transfers it.

[0987] (Claim 3)

[0988] The system according to claim 1, which provides relevant information based on user attribute information.

[0989] "Application Example 1"

[0990] (Claim 1)

[0991] Means of collecting activity data from users,

[0992] A means for analyzing the data to calculate and visualize carbon dioxide emissions,

[0993] A means of generating reduction measures based on analysis results and presenting them to the user,

[0994] A means of providing information on environmental activities and encouraging users to participate,

[0995] A means of sharing users' environmental contributions over the network,

[0996] A means of providing information on local activities and supporting participation in community activities,

[0997] A system that includes this.

[0998] (Claim 2)

[0999] The system according to claim 1, which maintains the anonymity of collected data and transmits it securely.

[1000] (Claim 3)

[1001] The system according to claim 1, which provides relevant information based on user attributes.

[1002] "Example 2 of combining an emotion engine"

[1003] (Claim 1)

[1004] A means of collecting activity data and emotional state data from users,

[1005] A means for analyzing the data to calculate and visualize carbon dioxide emissions,

[1006] A means of generating and presenting reduction measures adjusted according to the user's emotional state,

[1007] A means of creating prompt sentences using a generative AI model and optimizing the suggestion of reduction measures,

[1008] A means of providing information on environmental activities and encouraging participation based on the user's emotional state,

[1009] A means of sharing a user's environmental contribution as a customized message on social networks according to their emotional state,

[1010] A system that includes this.

[1011] (Claim 2)

[1012] The system according to claim 1, which maintains the anonymity of collected data and transmits it securely.

[1013] (Claim 3)

[1014] The system according to claim 1, which provides relevant information based on the user's profile and emotional state.

[1015] "Application example 2 when combining with an emotional engine"

[1016] (Claim 1)

[1017] Means of collecting activity data from users,

[1018] A means for analyzing the data to calculate and visualize carbon dioxide emissions,

[1019] A means of generating reduction measures based on analysis results and presenting them to the user,

[1020] A means of providing information on environmental activities and encouraging users to participate,

[1021] A means of analyzing the user's emotional state and adjusting the content and timing of suggestions based on that analysis,

[1022] A means of sharing users' environmental contributions on social networks,

[1023] A system that includes this.

[1024] (Claim 2)

[1025] The system according to claim 1, which maintains the anonymity of collected data and transmits it securely.

[1026] (Claim 3)

[1027] The system according to claim 1, which provides relevant information based on the user's profile. [Explanation of symbols]

[1028] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting activity data from users, A means for analyzing the data to calculate and visualize carbon dioxide emissions, A means of generating reduction measures based on analysis results and presenting them to the user, A means of providing information on environmental activities and encouraging users to participate, A means of sharing users' environmental contributions over the network, A means of providing information on local activities and supporting participation in community activities, A system that includes this.

2. The system according to claim 1, which maintains the anonymity of collected data and transmits it securely.

3. The system according to claim 1, which provides relevant information based on user attributes.

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