system

Abstract

We provide the system. [Solution] Means for collecting and securely storing user activity data, A method that uses artificial intelligence algorithms to analyze collected user activity data and propose sustainable actions, A means of analyzing user behavior in combination with urban infrastructure information to propose optimal eco-friendly activities, A means of receiving user feedback and quantifying environmental contribution, A means of visualizing and presenting analysis results and contribution data to users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the number of individuals aiming for a sustainable life is increasing, but there is a problem that they do not know through which specific activities they can contribute to the environment or cannot obtain clear guidance for implementation. In addition, since there is a lack of means to appropriately evaluate the effects of eco-activities and obtain feedback, it is also a problem that motivation is difficult to sustain.

Means for Solving the Problems

[0005] This system provides means for collecting and securely storing user activity data, means for analyzing the collected user activity data and using artificial intelligence algorithms to propose sustainable actions, means for receiving user feedback and quantifying environmental contribution, and means for visualizing the analysis results and contribution data and presenting them to the user. This system supports the implementation and continuation of sustainable actions by generating individual sustainable actions based on the user's activity patterns and providing automatic feedback.

[0006] "User activity data" refers to specific information about users' daily behavior and consumption, and this information serves as foundational data used to propose eco-friendly activities.

[0007] "Secure storage" means storing collected data using security measures to protect it from unauthorized access.

[0008] An "artificial intelligence algorithm" is a part of a computer program used to analyze large amounts of data and automatically generate useful information and action suggestions from it.

[0009] "Environmental contribution" is an index that quantifies the positive impact a user has on the environment through specific actions, and is used to evaluate the effectiveness of sustainable activities.

[0010] "Visualization" is the process of converting data and information into visually understandable formats such as graphs and charts.

[0011] A "dashboard" is an interface designed to allow users to see at a glance their environmental contributions and the progress of suggested actions. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (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), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage 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, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that supports users in easily practicing environmentally conscious lifestyles. This system collects and analyzes user activity data, proposes and implements sustainable actions based on that data, and quantifies the results.

[0034] First, users begin accessing the system through a dedicated terminal or app. They enter their profile information and provide the system with basic data for eco-friendly activities, such as their daily activity data (e.g., electricity consumption and frequently used modes of transportation). This data is encrypted by the terminal and transferred to the server via secure communication.

[0035] The server first securely stores the received data, and then analyzes it. Artificial intelligence algorithms are used for the analysis to reveal the user's lifestyle patterns and consumption trends. Based on these analysis results, the server utilizes generative AI to create specific eco-friendly activity suggestions tailored to the user. These suggested eco-activities are then displayed on the user's device in a format easily understood by the user, using natural language processing.

[0036] Users carry out suggested eco-friendly activities and report the results and feedback via their devices. This feedback is sent to a server, which quantifies the effectiveness of the activities and provides users with feedback on their environmental contribution.

[0037] Furthermore, progress and past activity history are visualized and provided to users on a dashboard. Users can use this dashboard to check the status of their eco-activities and plan further actions.

[0038] As a concrete example, if a user's power consumption is found to be high, the server will suggest "reducing air conditioner use by two hours on weekends and using more energy-efficient appliances." If the user follows the suggestion and their power consumption is reduced, the server will provide feedback to the user saying, "This week's power consumption has been reduced by 10%." This allows users to feel a sense of contributing to the environment while promoting a sustainable lifestyle.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users log in to the application using a dedicated terminal and enter their profile information and daily activity data. This activity data includes information such as electricity consumption and the modes of transportation used.

[0042] Step 2:

[0043] The terminal encrypts the entered user data and sends it to the server via a secure communication channel. Security protocols are applied during this process to prevent unauthorized access.

[0044] Step 3:

[0045] The server receives encrypted user data and securely stores it in the database. The stored data is then organized into a format suitable for analysis.

[0046] Step 4:

[0047] The server retrieves user data from the database and begins analysis. The analysis uses artificial intelligence algorithms to understand user behavior patterns, from which useful information is extracted.

[0048] Step 5:

[0049] Based on the data analyzed by the server, the data is sent to a generating AI to create specific eco-friendly activities tailored to the user's lifestyle. This generation process utilizes natural language processing to create suggestions in a format that is easy for the user to understand.

[0050] Step 6:

[0051] The server sends the generated eco-activity suggestions to the device. The device receives the suggestions and notifies or displays them to the user.

[0052] Step 7:

[0053] Users implement suggested eco-friendly activities and provide feedback on their results and impressions via their devices. This feedback includes details about the activities performed and their perceived effectiveness.

[0054] Step 8:

[0055] The device re-encrypts user feedback and securely sends it to the server.

[0056] Step 9:

[0057] The server analyzes the feedback received from users and quantifies the results of the activities. Specifically, it calculates things like the amount of carbon dioxide reduced and the rate of reduction in electricity consumption.

[0058] Step 10:

[0059] The server provides quantified results to the user and prepares the data for a dashboard. The results are visualized in an easy-to-understand format and presented to the user.

[0060] Step 11:

[0061] The device updates the dashboard, displaying the user's latest contributions and progress. Users can use this dashboard to plan their next eco-friendly activities.

[0062] (Example 1)

[0063] 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."

[0064] As environmental problems worsen, individual users are required to take sustainable actions in their daily lives. However, it is difficult to judge and implement which actions in daily life contribute to environmental protection. Furthermore, there is a lack of mechanisms to understand the extent to which actual actions contribute to the environment, making it difficult to voluntarily continue sustainable behavior. Therefore, there is a need for technology that utilizes users' daily data to propose specific and individually optimal sustainable actions, visualizes their effects, and encourages continuous behavioral improvement.

[0065] 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.

[0066] In this invention, the server includes means for acquiring and securely storing user activity information, means for analyzing the acquired user activity information and using artificial intelligence algorithms to propose sustainable activities, and means for creating sustainable activities suitable for the user by utilizing a generative AI model. This makes it possible to provide users with individually optimized sustainable actions and visualize the environmental contribution resulting from performing those actions.

[0067] "Users" refers to the individual people or groups who use the system.

[0068] "Activity information" refers to data related to the user's daily activities, including electricity consumption and choice of transportation.

[0069] An "artificial intelligence algorithm" refers to a computational method used to analyze large amounts of data and find patterns and trends.

[0070] A "generative AI model" is an advanced computational model used to generate optimal suggestions and information for users.

[0071] "Visualization" refers to converting data and information into a format that is easy for users to understand and presenting it as graphs, charts, and diagrams.

[0072] "Environmental contribution" refers to a numerical value that quantifies the impact that user behavior has on the environment.

[0073] A "display device" refers to a screen or dashboard that users use to view information.

[0074] This invention is a support system for users to practice environmentally conscious and sustainable living. The system acquires user activity information, analyzes it using artificial intelligence algorithms, and proposes sustainable activities using generated AI models. As a result, users receive specific and individually optimized eco-activity suggestions, along with feedback on the environmental impact of those activities in a quantified form.

[0075] server:

[0076] The server receives activity information sent from the user's device and stores it in a secure database. The received data is analyzed using artificial intelligence algorithms. This analysis reveals the user's lifestyle patterns and energy consumption trends. Based on this, the server uses a generative AI model to create and propose optimal and sustainable activities for the user. In this process, the generative AI model is used to efficiently generate new behavioral suggestions.

[0077] Terminal:

[0078] The device encrypts the user's input information and sends it to the server. It displays sustainable activity suggestions received from the server and encourages their implementation. It also provides an interface for users to report the results of their eco-activities and sends this information back to the server.

[0079] User:

[0080] Users access the system and input their activity information. This information is transmitted to the server via the terminal. They can then implement sustainable action suggestions received from the server and input the results as feedback into the terminal.

[0081] Specific example:

[0082] For example, if a user's energy consumption data shows a high trend, the server can generate specific suggestions such as "reduce air conditioner usage time and use more energy-efficient appliances." If the user actually takes action, they can receive quantified feedback such as "this week's electricity consumption was reduced by 10%."

[0083] Example of a prompt:

[0084] "Based on user electricity consumption data, generate specific action suggestions to reduce consumption. For example, suggest reducing air conditioner use or using energy-efficient appliances."

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] User:

[0088] Users input profile information and daily activity data using a dedicated terminal or application. This information includes power consumption and transportation choices. The entered data serves as the basis for subsequent processing.

[0089] Terminal:

[0090] The terminal encrypts the information entered by the user. This encryption process is crucial to ensure data security. The encrypted data is sent to the server via secure communication. At this stage, the user's input is provided to the server in the form of encrypted data.

[0091] Step 2:

[0092] server:

[0093] The server receives encrypted data sent from the terminal. The received data is first stored in a secure database. After that, it is converted into a format suitable for the analysis. Based on the converted data, an artificial intelligence algorithm is used to analyze the user's lifestyle patterns and consumption trends. The output is an analysis result showing the user's behavioral patterns.

[0094] Step 3:

[0095] server:

[0096] Based on the analysis results, the server uses a generative AI model to suggest sustainable activities suitable for the user. Here, a prompt is input to the generative AI, and appropriate suggestions are generated. For example, a prompt might be, "Generate specific suggestions to reduce usage time based on power consumption data." The output is a suggestion in a format easily understood by the user. This suggestion is then sent to the device using natural language processing in a form directly applicable to the user.

[0097] Step 4:

[0098] Terminal:

[0099] The terminal displays suggestions received from the server to the user. The suggestions are explained in a way that is easy for the user to understand and implement. For example, a concrete example such as "reduce air conditioner use by two hours on weekends" is provided. The user also inputs feedback through the terminal regarding the results of implementing the suggestions. This feedback becomes input data in the server's evaluation process.

[0100] Step 5:

[0101] server:

[0102] The server receives feedback from users and quantifies the effectiveness of their activities based on the data provided. This process involves calculating the environmental contribution. The calculation results are visualized to help users understand the impact of their activities and are displayed through a dashboard. This allows users to concretely grasp the impact their activities have on the environment and encourages continuous improvement of their behavior.

[0103] (Application Example 1)

[0104] 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."

[0105] In modern society, both individuals and cities need to share responsibility for the environment and effectively promote eco-friendly activities. However, many individuals do not have a good grasp of the specific methods and effects of eco-friendly activities, and the means to efficiently utilize urban infrastructure are not yet fully developed. To solve this problem and realize a sustainable society, there is a need to provide effective methods for individuals and cities to collaborate in implementing and improving eco-friendly activities.

[0106] 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.

[0107] This invention includes a server that collects and securely stores user activity data, analyzes the collected user activity data in combination with urban infrastructure information to propose optimal eco-activities, and visualizes the analysis results and contribution data, presents them to the user, and encourages the use of public facilities and transportation. This makes it possible for individual sustainable actions to be linked to urban-wide eco-activities, and for contributions to the environment to be quantified and visualized.

[0108] "User activity data" refers to information collected about an individual's daily activities and the resources they use (e.g., electricity, transportation).

[0109] "Means of secure storage" refers to methods of encrypting collected data and storing it on a server using appropriate protocols.

[0110] An "artificial intelligence algorithm" is a technology that analyzes large amounts of data, identifies patterns and trends, and proposes sustainable actions.

[0111] "Urban infrastructure information" refers to data on the usage of public facilities and transportation systems, energy supply systems, and so on.

[0112] "Optimal eco-activities" refer to a series of processes that propose actions that individuals can take based on analysis results and that contribute to the environment.

[0113] "Visualization" is a method of representing analysis results and contributions using graphs, charts, and other visual aids so that users can understand them intuitively.

[0114] "Encouraging the use of public facilities and transportation" means presenting users with specific ways of using them and the benefits they receive in order to practice sustainable behavior.

[0115] In an embodiment of this invention, the user uses a dedicated application on their smartphone to input data on their daily activities. This activity data includes information such as power consumption and means of transportation used, and this information is transmitted to a server via an encrypted communication protocol. The server analyzes the received data using artificial intelligence algorithms such as TENSORFLOW® to identify the user's behavioral patterns.

[0116] Based on the analysis results, the server uses the GPT series' generation AI to propose optimal eco-friendly activities. These suggestions are displayed on the smartphone screen in a user-friendly format using natural language processing technology. In some cases, the server combines urban infrastructure information with user activity data to suggest the use of public facilities and transportation. This promotes eco-friendly activities in a way that individual actions contribute to the sustainable activities of the entire city.

[0117] Users implement suggested eco-friendly activities and provide feedback to the server via a smartphone app. The server quantifies the degree of environmental contribution based on the feedback and provides it to the user. In addition, progress and past activity history are visualized and presented to the user as a dashboard to support the development of further action plans.

[0118] For example, if a user sets a goal of "reducing weekend electricity consumption," the server will use its AI-generating prompt to suggest appropriate eco-friendly activities, such as "Analyze the user's weekend electricity consumption and suggest appropriate reduction methods." By following the suggestions, the user can actually reduce their electricity consumption and feel a sense of contributing to the environment.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] Users input daily activity data using a dedicated smartphone application. This data includes electricity consumption and modes of transportation used. The entered data is encrypted in real time and transmitted to the server via a secure communication protocol.

[0122] Step 2:

[0123] The server receives the data and securely stores it in a storage database. Here, the data is organized to facilitate subsequent analysis. As a result of this processing, user activity data is stored securely.

[0124] Step 3:

[0125] The server activates an artificial intelligence algorithm using TensorFlow to analyze user activity data. This analysis process cleanses the data, removes outliers, and then identifies user behavior patterns. As a result, user consumption trends are identified.

[0126] Step 4:

[0127] Based on the analysis results, the server uses the GPT series generative AI model to generate suggestions for eco-friendly activities. The generative AI receives instructions in the form of a prompt: "Create a sustainable action plan based on the user's activities." The generated suggestions are output in a clear and actionable format.

[0128] Step 5:

[0129] The server uses natural language processing technology to send the generated suggestions to the terminal and displays them in a language that the user can easily understand. This allows the user to grasp the specific details of the eco-friendly activities.

[0130] Step 6:

[0131] Users carry out the suggested eco-friendly activities and report the results to the server via their smartphones. This report includes changes in electricity consumption and transportation usage during the relevant period.

[0132] Step 7:

[0133] The server analyzes user feedback and quantifies the environmental contribution. Based on the feedback data, it calculates the environmental contribution as numerical data and stores it in a database.

[0134] Step 8:

[0135] The server visualizes the calculated environmental contribution and past activity data as a dashboard and provides it to the user's device. Users can use this dashboard to check the progress of their eco-activities and plan further actions.

[0136] 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.

[0137] This invention is a system that comprehensively utilizes user activity data and emotional data to support the realization of a sustainable lifestyle. Based on activity data provided by the user, as well as emotional data detected using an emotion engine, the system proposes and manages more effective action plans.

[0138] First, users log in to the system using a dedicated terminal or app. Here, users input data on their daily activities (e.g., eating habits, commuting methods, etc.) as well as their emotional state (e.g., stress level, happiness level, etc.) provided by the emotion engine. This information is encrypted by the terminal and securely transmitted to the server.

[0139] The server first stores the received user activity and emotional data in a database and then begins analysis. An artificial intelligence algorithm is used for the analysis. This algorithm creates a sustainable action plan based on the user's lifestyle patterns and emotional state. The generated action suggestions are then displayed on the user's device in an easily understandable format through natural language processing.

[0140] Proposed eco-friendly activities become more effective and acceptable by taking into account the user's emotional state. For example, if a user is feeling stressed, small, manageable eco-friendly activities are suggested to reduce the user's burden.

[0141] Users perform the suggested actions and report the results and their impressions as feedback from their devices. Since this feedback data also includes emotional information, the server quantifies a more detailed environmental contribution based on the activity results and provides it to the user.

[0142] Furthermore, the progress of each user's behavior and emotional data is visualized on a dashboard. Based on this information, users can plan their next actions. For example, if a user is experiencing emotional stress due to "busyness," the system provides suggestions for eco-friendly activities that require less effort, making it easier for them to continue sustainable behavior.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] Users log in to the application using a dedicated terminal and input activity data and emotional state. Emotional state is information obtained using an emotion engine and includes indicators such as stress and happiness.

[0146] Step 2:

[0147] The device encrypts this data and sends all data to the server via a secure channel. Here, data is transmitted according to appropriate security protocols to ensure user privacy.

[0148] Step 3:

[0149] The server stores the received user activity and sentiment data in a database, preparing it for analysis. At this point, the data is organized by category and formatted to suit specific algorithmic processing.

[0150] Step 4:

[0151] The server uses artificial intelligence algorithms to analyze the user's activity patterns and emotional state. Based on the analysis results, it generates personalized, sustainable action suggestions using AI. These suggestions take the user's emotional state into consideration and are designed to reduce the burden of implementation.

[0152] Step 5:

[0153] The server sends the generated action suggestion to the device. The device receives it and notifies or displays it to the user in a highly visible format.

[0154] Step 6:

[0155] Users carry out suggested eco-friendly activities and provide feedback via their devices regarding the results, their impressions, and their current emotional state. This feedback is entered as detailed information, including changes in their emotions.

[0156] Step 7:

[0157] The device encrypts the user's feedback data and resends it to the server.

[0158] Step 8:

[0159] The server analyzes user feedback and quantifies the results. In this process, emotional information is used to calculate a more personalized level of environmental contribution.

[0160] Step 9:

[0161] The server prepares quantified results and progress data for the dashboard and generates visualization data to present to the user.

[0162] Step 10:

[0163] The device displays an updated dashboard, providing users with the latest environmental contributions and emotional state trends. Users then use this information to develop their next sustainable action plan.

[0164] (Example 2)

[0165] 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".

[0166] In modern society, users need concrete action plans that take into account their daily activities and emotional states in order to achieve a sustainable lifestyle. However, conventional systems struggle to provide action suggestions that adequately consider the user's emotional state, and they lack mechanisms to provide appropriate feedback on the effects of those actions. Therefore, there is a need for efficient methods to promote sustainable behavioral change in users.

[0167] 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.

[0168] In this invention, the server includes means for collecting and securely storing user activity information and emotional information, means for determining the user's emotional state using an emotion engine, and means for analyzing the collected user activity information and emotional information and using an artificial intelligence algorithm to propose sustainable actions. This makes it possible to promote sustainable behavioral change through effective action suggestions that take the user's emotional state into account and the feedback provided.

[0169] "User activity information" refers to data about users' daily behavioral patterns and habits.

[0170] "Emotional information" refers to data that indicates a user's emotional state, such as stress levels or feelings of happiness, which are quantified as psychological states.

[0171] An "emotion engine" is a software module that determines a user's emotional state based on their input data.

[0172] An "artificial intelligence algorithm" is a computational method used to analyze data and propose sustainable action plans for users.

[0173] "Natural language processing technology" is a technology used by machines to understand and process natural language, and is used to generate user-oriented suggestions.

[0174] "Environmental contribution" is an indicator that quantifies the positive impact that user behavior has on the environment.

[0175] An "interface" is a means for a user to interact with a system and plays a role in providing visual information.

[0176] This invention is a system that comprehensively utilizes user activity information and emotional information to support the realization of a sustainable lifestyle.

[0177] Users access the system using a dedicated terminal or app. Users input information about their daily activities (e.g., eating habits, commuting methods) and emotional information obtained using the emotion engine (e.g., stress levels, happiness levels). This information is AES encrypted by the terminal and securely transmitted to the server. Transmission is performed using SSL / TLS communication to ensure data security.

[0178] The server stores the received data in the database and begins analysis. The data analysis uses artificial intelligence algorithms based on machine learning libraries implemented in Python (e.g., TensorFlow and PyTorch). These algorithms analyze the user's past activity patterns and emotional states and propose optimal, sustainable actions. The proposed actions are presented to the user in an easily understandable format using natural language processing techniques.

[0179] The device receives suggestions from the server and displays them to the user. These suggestions appear as pop-up notifications or on the dashboard, with detailed instructions on specific actions to take. For example, a suggestion might be displayed such as, "Let's do some eco-friendly activities today that can be done in a short amount of time."

[0180] Users take action based on the suggestions and send feedback from their devices regarding the results and their impressions. This feedback, including emotional information, is then sent from the device to the server. The server uses this data to quantify the user's contribution to the environment and supports continuous improvement of their actions.

[0181] Progress is visualized on a dashboard. Through the dashboard, users can review activity results and changes in sentiment, which helps them plan their next actions.

[0182] For example, if a user is feeling emotionally burdened due to being busy, the system will take that emotional state into consideration and suggest easy-to-implement eco-friendly activities. For instance, a specific action such as "Let's stop by a recycling point on the way home" might be suggested.

[0183] As an example of a prompt, the following input would be given to the generating AI model: "Generate suggestions for eco-friendly activities that can be done quickly while the user is experiencing high stress. For example, please suggest recycling activities that can be done in a short amount of time."

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] Users log in to the system using a dedicated terminal or app. During this process, users input activity information (e.g., eating habits, commuting methods, etc.) and their emotional state. The terminal encrypts the user's input using AES encryption and securely transmits it to the server via SSL / TLS communication. The input information also includes the user's identifier and timestamp. The output is encrypted data.

[0187] Step 2:

[0188] The server receives encrypted data from the terminal. First, it decrypts the data and stores it in the database. The data processing performed here is a format conversion of the received data, and it is stored as a consistent data structure. The output is the raw data stored in the database.

[0189] Step 3:

[0190] The server analyzes user activity and sentiment information stored in the database. The analysis uses artificial intelligence algorithms and machine learning libraries (e.g., TensorFlow). The algorithms extract patterns based on historical data and generate optimal, sustainable action plans for each individual user. The input is raw user data, and the output is the analyzed action suggestions.

[0191] Step 4:

[0192] The server converts the generated action suggestions into text format using natural language processing technology. The text-based suggestions are then processed into a format that is easy for the user to understand. The output suggestions are then ready to be sent to the terminal.

[0193] Step 5:

[0194] The terminal displays action suggestions received from the server to the user. These suggestions are displayed via pop-up notifications and dashboards, allowing the user to easily review the provided suggestions. The input is the action suggestions in text format, and the output is the information displayed on the user's screen.

[0195] Step 6:

[0196] The user performs the suggested action and inputs the results and their impressions as feedback into the device. The device then re-encrypts this feedback information and securely transmits it to the server. The input is the user's feedback data, and the output is the encrypted feedback information.

[0197] Step 7:

[0198] The server receives feedback and analyzes the information. Based on the activity results, it quantifies the user's environmental contribution and saves it to the database as the latest information. This analysis process contributes to the evaluation of feedback data and the creation of new action suggestions. The output is the updated environmental contribution score.

[0199] (Application Example 2)

[0200] 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".

[0201] In modern society, there is a growing need to promote sustainable behavior. However, understanding and readily accepting what behavior is optimal for each individual is not easy. To address this challenge, there is a need for a system that proposes and supports effective sustainable behaviors while considering the user's emotional state.

[0202] 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.

[0203] This invention includes a server that collects and securely stores user activity data and emotional data, an artificial intelligence algorithm that analyzes the collected user activity data and emotional data and proposes sustainable actions, and a means that receives user feedback and quantifies evaluations based on environmental contribution and emotional state. This makes it possible to propose sustainable actions optimized for the user, enhance their acceptance, and support the realization of sustainable cities.

[0204] "User activity data" refers to information about an individual's daily actions and habits.

[0205] "Emotional data" refers to information that indicates a user's psychological state and changes in their emotions.

[0206] An "artificial intelligence algorithm" is a computational method for generating sustainable action plans by analyzing activity data and emotional data.

[0207] "Feedback" refers to information about a user's reaction or opinion to a suggested action.

[0208] "Environmental contribution" is an indicator that quantifies the extent to which user behavior contributes to environmental sustainability.

[0209] "Emotional state-based evaluation" refers to an evaluation of the action suggestions provided, taking into account the user's emotional response.

[0210] "Natural language processing technology" is a technology that processes human language in order to propose sustainable actions in a format that is easy for users to understand.

[0211] A "dashboard" is a display screen that visualizes the progress of a user's environmental contributions and behavioral patterns.

[0212] One embodiment of this invention is a system that proposes sustainable living by using user activity data and emotional data. This system primarily utilizes a server, a user terminal, and an artificial intelligence algorithm.

[0213] The user's device provides a dedicated application for acquiring activity and emotion data. This application aggregates emotion data detected by an emotion engine, in addition to data entered by the user. This data is encrypted using the DataEncryption library and securely transmitted to the server.

[0214] The server stores the received data in a database and begins analysis using an artificial intelligence algorithm called AIEngine. This analysis generates sustainable action suggestions optimized for the user. These suggestions are presented to the user in an easy-to-understand format through natural language processing technology. Specifically, it takes emotional states into consideration and proposes sustainable actions in a manageable way.

[0215] For example, users who tend to expend a lot of energy on weekends could be suggested to use a bicycle to conserve energy. If a user is experiencing negative emotions such as stress, they might be suggested to engage in relaxing activities.

[0216] An example of a prompt message would be input to the generating AI model in the form of: "User activity data: Increased energy consumption on weekends. User emotional state: High stress level. Generate sustainable action suggestions."

[0217] Feedback is sent from the device to the server, and detailed analysis results based on the user's environmental contribution and emotional state are visualized as a dashboard to help plan the next sustainable actions.

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] Users input activity data and emotional data using a dedicated application on their device. Activity data concerns the user's daily activities, such as exercise levels and commuting methods. Emotional data includes information such as stress levels and happiness levels, detected by the device's built-in emotion engine. This data is encrypted within the device using the DataEncryption library and prepared for transmission to the server.

[0221] Step 2:

[0222] The device transmits user activity and sentiment data to the server in an encrypted state. The server securely stores the received data in a database. At this stage, the input is encrypted data, and the output is decrypted structured data. The data is then processed into a format that can be used in subsequent analysis.

[0223] Step 3:

[0224] The server analyzes the data stored in the database using AIEngine. AIEngine considers the user's current activity patterns and emotional state to generate an optimal, sustainable action plan. Past data history is also considered, and the suggestions are adjusted based on medium- to long-term trends. The input is activity patterns and emotional state obtained from the user database, and the output is a sustainable action plan.

[0225] Step 4:

[0226] The generated action suggestions are sent from the server to the terminal, where they are converted into a user-friendly format using natural language processing technology. At this stage, a generative AI model is used to generate prompt sentences. An example prompt sentence is: "User activity data: Energy consumption increases on weekends. User emotional state: High stress level. Please generate sustainable action suggestions." The input is sustainable action suggestions, and the output is specific actions presented to the user.

[0227] Step 5:

[0228] The user performs the suggested action and sends the results and their comments as feedback from their device to the server. The feedback includes detailed information reflecting the user's emotional state. The server analyzes this feedback and quantifies the user's environmental contribution. This strengthens the system's foundational data for generating personalized future suggestions based on the user's behavioral history. The input is feedback information, and the output is updated environmental contribution data.

[0229] Step 6:

[0230] The server visualizes data based on updated environmental contributions and emotional states on a dashboard and provides it to the user. Users can refer to this and develop new action plans. The input is the updated analysis results, and the output is the visualized dashboard information. Based on this information, users can strive to improve their sustainable lifestyle.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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".

[0247] This invention is a system that supports users in easily practicing environmentally conscious lifestyles. This system collects and analyzes user activity data, proposes and implements sustainable actions based on that data, and quantifies the results.

[0248] First, users begin accessing the system through a dedicated terminal or app. They enter their profile information and provide the system with basic data for eco-friendly activities, such as their daily activity data (e.g., electricity consumption and frequently used modes of transportation). This data is encrypted by the terminal and transferred to the server via secure communication.

[0249] The server first securely stores the received data, and then analyzes it. Artificial intelligence algorithms are used for the analysis to reveal the user's lifestyle patterns and consumption trends. Based on these analysis results, the server utilizes generative AI to create specific eco-friendly activity suggestions tailored to the user. These suggested eco-activities are then displayed on the user's device in a format easily understood by the user, using natural language processing.

[0250] Users carry out suggested eco-friendly activities and report the results and feedback via their devices. This feedback is sent to a server, which quantifies the effectiveness of the activities and provides users with feedback on their environmental contribution.

[0251] Furthermore, progress and past activity history are visualized and provided to users on a dashboard. Users can use this dashboard to check the status of their eco-activities and plan further actions.

[0252] As a concrete example, if a user's power consumption is found to be high, the server will suggest "reducing air conditioner use by two hours on weekends and using more energy-efficient appliances." If the user follows the suggestion and their power consumption is reduced, the server will provide feedback to the user saying, "This week's power consumption has been reduced by 10%." This allows users to feel a sense of contributing to the environment while promoting a sustainable lifestyle.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] Users log in to the application using a dedicated terminal and enter their profile information and daily activity data. This activity data includes information such as electricity consumption and the modes of transportation used.

[0256] Step 2:

[0257] The terminal encrypts the entered user data and sends it to the server via a secure communication channel. Security protocols are applied during this process to prevent unauthorized access.

[0258] Step 3:

[0259] The server receives encrypted user data and securely stores it in the database. The stored data is then organized into a format suitable for analysis.

[0260] Step 4:

[0261] The server retrieves user data from the database and begins analysis. The analysis uses artificial intelligence algorithms to understand user behavior patterns, from which useful information is extracted.

[0262] Step 5:

[0263] Based on the data analyzed by the server, the data is sent to a generating AI to create specific eco-friendly activities tailored to the user's lifestyle. This generation process utilizes natural language processing to create suggestions in a format that is easy for the user to understand.

[0264] Step 6:

[0265] The server sends the generated eco-activity suggestions to the device. The device receives the suggestions and notifies or displays them to the user.

[0266] Step 7:

[0267] Users implement suggested eco-friendly activities and provide feedback on their results and impressions via their devices. This feedback includes details about the activities performed and their perceived effectiveness.

[0268] Step 8:

[0269] The device re-encrypts user feedback and securely sends it to the server.

[0270] Step 9:

[0271] The server analyzes the feedback received from users and quantifies the results of the activities. Specifically, it calculates things like the amount of carbon dioxide reduced and the rate of reduction in electricity consumption.

[0272] Step 10:

[0273] The server provides quantified results to the user and prepares the data for a dashboard. The results are visualized in an easy-to-understand format and presented to the user.

[0274] Step 11:

[0275] The device updates the dashboard, displaying the user's latest contributions and progress. Users can use this dashboard to plan their next eco-friendly activities.

[0276] (Example 1)

[0277] 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."

[0278] As environmental problems become more serious, individual users are required to take sustainable actions in their own lives. However, it is difficult to determine and practice which actions in daily life contribute to environmental protection. Furthermore, due to the lack of a mechanism to grasp the extent to which actual actions have contributed to the environment, it is difficult to spontaneously continue sustainable actions. Therefore, there is a need for a technology that utilizes users' daily data, proposes specific and individualized optimal sustainable actions, visualizes the effects, and promotes continuous action improvement.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes means for acquiring and safely storing the activity information of the user, means for analyzing the acquired activity information of the user and using an artificial intelligence algorithm to propose sustainable activities, and means for creating sustainable activities suitable for the user by utilizing a generative AI model. As a result, it becomes possible to provide sustainable actions optimized individually for the user and visualize the degree of environmental contribution by executing them.

[0281] The "user" refers to individual people or groups who use the system.

[0282] The "activity information" refers to data related to the actions of the user's daily life and includes power consumption and the choice of transportation means.

[0283] The "artificial intelligence algorithm" refers to a computational method for analyzing a large amount of data to find patterns and trends.

[0284] The "generative AI model" is an advanced computational model used to generate optimal proposals and information for the user.

[0285] "Visualization" means converting data and information into a form that is easy for the user to understand and presenting it as a graph or chart.

[0286] The "environmental contribution degree" refers to a value obtained by quantifying the impact of a user's behavior on the environment.

[0287] The "display device" refers to a screen or dashboard used by a user to view information.

[0288] This invention is a support system for users to practice a sustainable life considering the environment. The system acquires the user's activity information, analyzes it using artificial intelligence algorithms, and uses the generated AI model to propose sustainable activities. As a result, specific and individually optimized eco-activity proposals and feedback on the environmental impact of those activities are provided to the user in a quantified form.

[0289] Server:

[0290] The server receives the activity information sent from the user's terminal and stores it in a secure database. The received data is analyzed using artificial intelligence algorithms. Through this analysis, the user's lifestyle pattern and energy consumption tendency are revealed. Based on this, the server creates and proposes optimal sustainable activities for the user using the generated AI model. In this process, the generated AI model is utilized to efficiently create new action proposals.

[0291] Terminal:

[0292] The terminal encrypts the user's input information and sends it to the server. It displays the proposed sustainable activities received from the server and encourages implementation. It also provides an interface for the user to report the results of eco-activities and sends this information to the server again.

[0293] User:

[0294] Users access the system and input their activity information. This information is transmitted to the server via the terminal. They can then implement sustainable action suggestions received from the server and input the results as feedback into the terminal.

[0295] Specific example:

[0296] For example, if a user's energy consumption data shows a high trend, the server can generate specific suggestions such as "reduce air conditioner usage time and use more energy-efficient appliances." If the user actually takes action, they can receive quantified feedback such as "this week's electricity consumption was reduced by 10%."

[0297] Example of a prompt:

[0298] "Based on user electricity consumption data, generate specific action suggestions to reduce consumption. For example, suggest reducing air conditioner use or using energy-efficient appliances."

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] User:

[0302] Users input profile information and daily activity data using a dedicated terminal or application. This information includes power consumption and transportation choices. The entered data serves as the basis for subsequent processing.

[0303] Terminal:

[0304] The terminal encrypts the information entered by the user. This encryption process is important to ensure the security of the data. The encrypted data is sent to the server through secure communication. At this stage, the input from the user is provided to the server in the form of encrypted data.

[0305] Step 2:

[0306] Server:

[0307] The server receives the encrypted data sent from the terminal. The received data is first stored in a secure database. After that, for data analysis, it is converted into a format suitable for this analysis. Based on the converted data, artificial intelligence algorithms are used to analyze the user's living patterns and consumption trends. As an output, an analysis result indicating the user's behavior pattern is obtained.

[0308] Step 3:

[0309] Server:

[0310] Based on the analysis result, the server uses a generative AI model to propose sustainable activities suitable for the user. Here, a prompt sentence is input into the generative AI, and appropriate proposals are generated. For example, a prompt sentence such as "Generate specific proposals to reduce the usage time based on the power consumption data" is used. As an output, proposals in a form easy for the user to understand are generated. This proposal is sent to the terminal in a form directly applicable to the user using natural language processing.

[0311] Step 4:

[0312] Terminal:

[0313] The terminal displays suggestions received from the server to the user. The suggestions are explained in a way that is easy for the user to understand and implement. For example, a concrete example such as "reduce air conditioner use by two hours on weekends" is provided. The user also inputs feedback through the terminal regarding the results of implementing the suggestions. This feedback becomes input data in the server's evaluation process.

[0314] Step 5:

[0315] server:

[0316] The server receives feedback from users and quantifies the effectiveness of their activities based on the data provided. This process involves calculating the environmental contribution. The calculation results are visualized to help users understand the impact of their activities and are displayed through a dashboard. This allows users to concretely grasp the impact their activities have on the environment and encourages continuous improvement of their behavior.

[0317] (Application Example 1)

[0318] 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."

[0319] In modern society, both individuals and cities need to share responsibility for the environment and effectively promote eco-friendly activities. However, many individuals do not have a good grasp of the specific methods and effects of eco-friendly activities, and the means to efficiently utilize urban infrastructure are not yet fully developed. To solve this problem and realize a sustainable society, there is a need to provide effective methods for individuals and cities to collaborate in implementing and improving eco-friendly activities.

[0320] 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.

[0321] This invention includes a server that collects and securely stores user activity data, analyzes the collected user activity data in combination with urban infrastructure information to propose optimal eco-activities, and visualizes the analysis results and contribution data, presents them to the user, and encourages the use of public facilities and transportation. This makes it possible for individual sustainable actions to be linked to urban-wide eco-activities, and for contributions to the environment to be quantified and visualized.

[0322] "User activity data" refers to information collected about an individual's daily activities and the resources they use (e.g., electricity, transportation).

[0323] "Means of secure storage" refers to methods of encrypting collected data and storing it on a server using appropriate protocols.

[0324] An "artificial intelligence algorithm" is a technology that analyzes large amounts of data, identifies patterns and trends, and proposes sustainable actions.

[0325] "Urban infrastructure information" refers to data on the usage of public facilities and transportation systems, energy supply systems, and so on.

[0326] "Optimal eco-activities" refer to a series of processes that propose actions that individuals can take based on analysis results and that contribute to the environment.

[0327] "Visualization" is a method of representing analysis results and contributions using graphs, charts, and other visual aids so that users can understand them intuitively.

[0328] "Encouraging the use of public facilities and transportation" means presenting users with specific ways of using them and the benefits they receive in order to practice sustainable behavior.

[0329] In one embodiment of this invention, the user uses a dedicated application on their smartphone to input data on their daily activities. This activity data includes things like electricity consumption and the means of transportation used, and this information is transmitted to a server via an encrypted communication protocol. The server analyzes the received data using artificial intelligence algorithms such as TensorFlow to identify the user's behavioral patterns.

[0330] Based on the analysis results, the server uses the GPT series' generation AI to propose optimal eco-friendly activities. These suggestions are displayed on the smartphone screen in a user-friendly format using natural language processing technology. In some cases, the server combines urban infrastructure information with user activity data to suggest the use of public facilities and transportation. This promotes eco-friendly activities in a way that individual actions contribute to the sustainable activities of the entire city.

[0331] Users implement suggested eco-friendly activities and provide feedback to the server via a smartphone app. The server quantifies the degree of environmental contribution based on the feedback and provides it to the user. In addition, progress and past activity history are visualized and presented to the user as a dashboard to support the development of further action plans.

[0332] For example, if a user sets a goal of "reducing weekend electricity consumption," the server will use its AI-generating prompt to suggest appropriate eco-friendly activities, such as "Analyze the user's weekend electricity consumption and suggest appropriate reduction methods." By following the suggestions, the user can actually reduce their electricity consumption and feel a sense of contributing to the environment.

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] Users input daily activity data using a dedicated smartphone application. This data includes electricity consumption and modes of transportation used. The entered data is encrypted in real time and transmitted to the server via a secure communication protocol.

[0336] Step 2:

[0337] The server receives the data and securely stores it in a storage database. Here, the data is organized to facilitate subsequent analysis. As a result of this processing, user activity data is stored securely.

[0338] Step 3:

[0339] The server activates an artificial intelligence algorithm using TensorFlow to analyze user activity data. This analysis process cleanses the data, removes outliers, and then identifies user behavior patterns. As a result, user consumption trends are identified.

[0340] Step 4:

[0341] Based on the analysis results, the server uses the GPT series generative AI model to generate suggestions for eco-friendly activities. The generative AI receives instructions in the form of a prompt: "Create a sustainable action plan based on the user's activities." The generated suggestions are output in a clear and actionable format.

[0342] Step 5:

[0343] The server uses natural language processing technology to send the generated suggestions to the terminal and displays them in a language that the user can easily understand. This allows the user to grasp the specific details of the eco-friendly activities.

[0344] Step 6:

[0345] Users carry out the suggested eco-friendly activities and report the results to the server via their smartphones. This report includes changes in electricity consumption and transportation usage during the relevant period.

[0346] Step 7:

[0347] The server analyzes user feedback and quantifies the environmental contribution. Based on the feedback data, it calculates the environmental contribution as numerical data and stores it in a database.

[0348] Step 8:

[0349] The server visualizes the calculated environmental contribution and past activity data as a dashboard and provides it to the user's device. Users can use this dashboard to check the progress of their eco-activities and plan further actions.

[0350] 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.

[0351] This invention is a system that comprehensively utilizes user activity data and emotional data to support the realization of a sustainable lifestyle. Based on activity data provided by the user, as well as emotional data detected using an emotion engine, the system proposes and manages more effective action plans.

[0352] First, users log in to the system using a dedicated terminal or app. Here, users input data on their daily activities (e.g., eating habits, commuting methods, etc.) as well as their emotional state (e.g., stress level, happiness level, etc.) provided by the emotion engine. This information is encrypted by the terminal and securely transmitted to the server.

[0353] The server first stores the received user activity and emotional data in a database and then begins analysis. An artificial intelligence algorithm is used for the analysis. This algorithm creates a sustainable action plan based on the user's lifestyle patterns and emotional state. The generated action suggestions are then displayed on the user's device in an easily understandable format through natural language processing.

[0354] Proposed eco-friendly activities become more effective and acceptable by taking into account the user's emotional state. For example, if a user is feeling stressed, small, manageable eco-friendly activities are suggested to reduce the user's burden.

[0355] Users perform the suggested actions and report the results and their impressions as feedback from their devices. Since this feedback data also includes emotional information, the server quantifies a more detailed environmental contribution based on the activity results and provides it to the user.

[0356] Furthermore, the progress of each user's behavior and emotional data is visualized on a dashboard. Based on this information, users can plan their next actions. For example, if a user is experiencing emotional stress due to "busyness," the system provides suggestions for eco-friendly activities that require less effort, making it easier for them to continue sustainable behavior.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] Users log in to the application using a dedicated terminal and input activity data and emotional state. Emotional state is information obtained using an emotion engine and includes indicators such as stress and happiness.

[0360] Step 2:

[0361] The device encrypts this data and sends all data to the server via a secure channel. Here, data is transmitted according to appropriate security protocols to ensure user privacy.

[0362] Step 3:

[0363] The server stores the received user activity and sentiment data in a database, preparing it for analysis. At this point, the data is organized by category and formatted to suit specific algorithmic processing.

[0364] Step 4:

[0365] The server uses artificial intelligence algorithms to analyze the user's activity patterns and emotional state. Based on the analysis results, it generates personalized, sustainable action suggestions using AI. These suggestions take the user's emotional state into consideration and are designed to reduce the burden of implementation.

[0366] Step 5:

[0367] The server sends the generated action suggestion to the device. The device receives it and notifies or displays it to the user in a highly visible format.

[0368] Step 6:

[0369] Users carry out suggested eco-friendly activities and provide feedback via their devices regarding the results, their impressions, and their current emotional state. This feedback is entered as detailed information, including changes in their emotions.

[0370] Step 7:

[0371] The device encrypts the user's feedback data and resends it to the server.

[0372] Step 8:

[0373] The server analyzes user feedback and quantifies the results. In this process, emotional information is used to calculate a more personalized level of environmental contribution.

[0374] Step 9:

[0375] The server prepares quantified results and progress data for the dashboard and generates visualization data to present to the user.

[0376] Step 10:

[0377] The device displays an updated dashboard, providing users with the latest environmental contributions and emotional state trends. Users then use this information to develop their next sustainable action plan.

[0378] (Example 2)

[0379] 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".

[0380] In modern society, users need concrete action plans that take into account their daily activities and emotional states in order to achieve a sustainable lifestyle. However, conventional systems struggle to provide action suggestions that adequately consider the user's emotional state, and they lack mechanisms to provide appropriate feedback on the effects of those actions. Therefore, there is a need for efficient methods to promote sustainable behavioral change in users.

[0381] 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.

[0382] In this invention, the server includes means for collecting and securely storing user activity information and emotional information, means for determining the user's emotional state using an emotion engine, and means for analyzing the collected user activity information and emotional information and using an artificial intelligence algorithm to propose sustainable actions. This makes it possible to promote sustainable behavioral change through effective action suggestions that take the user's emotional state into account and the feedback provided.

[0383] "User activity information" refers to data about users' daily behavioral patterns and habits.

[0384] "Emotional information" refers to data that indicates a user's emotional state, such as stress levels or feelings of happiness, which are quantified as psychological states.

[0385] An "emotion engine" is a software module that determines a user's emotional state based on their input data.

[0386] An "artificial intelligence algorithm" is a computational method used to analyze data and propose sustainable action plans for users.

[0387] "Natural language processing technology" is a technology used by machines to understand and process natural language, and is used to generate user-oriented suggestions.

[0388] "Environmental contribution" is an indicator that quantifies the positive impact that user behavior has on the environment.

[0389] An "interface" is a means for a user to interact with a system and plays a role in providing visual information.

[0390] This invention is a system that comprehensively utilizes user activity information and emotional information to support the realization of a sustainable lifestyle.

[0391] Users access the system using a dedicated terminal or app. Users input information about their daily activities (e.g., eating habits, commuting methods) and emotional information obtained using the emotion engine (e.g., stress levels, happiness levels). This information is AES encrypted by the terminal and securely transmitted to the server. Transmission is performed using SSL / TLS communication to ensure data security.

[0392] The server stores the received data in the database and begins analysis. The data analysis uses artificial intelligence algorithms based on machine learning libraries implemented in Python (e.g., TensorFlow and PyTorch). These algorithms analyze the user's past activity patterns and emotional states and propose optimal, sustainable actions. The proposed actions are presented to the user in an easily understandable format using natural language processing techniques.

[0393] The device receives suggestions from the server and displays them to the user. These suggestions appear as pop-up notifications or on the dashboard, with detailed instructions on specific actions to take. For example, a suggestion might be displayed such as, "Let's do some eco-friendly activities today that can be done in a short amount of time."

[0394] Users take action based on the suggestions and send feedback from their devices regarding the results and their impressions. This feedback, including emotional information, is then sent from the device to the server. The server uses this data to quantify the user's contribution to the environment and supports continuous improvement of their actions.

[0395] Progress is visualized on a dashboard. Through the dashboard, users can review activity results and changes in sentiment, which helps them plan their next actions.

[0396] For example, if a user is feeling emotionally burdened due to being busy, the system will take that emotional state into consideration and suggest easy-to-implement eco-friendly activities. For instance, a specific action such as "Let's stop by a recycling point on the way home" might be suggested.

[0397] As an example of a prompt, the following input would be given to the generating AI model: "Generate suggestions for eco-friendly activities that can be done quickly while the user is experiencing high stress. For example, please suggest recycling activities that can be done in a short amount of time."

[0398] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0399] Step 1:

[0400] Users log in to the system using a dedicated terminal or app. During this process, users input activity information (e.g., eating habits, commuting methods, etc.) and their emotional state. The terminal encrypts the user's input using AES encryption and securely transmits it to the server via SSL / TLS communication. The input information also includes the user's identifier and timestamp. The output is encrypted data.

[0401] Step 2:

[0402] The server receives encrypted data from the terminal. First, it decrypts the data and stores it in the database. The data processing performed here is a format conversion of the received data, and it is stored as a consistent data structure. The output is the raw data stored in the database.

[0403] Step 3:

[0404] The server analyzes user activity and sentiment information stored in the database. The analysis uses artificial intelligence algorithms and machine learning libraries (e.g., TensorFlow). The algorithms extract patterns based on historical data and generate optimal, sustainable action plans for each individual user. The input is raw user data, and the output is the analyzed action suggestions.

[0405] Step 4:

[0406] The server converts the generated action suggestions into text format using natural language processing technology. The text-based suggestions are then processed into a format that is easy for the user to understand. The output suggestions are then ready to be sent to the terminal.

[0407] Step 5:

[0408] The terminal displays action suggestions received from the server to the user. These suggestions are displayed via pop-up notifications and dashboards, allowing the user to easily review the provided suggestions. The input is the action suggestions in text format, and the output is the information displayed on the user's screen.

[0409] Step 6:

[0410] The user performs the suggested action and inputs the results and their impressions as feedback into the device. The device then re-encrypts this feedback information and securely transmits it to the server. The input is the user's feedback data, and the output is the encrypted feedback information.

[0411] Step 7:

[0412] The server receives feedback and analyzes the information. Based on the activity results, it quantifies the user's environmental contribution and saves it to the database as the latest information. This analysis process contributes to the evaluation of feedback data and the creation of new action suggestions. The output is the updated environmental contribution score.

[0413] (Application Example 2)

[0414] 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 as the "terminal".

[0415] In modern society, there is a growing need to promote sustainable behavior. However, understanding and readily accepting what behavior is optimal for each individual is not easy. To address this challenge, there is a need for a system that proposes and supports effective sustainable behaviors while considering the user's emotional state.

[0416] 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.

[0417] This invention includes a server that collects and securely stores user activity data and emotional data, an artificial intelligence algorithm that analyzes the collected user activity data and emotional data and proposes sustainable actions, and a means that receives user feedback and quantifies evaluations based on environmental contribution and emotional state. This makes it possible to propose sustainable actions optimized for the user, enhance their acceptance, and support the realization of sustainable cities.

[0418] "User activity data" refers to information about an individual's daily actions and habits.

[0419] "Emotional data" refers to information that indicates a user's psychological state and changes in their emotions.

[0420] An "artificial intelligence algorithm" is a computational method for generating sustainable action plans by analyzing activity data and emotional data.

[0421] "Feedback" refers to information about a user's reaction or opinion to a suggested action.

[0422] "Environmental contribution" is an indicator that quantifies the extent to which user behavior contributes to environmental sustainability.

[0423] "Emotional state-based evaluation" refers to an evaluation of the action suggestions provided, taking into account the user's emotional response.

[0424] "Natural language processing technology" is a technology that processes human language in order to propose sustainable actions in a format that is easy for users to understand.

[0425] A "dashboard" is a display screen that visualizes the progress of a user's environmental contributions and behavioral patterns.

[0426] One embodiment of this invention is a system that proposes sustainable living by using user activity data and emotional data. This system primarily utilizes a server, a user terminal, and an artificial intelligence algorithm.

[0427] The user's device provides a dedicated application for acquiring activity and emotion data. This application aggregates emotion data detected by an emotion engine, in addition to data entered by the user. This data is encrypted using the DataEncryption library and securely transmitted to the server.

[0428] The server stores the received data in a database and begins analysis using an artificial intelligence algorithm called AIEngine. This analysis generates sustainable action suggestions optimized for the user. These suggestions are presented to the user in an easy-to-understand format through natural language processing technology. Specifically, it takes emotional states into consideration and proposes sustainable actions in a manageable way.

[0429] For example, users who tend to expend a lot of energy on weekends could be suggested to use a bicycle to conserve energy. If a user is experiencing negative emotions such as stress, they might be suggested to engage in relaxing activities.

[0430] An example of a prompt message would be input to the generating AI model in the form of: "User activity data: Increased energy consumption on weekends. User emotional state: High stress level. Generate sustainable action suggestions."

[0431] Feedback is sent from the device to the server, and detailed analysis results based on the user's environmental contribution and emotional state are visualized as a dashboard to help plan the next sustainable actions.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] Users input activity data and emotional data using a dedicated application on their device. Activity data concerns the user's daily activities, such as exercise levels and commuting methods. Emotional data includes information such as stress levels and happiness levels, detected by the device's built-in emotion engine. This data is encrypted within the device using the DataEncryption library and prepared for transmission to the server.

[0435] Step 2:

[0436] The device transmits user activity and sentiment data to the server in an encrypted state. The server securely stores the received data in a database. At this stage, the input is encrypted data, and the output is decrypted structured data. The data is then processed into a format that can be used in subsequent analysis.

[0437] Step 3:

[0438] The server analyzes the data stored in the database using AIEngine. AIEngine considers the user's current activity patterns and emotional state to generate an optimal, sustainable action plan. Past data history is also considered, and the suggestions are adjusted based on medium- to long-term trends. The input is activity patterns and emotional state obtained from the user database, and the output is a sustainable action plan.

[0439] Step 4:

[0440] The generated action suggestions are sent from the server to the terminal, where they are converted into a user-friendly format using natural language processing technology. At this stage, a generative AI model is used to generate prompt sentences. An example prompt sentence is: "User activity data: Energy consumption increases on weekends. User emotional state: High stress level. Please generate sustainable action suggestions." The input is sustainable action suggestions, and the output is specific actions presented to the user.

[0441] Step 5:

[0442] The user performs the suggested action and sends the results and their comments as feedback from their device to the server. The feedback includes detailed information reflecting the user's emotional state. The server analyzes this feedback and quantifies the user's environmental contribution. This strengthens the system's foundational data for generating personalized future suggestions based on the user's behavioral history. The input is feedback information, and the output is updated environmental contribution data.

[0443] Step 6:

[0444] The server visualizes data based on updated environmental contributions and emotional states on a dashboard and provides it to the user. Users can refer to this and develop new action plans. The input is the updated analysis results, and the output is the visualized dashboard information. Based on this information, users can strive to improve their sustainable lifestyle.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] This invention is a system that supports users in easily practicing environmentally conscious lifestyles. This system collects and analyzes user activity data, proposes and implements sustainable actions based on that data, and quantifies the results.

[0462] First, users begin accessing the system through a dedicated terminal or app. They enter their profile information and provide the system with basic data for eco-friendly activities, such as their daily activity data (e.g., electricity consumption and frequently used modes of transportation). This data is encrypted by the terminal and transferred to the server via secure communication.

[0463] The server first securely stores the received data, and then analyzes it. Artificial intelligence algorithms are used for the analysis to reveal the user's lifestyle patterns and consumption trends. Based on these analysis results, the server utilizes generative AI to create specific eco-friendly activity suggestions tailored to the user. These suggested eco-activities are then displayed on the user's device in a format easily understood by the user, using natural language processing.

[0464] Users carry out suggested eco-friendly activities and report the results and feedback via their devices. This feedback is sent to a server, which quantifies the effectiveness of the activities and provides users with feedback on their environmental contribution.

[0465] Furthermore, progress and past activity history are visualized and provided to users on a dashboard. Users can use this dashboard to check the status of their eco-activities and plan further actions.

[0466] As a concrete example, if a user's power consumption is found to be high, the server will suggest "reducing air conditioner use by two hours on weekends and using more energy-efficient appliances." If the user follows the suggestion and their power consumption is reduced, the server will provide feedback to the user saying, "This week's power consumption has been reduced by 10%." This allows users to feel a sense of contributing to the environment while promoting a sustainable lifestyle.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] Users log in to the application using a dedicated terminal and enter their profile information and daily activity data. This activity data includes information such as electricity consumption and the modes of transportation used.

[0470] Step 2:

[0471] The terminal encrypts the entered user data and sends it to the server via a secure communication channel. Security protocols are applied during this process to prevent unauthorized access.

[0472] Step 3:

[0473] The server receives encrypted user data and securely stores it in the database. The stored data is then organized into a format suitable for analysis.

[0474] Step 4:

[0475] The server retrieves user data from the database and begins analysis. The analysis uses artificial intelligence algorithms to understand user behavior patterns, from which useful information is extracted.

[0476] Step 5:

[0477] Based on the data analyzed by the server, the data is sent to a generating AI to create specific eco-friendly activities tailored to the user's lifestyle. This generation process utilizes natural language processing to create suggestions in a format that is easy for the user to understand.

[0478] Step 6:

[0479] The server sends the generated eco-activity suggestions to the device. The device receives the suggestions and notifies or displays them to the user.

[0480] Step 7:

[0481] Users implement suggested eco-friendly activities and provide feedback on their results and impressions via their devices. This feedback includes details about the activities performed and their perceived effectiveness.

[0482] Step 8:

[0483] The device re-encrypts user feedback and securely sends it to the server.

[0484] Step 9:

[0485] The server analyzes the feedback received from users and quantifies the results of the activities. Specifically, it calculates things like the amount of carbon dioxide reduced and the rate of reduction in electricity consumption.

[0486] Step 10:

[0487] The server provides quantified results to the user and prepares the data for a dashboard. The results are visualized in an easy-to-understand format and presented to the user.

[0488] Step 11:

[0489] The device updates the dashboard, displaying the user's latest contributions and progress. Users can use this dashboard to plan their next eco-friendly activities.

[0490] (Example 1)

[0491] 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."

[0492] As environmental problems worsen, individual users are required to take sustainable actions in their daily lives. However, it is difficult to judge and implement which actions in daily life contribute to environmental protection. Furthermore, there is a lack of mechanisms to understand the extent to which actual actions contribute to the environment, making it difficult to voluntarily continue sustainable behavior. Therefore, there is a need for technology that utilizes users' daily data to propose specific and individually optimal sustainable actions, visualizes their effects, and encourages continuous behavioral improvement.

[0493] 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.

[0494] In this invention, the server includes means for acquiring and securely storing user activity information, means for analyzing the acquired user activity information and using artificial intelligence algorithms to propose sustainable activities, and means for creating sustainable activities suitable for the user by utilizing a generative AI model. This makes it possible to provide users with individually optimized sustainable actions and visualize the environmental contribution resulting from performing those actions.

[0495] "Users" refers to the individual people or groups who use the system.

[0496] "Activity information" refers to data related to the user's daily activities, including electricity consumption and choice of transportation.

[0497] An "artificial intelligence algorithm" refers to a computational method used to analyze large amounts of data and find patterns and trends.

[0498] A "generative AI model" is an advanced computational model used to generate optimal suggestions and information for users.

[0499] "Visualization" refers to converting data and information into a format that is easy for users to understand and presenting it as graphs, charts, and diagrams.

[0500] "Environmental contribution" refers to a numerical value that quantifies the impact that user behavior has on the environment.

[0501] A "display device" refers to a screen or dashboard that users use to view information.

[0502] This invention is a support system for users to practice environmentally conscious and sustainable living. The system acquires user activity information, analyzes it using artificial intelligence algorithms, and proposes sustainable activities using generated AI models. As a result, users receive specific and individually optimized eco-activity suggestions, along with feedback on the environmental impact of those activities in a quantified form.

[0503] server:

[0504] The server receives activity information sent from the user's device and stores it in a secure database. The received data is analyzed using artificial intelligence algorithms. This analysis reveals the user's lifestyle patterns and energy consumption trends. Based on this, the server uses a generative AI model to create and propose optimal and sustainable activities for the user. In this process, the generative AI model is used to efficiently generate new behavioral suggestions.

[0505] Terminal:

[0506] The device encrypts the user's input information and sends it to the server. It displays sustainable activity suggestions received from the server and encourages their implementation. It also provides an interface for users to report the results of their eco-activities and sends this information back to the server.

[0507] User:

[0508] Users access the system and input their activity information. This information is transmitted to the server via the terminal. They can then implement sustainable action suggestions received from the server and input the results as feedback into the terminal.

[0509] Specific example:

[0510] For example, if a user's energy consumption data shows a high trend, the server can generate specific suggestions such as "reduce air conditioner usage time and use more energy-efficient appliances." If the user actually takes action, they can receive quantified feedback such as "this week's electricity consumption was reduced by 10%."

[0511] Example of a prompt:

[0512] "Based on user electricity consumption data, generate specific action suggestions to reduce consumption. For example, suggest reducing air conditioner use or using energy-efficient appliances."

[0513] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0514] Step 1:

[0515] User:

[0516] Users input profile information and daily activity data using a dedicated terminal or application. This information includes power consumption and transportation choices. The entered data serves as the basis for subsequent processing.

[0517] Terminal:

[0518] The terminal encrypts the information entered by the user. This encryption process is crucial to ensure data security. The encrypted data is sent to the server via secure communication. At this stage, the user's input is provided to the server in the form of encrypted data.

[0519] Step 2:

[0520] server:

[0521] The server receives encrypted data sent from the terminal. The received data is first stored in a secure database. After that, it is converted into a format suitable for the analysis. Based on the converted data, an artificial intelligence algorithm is used to analyze the user's lifestyle patterns and consumption trends. The output is an analysis result showing the user's behavioral patterns.

[0522] Step 3:

[0523] server:

[0524] Based on the analysis results, the server uses a generative AI model to suggest sustainable activities suitable for the user. Here, a prompt is input to the generative AI, and appropriate suggestions are generated. For example, a prompt might be, "Generate specific suggestions to reduce usage time based on power consumption data." The output is a suggestion in a format easily understood by the user. This suggestion is then sent to the device using natural language processing in a form directly applicable to the user.

[0525] Step 4:

[0526] Terminal:

[0527] The terminal displays suggestions received from the server to the user. The suggestions are explained in a way that is easy for the user to understand and implement. For example, a concrete example such as "reduce air conditioner use by two hours on weekends" is provided. The user also inputs feedback through the terminal regarding the results of implementing the suggestions. This feedback becomes input data in the server's evaluation process.

[0528] Step 5:

[0529] server:

[0530] The server receives feedback from users and quantifies the effectiveness of their activities based on the data provided. This process involves calculating the environmental contribution. The calculation results are visualized to help users understand the impact of their activities and are displayed through a dashboard. This allows users to concretely grasp the impact their activities have on the environment and encourages continuous improvement of their behavior.

[0531] (Application Example 1)

[0532] 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."

[0533] In modern society, both individuals and cities need to share responsibility for the environment and effectively promote eco-friendly activities. However, many individuals do not have a good grasp of the specific methods and effects of eco-friendly activities, and the means to efficiently utilize urban infrastructure are not yet fully developed. To solve this problem and realize a sustainable society, there is a need to provide effective methods for individuals and cities to collaborate in implementing and improving eco-friendly activities.

[0534] 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.

[0535] This invention includes a server that collects and securely stores user activity data, analyzes the collected user activity data in combination with urban infrastructure information to propose optimal eco-activities, and visualizes the analysis results and contribution data, presents them to the user, and encourages the use of public facilities and transportation. This makes it possible for individual sustainable actions to be linked to urban-wide eco-activities, and for contributions to the environment to be quantified and visualized.

[0536] "User activity data" refers to information collected about an individual's daily activities and the resources they use (e.g., electricity, transportation).

[0537] "Means of secure storage" refers to methods of encrypting collected data and storing it on a server using appropriate protocols.

[0538] An "artificial intelligence algorithm" is a technology that analyzes large amounts of data, identifies patterns and trends, and proposes sustainable actions.

[0539] "Urban infrastructure information" refers to data on the usage of public facilities and transportation systems, energy supply systems, and so on.

[0540] "Optimal eco-activities" refer to a series of processes that propose actions that individuals can take based on analysis results and that contribute to the environment.

[0541] "Visualization" is a method of representing analysis results and contributions using graphs, charts, and other visual aids so that users can understand them intuitively.

[0542] "Encouraging the use of public facilities and transportation" means presenting users with specific ways of using them and the benefits they receive in order to practice sustainable behavior.

[0543] In one embodiment of this invention, the user uses a dedicated application on their smartphone to input data on their daily activities. This activity data includes things like electricity consumption and the means of transportation used, and this information is transmitted to a server via an encrypted communication protocol. The server analyzes the received data using artificial intelligence algorithms such as TensorFlow to identify the user's behavioral patterns.

[0544] Based on the analysis results, the server uses the GPT series' generation AI to propose optimal eco-friendly activities. These suggestions are displayed on the smartphone screen in a user-friendly format using natural language processing technology. In some cases, the server combines urban infrastructure information with user activity data to suggest the use of public facilities and transportation. This promotes eco-friendly activities in a way that individual actions contribute to the sustainable activities of the entire city.

[0545] Users implement suggested eco-friendly activities and provide feedback to the server via a smartphone app. The server quantifies the degree of environmental contribution based on the feedback and provides it to the user. In addition, progress and past activity history are visualized and presented to the user as a dashboard to support the development of further action plans.

[0546] For example, if a user sets a goal of "reducing weekend electricity consumption," the server will use its AI-generating prompt to suggest appropriate eco-friendly activities, such as "Analyze the user's weekend electricity consumption and suggest appropriate reduction methods." By following the suggestions, the user can actually reduce their electricity consumption and feel a sense of contributing to the environment.

[0547] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0548] Step 1:

[0549] Users input daily activity data using a dedicated smartphone application. This data includes electricity consumption and modes of transportation used. The entered data is encrypted in real time and transmitted to the server via a secure communication protocol.

[0550] Step 2:

[0551] The server receives the data and securely stores it in a storage database. Here, the data is organized to facilitate subsequent analysis. As a result of this processing, user activity data is stored securely.

[0552] Step 3:

[0553] The server activates an artificial intelligence algorithm using TensorFlow to analyze user activity data. This analysis process cleanses the data, removes outliers, and then identifies user behavior patterns. As a result, user consumption trends are identified.

[0554] Step 4:

[0555] Based on the analysis results, the server uses the GPT series generative AI model to generate suggestions for eco-friendly activities. The generative AI receives instructions in the form of a prompt: "Create a sustainable action plan based on the user's activities." The generated suggestions are output in a clear and actionable format.

[0556] Step 5:

[0557] The server uses natural language processing technology to send the generated suggestions to the terminal and displays them in a language that the user can easily understand. This allows the user to grasp the specific details of the eco-friendly activities.

[0558] Step 6:

[0559] Users carry out the suggested eco-friendly activities and report the results to the server via their smartphones. This report includes changes in electricity consumption and transportation usage during the relevant period.

[0560] Step 7:

[0561] The server analyzes user feedback and quantifies the environmental contribution. Based on the feedback data, it calculates the environmental contribution as numerical data and stores it in a database.

[0562] Step 8:

[0563] The server visualizes the calculated environmental contribution and past activity data as a dashboard and provides it to the user's device. Users can use this dashboard to check the progress of their eco-activities and plan further actions.

[0564] 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.

[0565] This invention is a system that comprehensively utilizes user activity data and emotional data to support the realization of a sustainable lifestyle. Based on activity data provided by the user, as well as emotional data detected using an emotion engine, the system proposes and manages more effective action plans.

[0566] First, users log in to the system using a dedicated terminal or app. Here, users input data on their daily activities (e.g., eating habits, commuting methods, etc.) as well as their emotional state (e.g., stress level, happiness level, etc.) provided by the emotion engine. This information is encrypted by the terminal and securely transmitted to the server.

[0567] The server first stores the received user activity and emotional data in a database and then begins analysis. An artificial intelligence algorithm is used for the analysis. This algorithm creates a sustainable action plan based on the user's lifestyle patterns and emotional state. The generated action suggestions are then displayed on the user's device in an easily understandable format through natural language processing.

[0568] Proposed eco-friendly activities become more effective and acceptable by taking into account the user's emotional state. For example, if a user is feeling stressed, small, manageable eco-friendly activities are suggested to reduce the user's burden.

[0569] Users perform the suggested actions and report the results and their impressions as feedback from their devices. Since this feedback data also includes emotional information, the server quantifies a more detailed environmental contribution based on the activity results and provides it to the user.

[0570] Furthermore, the progress of each user's behavior and emotional data is visualized on a dashboard. Based on this information, users can plan their next actions. For example, if a user is experiencing emotional stress due to "busyness," the system provides suggestions for eco-friendly activities that require less effort, making it easier for them to continue sustainable behavior.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] Users log in to the application using a dedicated terminal and input activity data and emotional state. Emotional state is information obtained using an emotion engine and includes indicators such as stress and happiness.

[0574] Step 2:

[0575] The device encrypts this data and sends all data to the server via a secure channel. Here, data is transmitted according to appropriate security protocols to ensure user privacy.

[0576] Step 3:

[0577] The server stores the received user activity and sentiment data in a database, preparing it for analysis. At this point, the data is organized by category and formatted to suit specific algorithmic processing.

[0578] Step 4:

[0579] The server uses artificial intelligence algorithms to analyze the user's activity patterns and emotional state. Based on the analysis results, it generates personalized, sustainable action suggestions using AI. These suggestions take the user's emotional state into consideration and are designed to reduce the burden of implementation.

[0580] Step 5:

[0581] The server sends the generated action suggestion to the device. The device receives it and notifies or displays it to the user in a highly visible format.

[0582] Step 6:

[0583] Users carry out suggested eco-friendly activities and provide feedback via their devices regarding the results, their impressions, and their current emotional state. This feedback is entered as detailed information, including changes in their emotions.

[0584] Step 7:

[0585] The device encrypts the user's feedback data and resends it to the server.

[0586] Step 8:

[0587] The server analyzes user feedback and quantifies the results. In this process, emotional information is used to calculate a more personalized level of environmental contribution.

[0588] Step 9:

[0589] The server prepares quantified results and progress data for the dashboard and generates visualization data to present to the user.

[0590] Step 10:

[0591] The device displays an updated dashboard, providing users with the latest environmental contributions and emotional state trends. Users then use this information to develop their next sustainable action plan.

[0592] (Example 2)

[0593] 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."

[0594] In modern society, users need concrete action plans that take into account their daily activities and emotional states in order to achieve a sustainable lifestyle. However, conventional systems struggle to provide action suggestions that adequately consider the user's emotional state, and they lack mechanisms to provide appropriate feedback on the effects of those actions. Therefore, there is a need for efficient methods to promote sustainable behavioral change in users.

[0595] 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.

[0596] In this invention, the server includes means for collecting and securely storing user activity information and emotional information, means for determining the user's emotional state using an emotion engine, and means for analyzing the collected user activity information and emotional information and using an artificial intelligence algorithm to propose sustainable actions. This makes it possible to promote sustainable behavioral change through effective action suggestions that take the user's emotional state into account and the feedback provided.

[0597] "User activity information" refers to data about users' daily behavioral patterns and habits.

[0598] "Emotional information" refers to data that indicates a user's emotional state, such as stress levels or feelings of happiness, which are quantified as psychological states.

[0599] An "emotion engine" is a software module that determines a user's emotional state based on their input data.

[0600] An "artificial intelligence algorithm" is a computational method used to analyze data and propose sustainable action plans for users.

[0601] "Natural language processing technology" is a technology used by machines to understand and process natural language, and is used to generate user-oriented suggestions.

[0602] "Environmental contribution" is an indicator that quantifies the positive impact that user behavior has on the environment.

[0603] An "interface" is a means for a user to interact with a system and plays a role in providing visual information.

[0604] This invention is a system that comprehensively utilizes user activity information and emotional information to support the realization of a sustainable lifestyle.

[0605] Users access the system using a dedicated terminal or app. Users input information about their daily activities (e.g., eating habits, commuting methods) and emotional information obtained using the emotion engine (e.g., stress levels, happiness levels). This information is AES encrypted by the terminal and securely transmitted to the server. Transmission is performed using SSL / TLS communication to ensure data security.

[0606] The server stores the received data in the database and begins analysis. The data analysis uses artificial intelligence algorithms based on machine learning libraries implemented in Python (e.g., TensorFlow and PyTorch). These algorithms analyze the user's past activity patterns and emotional states and propose optimal, sustainable actions. The proposed actions are presented to the user in an easily understandable format using natural language processing techniques.

[0607] The device receives suggestions from the server and displays them to the user. These suggestions appear as pop-up notifications or on the dashboard, with detailed instructions on specific actions to take. For example, a suggestion might be displayed such as, "Let's do some eco-friendly activities today that can be done in a short amount of time."

[0608] Users take action based on the suggestions and send feedback from their devices regarding the results and their impressions. This feedback, including emotional information, is then sent from the device to the server. The server uses this data to quantify the user's contribution to the environment and supports continuous improvement of their actions.

[0609] Progress is visualized on a dashboard. Through the dashboard, users can review activity results and changes in sentiment, which helps them plan their next actions.

[0610] For example, if a user is feeling emotionally burdened due to being busy, the system will take that emotional state into consideration and suggest easy-to-implement eco-friendly activities. For instance, a specific action such as "Let's stop by a recycling point on the way home" might be suggested.

[0611] As an example of a prompt, the following input would be given to the generating AI model: "Generate suggestions for eco-friendly activities that can be done quickly while the user is experiencing high stress. For example, please suggest recycling activities that can be done in a short amount of time."

[0612] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0613] Step 1:

[0614] Users log in to the system using a dedicated terminal or app. During this process, users input activity information (e.g., eating habits, commuting methods, etc.) and their emotional state. The terminal encrypts the user's input using AES encryption and securely transmits it to the server via SSL / TLS communication. The input information also includes the user's identifier and timestamp. The output is encrypted data.

[0615] Step 2:

[0616] The server receives encrypted data from the terminal. First, it decrypts the data and stores it in the database. The data processing performed here is a format conversion of the received data, and it is stored as a consistent data structure. The output is the raw data stored in the database.

[0617] Step 3:

[0618] The server analyzes user activity and sentiment information stored in the database. The analysis uses artificial intelligence algorithms and machine learning libraries (e.g., TensorFlow). The algorithms extract patterns based on historical data and generate optimal, sustainable action plans for each individual user. The input is raw user data, and the output is the analyzed action suggestions.

[0619] Step 4:

[0620] The server converts the generated action suggestions into text format using natural language processing technology. The text-based suggestions are then processed into a format that is easy for the user to understand. The output suggestions are then ready to be sent to the terminal.

[0621] Step 5:

[0622] The terminal displays action suggestions received from the server to the user. These suggestions are displayed via pop-up notifications and dashboards, allowing the user to easily review the provided suggestions. The input is the action suggestions in text format, and the output is the information displayed on the user's screen.

[0623] Step 6:

[0624] The user performs the suggested action and inputs the results and their impressions as feedback into the device. The device then re-encrypts this feedback information and securely transmits it to the server. The input is the user's feedback data, and the output is the encrypted feedback information.

[0625] Step 7:

[0626] The server receives feedback and analyzes the information. Based on the activity results, it quantifies the user's environmental contribution and saves it to the database as the latest information. This analysis process contributes to the evaluation of feedback data and the creation of new action suggestions. The output is the updated environmental contribution score.

[0627] (Application Example 2)

[0628] 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."

[0629] In modern society, there is a growing need to promote sustainable behavior. However, understanding and readily accepting what behavior is optimal for each individual is not easy. To address this challenge, there is a need for a system that proposes and supports effective sustainable behaviors while considering the user's emotional state.

[0630] 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.

[0631] This invention includes a server that collects and securely stores user activity data and emotional data, an artificial intelligence algorithm that analyzes the collected user activity data and emotional data and proposes sustainable actions, and a means that receives user feedback and quantifies evaluations based on environmental contribution and emotional state. This makes it possible to propose sustainable actions optimized for the user, enhance their acceptance, and support the realization of sustainable cities.

[0632] "User activity data" refers to information about an individual's daily actions and habits.

[0633] "Emotional data" refers to information that indicates a user's psychological state and changes in their emotions.

[0634] An "artificial intelligence algorithm" is a computational method for generating sustainable action plans by analyzing activity data and emotional data.

[0635] "Feedback" refers to information about a user's reaction or opinion to a suggested action.

[0636] "Environmental contribution" is an indicator that quantifies the extent to which user behavior contributes to environmental sustainability.

[0637] "Emotional state-based evaluation" refers to an evaluation of the action suggestions provided, taking into account the user's emotional response.

[0638] "Natural language processing technology" is a technology that processes human language in order to propose sustainable actions in a format that is easy for users to understand.

[0639] A "dashboard" is a display screen that visualizes the progress of a user's environmental contributions and behavioral patterns.

[0640] One embodiment of this invention is a system that proposes sustainable living by using user activity data and emotional data. This system primarily utilizes a server, a user terminal, and an artificial intelligence algorithm.

[0641] The user's device provides a dedicated application for acquiring activity and emotion data. This application aggregates emotion data detected by an emotion engine, in addition to data entered by the user. This data is encrypted using the DataEncryption library and securely transmitted to the server.

[0642] The server stores the received data in a database and begins analysis using an artificial intelligence algorithm called AIEngine. This analysis generates sustainable action suggestions optimized for the user. These suggestions are presented to the user in an easy-to-understand format through natural language processing technology. Specifically, it takes emotional states into consideration and proposes sustainable actions in a manageable way.

[0643] For example, users who tend to expend a lot of energy on weekends could be suggested to use a bicycle to conserve energy. If a user is experiencing negative emotions such as stress, they might be suggested to engage in relaxing activities.

[0644] An example of a prompt message would be input to the generating AI model in the form of: "User activity data: Increased energy consumption on weekends. User emotional state: High stress level. Generate sustainable action suggestions."

[0645] Feedback is sent from the device to the server, and detailed analysis results based on the user's environmental contribution and emotional state are visualized as a dashboard to help plan the next sustainable actions.

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] Users input activity data and emotional data using a dedicated application on their device. Activity data concerns the user's daily activities, such as exercise levels and commuting methods. Emotional data includes information such as stress levels and happiness levels, detected by the device's built-in emotion engine. This data is encrypted within the device using the DataEncryption library and prepared for transmission to the server.

[0649] Step 2:

[0650] The device transmits user activity and sentiment data to the server in an encrypted state. The server securely stores the received data in a database. At this stage, the input is encrypted data, and the output is decrypted structured data. The data is then processed into a format that can be used in subsequent analysis.

[0651] Step 3:

[0652] The server analyzes the data stored in the database using AIEngine. AIEngine considers the user's current activity patterns and emotional state to generate an optimal, sustainable action plan. Past data history is also considered, and the suggestions are adjusted based on medium- to long-term trends. The input is activity patterns and emotional state obtained from the user database, and the output is a sustainable action plan.

[0653] Step 4:

[0654] The generated action suggestions are sent from the server to the terminal, where they are converted into a user-friendly format using natural language processing technology. At this stage, a generative AI model is used to generate prompt sentences. An example prompt sentence is: "User activity data: Energy consumption increases on weekends. User emotional state: High stress level. Please generate sustainable action suggestions." The input is sustainable action suggestions, and the output is specific actions presented to the user.

[0655] Step 5:

[0656] The user performs the suggested action and sends the results and their comments as feedback from their device to the server. The feedback includes detailed information reflecting the user's emotional state. The server analyzes this feedback and quantifies the user's environmental contribution. This strengthens the system's foundational data for generating personalized future suggestions based on the user's behavioral history. The input is feedback information, and the output is updated environmental contribution data.

[0657] Step 6:

[0658] The server visualizes data based on updated environmental contributions and emotional states on a dashboard and provides it to the user. Users can refer to this and develop new action plans. The input is the updated analysis results, and the output is the visualized dashboard information. Based on this information, users can strive to improve their sustainable lifestyle.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] [Fourth Embodiment]

[0663] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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).

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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".

[0676] This invention is a system that supports users in easily practicing environmentally conscious lifestyles. This system collects and analyzes user activity data, proposes and implements sustainable actions based on that data, and quantifies the results.

[0677] First, users begin accessing the system through a dedicated terminal or app. They enter their profile information and provide the system with basic data for eco-friendly activities, such as their daily activity data (e.g., electricity consumption and frequently used modes of transportation). This data is encrypted by the terminal and transferred to the server via secure communication.

[0678] The server first securely stores the received data, and then analyzes it. Artificial intelligence algorithms are used for the analysis to reveal the user's lifestyle patterns and consumption trends. Based on these analysis results, the server utilizes generative AI to create specific eco-friendly activity suggestions tailored to the user. These suggested eco-activities are then displayed on the user's device in a format easily understood by the user, using natural language processing.

[0679] Users carry out suggested eco-friendly activities and report the results and feedback via their devices. This feedback is sent to a server, which quantifies the effectiveness of the activities and provides users with feedback on their environmental contribution.

[0680] Furthermore, progress and past activity history are visualized and provided to users on a dashboard. Users can use this dashboard to check the status of their eco-activities and plan further actions.

[0681] As a concrete example, if a user's power consumption is found to be high, the server will suggest "reducing air conditioner use by two hours on weekends and using more energy-efficient appliances." If the user follows the suggestion and their power consumption is reduced, the server will provide feedback to the user saying, "This week's power consumption has been reduced by 10%." This allows users to feel a sense of contributing to the environment while promoting a sustainable lifestyle.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] Users log in to the application using a dedicated terminal and enter their profile information and daily activity data. This activity data includes information such as electricity consumption and the modes of transportation used.

[0685] Step 2:

[0686] The terminal encrypts the entered user data and sends it to the server via a secure communication channel. Security protocols are applied during this process to prevent unauthorized access.

[0687] Step 3:

[0688] The server receives encrypted user data and securely stores it in the database. The stored data is then organized into a format suitable for analysis.

[0689] Step 4:

[0690] The server retrieves user data from the database and begins analysis. The analysis uses artificial intelligence algorithms to understand user behavior patterns, from which useful information is extracted.

[0691] Step 5:

[0692] Based on the data analyzed by the server, the data is sent to a generating AI to create specific eco-friendly activities tailored to the user's lifestyle. This generation process utilizes natural language processing to create suggestions in a format that is easy for the user to understand.

[0693] Step 6:

[0694] The server sends the generated eco-activity suggestions to the device. The device receives the suggestions and notifies or displays them to the user.

[0695] Step 7:

[0696] Users implement suggested eco-friendly activities and provide feedback on their results and impressions via their devices. This feedback includes details about the activities performed and their perceived effectiveness.

[0697] Step 8:

[0698] The device re-encrypts user feedback and securely sends it to the server.

[0699] Step 9:

[0700] The server analyzes the feedback received from users and quantifies the results of the activities. Specifically, it calculates things like the amount of carbon dioxide reduced and the rate of reduction in electricity consumption.

[0701] Step 10:

[0702] The server provides quantified results to the user and prepares the data for a dashboard. The results are visualized in an easy-to-understand format and presented to the user.

[0703] Step 11:

[0704] The device updates the dashboard, displaying the user's latest contributions and progress. Users can use this dashboard to plan their next eco-friendly activities.

[0705] (Example 1)

[0706] 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".

[0707] As environmental problems worsen, individual users are required to take sustainable actions in their daily lives. However, it is difficult to judge and implement which actions in daily life contribute to environmental protection. Furthermore, there is a lack of mechanisms to understand the extent to which actual actions contribute to the environment, making it difficult to voluntarily continue sustainable behavior. Therefore, there is a need for technology that utilizes users' daily data to propose specific and individually optimal sustainable actions, visualizes their effects, and encourages continuous behavioral improvement.

[0708] 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.

[0709] In this invention, the server includes means for acquiring and securely storing user activity information, means for analyzing the acquired user activity information and using artificial intelligence algorithms to propose sustainable activities, and means for creating sustainable activities suitable for the user by utilizing a generative AI model. This makes it possible to provide users with individually optimized sustainable actions and visualize the environmental contribution resulting from performing those actions.

[0710] "Users" refers to the individual people or groups who use the system.

[0711] "Activity information" refers to data related to the user's daily activities, including electricity consumption and choice of transportation.

[0712] An "artificial intelligence algorithm" refers to a computational method used to analyze large amounts of data and find patterns and trends.

[0713] A "generative AI model" is an advanced computational model used to generate optimal suggestions and information for users.

[0714] "Visualization" refers to converting data and information into a format that is easy for users to understand and presenting it as graphs, charts, and diagrams.

[0715] "Environmental contribution" refers to a numerical value that quantifies the impact that user behavior has on the environment.

[0716] A "display device" refers to a screen or dashboard that users use to view information.

[0717] This invention is a support system for users to practice environmentally conscious and sustainable living. The system acquires user activity information, analyzes it using artificial intelligence algorithms, and proposes sustainable activities using generated AI models. As a result, users receive specific and individually optimized eco-activity suggestions, along with feedback on the environmental impact of those activities in a quantified form.

[0718] server:

[0719] The server receives activity information sent from the user's device and stores it in a secure database. The received data is analyzed using artificial intelligence algorithms. This analysis reveals the user's lifestyle patterns and energy consumption trends. Based on this, the server uses a generative AI model to create and propose optimal and sustainable activities for the user. In this process, the generative AI model is used to efficiently generate new behavioral suggestions.

[0720] Terminal:

[0721] The device encrypts the user's input information and sends it to the server. It displays sustainable activity suggestions received from the server and encourages their implementation. It also provides an interface for users to report the results of their eco-activities and sends this information back to the server.

[0722] User:

[0723] Users access the system and input their activity information. This information is transmitted to the server via the terminal. They can then implement sustainable action suggestions received from the server and input the results as feedback into the terminal.

[0724] Specific example:

[0725] For example, if a user's energy consumption data shows a high trend, the server can generate specific suggestions such as "reduce air conditioner usage time and use more energy-efficient appliances." If the user actually takes action, they can receive quantified feedback such as "this week's electricity consumption was reduced by 10%."

[0726] Example of a prompt:

[0727] "Based on user electricity consumption data, generate specific action suggestions to reduce consumption. For example, suggest reducing air conditioner use or using energy-efficient appliances."

[0728] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0729] Step 1:

[0730] User:

[0731] Users input profile information and daily activity data using a dedicated terminal or application. This information includes power consumption and transportation choices. The entered data serves as the basis for subsequent processing.

[0732] Terminal:

[0733] The terminal encrypts the information entered by the user. This encryption process is crucial to ensure data security. The encrypted data is sent to the server via secure communication. At this stage, the user's input is provided to the server in the form of encrypted data.

[0734] Step 2:

[0735] server:

[0736] The server receives encrypted data sent from the terminal. The received data is first stored in a secure database. After that, it is converted into a format suitable for the analysis. Based on the converted data, an artificial intelligence algorithm is used to analyze the user's lifestyle patterns and consumption trends. The output is an analysis result showing the user's behavioral patterns.

[0737] Step 3:

[0738] server:

[0739] Based on the analysis results, the server uses a generative AI model to suggest sustainable activities suitable for the user. Here, a prompt is input to the generative AI, and appropriate suggestions are generated. For example, a prompt might be, "Generate specific suggestions to reduce usage time based on power consumption data." The output is a suggestion in a format easily understood by the user. This suggestion is then sent to the device using natural language processing in a form directly applicable to the user.

[0740] Step 4:

[0741] Terminal:

[0742] The terminal displays suggestions received from the server to the user. The suggestions are explained in a way that is easy for the user to understand and implement. For example, a concrete example such as "reduce air conditioner use by two hours on weekends" is provided. The user also inputs feedback through the terminal regarding the results of implementing the suggestions. This feedback becomes input data in the server's evaluation process.

[0743] Step 5:

[0744] server:

[0745] The server receives feedback from users and quantifies the effectiveness of their activities based on the data provided. This process involves calculating the environmental contribution. The calculation results are visualized to help users understand the impact of their activities and are displayed through a dashboard. This allows users to concretely grasp the impact their activities have on the environment and encourages continuous improvement of their behavior.

[0746] (Application Example 1)

[0747] 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".

[0748] In modern society, both individuals and cities need to share responsibility for the environment and effectively promote eco-friendly activities. However, many individuals do not have a good grasp of the specific methods and effects of eco-friendly activities, and the means to efficiently utilize urban infrastructure are not yet fully developed. To solve this problem and realize a sustainable society, there is a need to provide effective methods for individuals and cities to collaborate in implementing and improving eco-friendly activities.

[0749] 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.

[0750] This invention includes a server that collects and securely stores user activity data, analyzes the collected user activity data in combination with urban infrastructure information to propose optimal eco-activities, and visualizes the analysis results and contribution data, presents them to the user, and encourages the use of public facilities and transportation. This makes it possible for individual sustainable actions to be linked to urban-wide eco-activities, and for contributions to the environment to be quantified and visualized.

[0751] "User activity data" refers to information collected about an individual's daily activities and the resources they use (e.g., electricity, transportation).

[0752] "Means of secure storage" refers to methods of encrypting collected data and storing it on a server using appropriate protocols.

[0753] An "artificial intelligence algorithm" is a technology that analyzes large amounts of data, identifies patterns and trends, and proposes sustainable actions.

[0754] "Urban infrastructure information" refers to data on the usage of public facilities and transportation systems, energy supply systems, and so on.

[0755] "Optimal eco-activities" refer to a series of processes that propose actions that individuals can take based on analysis results and that contribute to the environment.

[0756] "Visualization" is a method of representing analysis results and contributions using graphs, charts, and other visual aids so that users can understand them intuitively.

[0757] "Encouraging the use of public facilities and transportation" means presenting users with specific ways of using them and the benefits they receive in order to practice sustainable behavior.

[0758] In one embodiment of this invention, the user uses a dedicated application on their smartphone to input data on their daily activities. This activity data includes things like electricity consumption and the means of transportation used, and this information is transmitted to a server via an encrypted communication protocol. The server analyzes the received data using artificial intelligence algorithms such as TensorFlow to identify the user's behavioral patterns.

[0759] Based on the analysis results, the server uses the GPT series' generation AI to propose optimal eco-friendly activities. These suggestions are displayed on the smartphone screen in a user-friendly format using natural language processing technology. In some cases, the server combines urban infrastructure information with user activity data to suggest the use of public facilities and transportation. This promotes eco-friendly activities in a way that individual actions contribute to the sustainable activities of the entire city.

[0760] Users implement suggested eco-friendly activities and provide feedback to the server via a smartphone app. The server quantifies the degree of environmental contribution based on the feedback and provides it to the user. In addition, progress and past activity history are visualized and presented to the user as a dashboard to support the development of further action plans.

[0761] For example, if a user sets a goal of "reducing weekend electricity consumption," the server will use its AI-generating prompt to suggest appropriate eco-friendly activities, such as "Analyze the user's weekend electricity consumption and suggest appropriate reduction methods." By following the suggestions, the user can actually reduce their electricity consumption and feel a sense of contributing to the environment.

[0762] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0763] Step 1:

[0764] Users input daily activity data using a dedicated smartphone application. This data includes electricity consumption and modes of transportation used. The entered data is encrypted in real time and transmitted to the server via a secure communication protocol.

[0765] Step 2:

[0766] The server receives the data and securely stores it in a storage database. Here, the data is organized to facilitate subsequent analysis. As a result of this processing, user activity data is stored securely.

[0767] Step 3:

[0768] The server activates an artificial intelligence algorithm using TensorFlow to analyze user activity data. This analysis process cleanses the data, removes outliers, and then identifies user behavior patterns. As a result, user consumption trends are identified.

[0769] Step 4:

[0770] Based on the analysis results, the server uses the GPT series generative AI model to generate suggestions for eco-friendly activities. The generative AI receives instructions in the form of a prompt: "Create a sustainable action plan based on the user's activities." The generated suggestions are output in a clear and actionable format.

[0771] Step 5:

[0772] The server uses natural language processing technology to send the generated suggestions to the terminal and displays them in a language that the user can easily understand. This allows the user to grasp the specific details of the eco-friendly activities.

[0773] Step 6:

[0774] Users carry out the suggested eco-friendly activities and report the results to the server via their smartphones. This report includes changes in electricity consumption and transportation usage during the relevant period.

[0775] Step 7:

[0776] The server analyzes user feedback and quantifies the environmental contribution. Based on the feedback data, it calculates the environmental contribution as numerical data and stores it in a database.

[0777] Step 8:

[0778] The server visualizes the calculated environmental contribution and past activity data as a dashboard and provides it to the user's device. Users can use this dashboard to check the progress of their eco-activities and plan further actions.

[0779] 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.

[0780] This invention is a system that comprehensively utilizes user activity data and emotional data to support the realization of a sustainable lifestyle. Based on activity data provided by the user, as well as emotional data detected using an emotion engine, the system proposes and manages more effective action plans.

[0781] First, users log in to the system using a dedicated terminal or app. Here, users input data on their daily activities (e.g., eating habits, commuting methods, etc.) as well as their emotional state (e.g., stress level, happiness level, etc.) provided by the emotion engine. This information is encrypted by the terminal and securely transmitted to the server.

[0782] The server first stores the received user activity and emotional data in a database and then begins analysis. An artificial intelligence algorithm is used for the analysis. This algorithm creates a sustainable action plan based on the user's lifestyle patterns and emotional state. The generated action suggestions are then displayed on the user's device in an easily understandable format through natural language processing.

[0783] Proposed eco-friendly activities become more effective and acceptable by taking into account the user's emotional state. For example, if a user is feeling stressed, small, manageable eco-friendly activities are suggested to reduce the user's burden.

[0784] Users perform the suggested actions and report the results and their impressions as feedback from their devices. Since this feedback data also includes emotional information, the server quantifies a more detailed environmental contribution based on the activity results and provides it to the user.

[0785] Furthermore, the progress of each user's behavior and emotional data is visualized on a dashboard. Based on this information, users can plan their next actions. For example, if a user is experiencing emotional stress due to "busyness," the system provides suggestions for eco-friendly activities that require less effort, making it easier for them to continue sustainable behavior.

[0786] The following describes the processing flow.

[0787] Step 1:

[0788] Users log in to the application using a dedicated terminal and input activity data and emotional state. Emotional state is information obtained using an emotion engine and includes indicators such as stress and happiness.

[0789] Step 2:

[0790] The device encrypts this data and sends all data to the server via a secure channel. Here, data is transmitted according to appropriate security protocols to ensure user privacy.

[0791] Step 3:

[0792] The server stores the received user activity and sentiment data in a database, preparing it for analysis. At this point, the data is organized by category and formatted to suit specific algorithmic processing.

[0793] Step 4:

[0794] The server uses artificial intelligence algorithms to analyze the user's activity patterns and emotional state. Based on the analysis results, it generates personalized, sustainable action suggestions using AI. These suggestions take the user's emotional state into consideration and are designed to reduce the burden of implementation.

[0795] Step 5:

[0796] The server sends the generated action suggestion to the device. The device receives it and notifies or displays it to the user in a highly visible format.

[0797] Step 6:

[0798] Users carry out suggested eco-friendly activities and provide feedback via their devices regarding the results, their impressions, and their current emotional state. This feedback is entered as detailed information, including changes in their emotions.

[0799] Step 7:

[0800] The device encrypts the user's feedback data and resends it to the server.

[0801] Step 8:

[0802] The server analyzes user feedback and quantifies the results. In this process, emotional information is used to calculate a more personalized level of environmental contribution.

[0803] Step 9:

[0804] The server prepares quantified results and progress data for the dashboard and generates visualization data to present to the user.

[0805] Step 10:

[0806] The device displays an updated dashboard, providing users with the latest environmental contributions and emotional state trends. Users then use this information to develop their next sustainable action plan.

[0807] (Example 2)

[0808] 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".

[0809] In modern society, users need concrete action plans that take into account their daily activities and emotional states in order to achieve a sustainable lifestyle. However, conventional systems struggle to provide action suggestions that adequately consider the user's emotional state, and they lack mechanisms to provide appropriate feedback on the effects of those actions. Therefore, there is a need for efficient methods to promote sustainable behavioral change in users.

[0810] 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.

[0811] In this invention, the server includes means for collecting and securely storing user activity information and emotional information, means for determining the user's emotional state using an emotion engine, and means for analyzing the collected user activity information and emotional information and using an artificial intelligence algorithm to propose sustainable actions. This makes it possible to promote sustainable behavioral change through effective action suggestions that take the user's emotional state into account and the feedback provided.

[0812] "User activity information" refers to data about users' daily behavioral patterns and habits.

[0813] "Emotional information" refers to data that indicates a user's emotional state, such as stress levels or feelings of happiness, which are quantified as psychological states.

[0814] An "emotion engine" is a software module that determines a user's emotional state based on their input data.

[0815] An "artificial intelligence algorithm" is a computational method used to analyze data and propose sustainable action plans for users.

[0816] "Natural language processing technology" is a technology used by machines to understand and process natural language, and is used to generate user-oriented suggestions.

[0817] "Environmental contribution" is an indicator that quantifies the positive impact that user behavior has on the environment.

[0818] An "interface" is a means for a user to interact with a system and plays a role in providing visual information.

[0819] This invention is a system that comprehensively utilizes user activity information and emotional information to support the realization of a sustainable lifestyle.

[0820] Users access the system using a dedicated terminal or app. Users input information about their daily activities (e.g., eating habits, commuting methods) and emotional information obtained using the emotion engine (e.g., stress levels, happiness levels). This information is AES encrypted by the terminal and securely transmitted to the server. Transmission is performed using SSL / TLS communication to ensure data security.

[0821] The server stores the received data in the database and begins analysis. The data analysis uses artificial intelligence algorithms based on machine learning libraries implemented in Python (e.g., TensorFlow and PyTorch). These algorithms analyze the user's past activity patterns and emotional states and propose optimal, sustainable actions. The proposed actions are presented to the user in an easily understandable format using natural language processing techniques.

[0822] The device receives suggestions from the server and displays them to the user. These suggestions appear as pop-up notifications or on the dashboard, with detailed instructions on specific actions to take. For example, a suggestion might be displayed such as, "Let's do some eco-friendly activities today that can be done in a short amount of time."

[0823] Users take action based on the suggestions and send feedback from their devices regarding the results and their impressions. This feedback, including emotional information, is then sent from the device to the server. The server uses this data to quantify the user's contribution to the environment and supports continuous improvement of their actions.

[0824] Progress is visualized on a dashboard. Through the dashboard, users can review activity results and changes in sentiment, which helps them plan their next actions.

[0825] For example, if a user is feeling emotionally burdened due to being busy, the system will take that emotional state into consideration and suggest easy-to-implement eco-friendly activities. For instance, a specific action such as "Let's stop by a recycling point on the way home" might be suggested.

[0826] As an example of a prompt, the following input would be given to the generating AI model: "Generate suggestions for eco-friendly activities that can be done quickly while the user is experiencing high stress. For example, please suggest recycling activities that can be done in a short amount of time."

[0827] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0828] Step 1:

[0829] Users log in to the system using a dedicated terminal or app. During this process, users input activity information (e.g., eating habits, commuting methods, etc.) and their emotional state. The terminal encrypts the user's input using AES encryption and securely transmits it to the server via SSL / TLS communication. The input information also includes the user's identifier and timestamp. The output is encrypted data.

[0830] Step 2:

[0831] The server receives encrypted data from the terminal. First, it decrypts the data and stores it in the database. The data processing performed here is a format conversion of the received data, and it is stored as a consistent data structure. The output is the raw data stored in the database.

[0832] Step 3:

[0833] The server analyzes user activity and sentiment information stored in the database. The analysis uses artificial intelligence algorithms and machine learning libraries (e.g., TensorFlow). The algorithms extract patterns based on historical data and generate optimal, sustainable action plans for each individual user. The input is raw user data, and the output is the analyzed action suggestions.

[0834] Step 4:

[0835] The server converts the generated action suggestions into text format using natural language processing technology. The text-based suggestions are then processed into a format that is easy for the user to understand. The output suggestions are then ready to be sent to the terminal.

[0836] Step 5:

[0837] The terminal displays action suggestions received from the server to the user. These suggestions are displayed via pop-up notifications and dashboards, allowing the user to easily review the provided suggestions. The input is the action suggestions in text format, and the output is the information displayed on the user's screen.

[0838] Step 6:

[0839] The user performs the suggested action and inputs the results and their impressions as feedback into the device. The device then re-encrypts this feedback information and securely transmits it to the server. The input is the user's feedback data, and the output is the encrypted feedback information.

[0840] Step 7:

[0841] The server receives feedback and analyzes the information. Based on the activity results, it quantifies the user's environmental contribution and saves it to the database as the latest information. This analysis process contributes to the evaluation of feedback data and the creation of new action suggestions. The output is the updated environmental contribution score.

[0842] (Application Example 2)

[0843] 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".

[0844] In modern society, there is a growing need to promote sustainable behavior. However, understanding and readily accepting what behavior is optimal for each individual is not easy. To address this challenge, there is a need for a system that proposes and supports effective sustainable behaviors while considering the user's emotional state.

[0845] 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.

[0846] This invention includes a server that collects and securely stores user activity data and emotional data, an artificial intelligence algorithm that analyzes the collected user activity data and emotional data and proposes sustainable actions, and a means that receives user feedback and quantifies evaluations based on environmental contribution and emotional state. This makes it possible to propose sustainable actions optimized for the user, enhance their acceptance, and support the realization of sustainable cities.

[0847] "User activity data" refers to information about an individual's daily actions and habits.

[0848] "Emotional data" refers to information that indicates a user's psychological state and changes in their emotions.

[0849] An "artificial intelligence algorithm" is a computational method for generating sustainable action plans by analyzing activity data and emotional data.

[0850] "Feedback" refers to information about a user's reaction or opinion to a suggested action.

[0851] "Environmental contribution" is an indicator that quantifies the extent to which user behavior contributes to environmental sustainability.

[0852] "Emotional state-based evaluation" refers to an evaluation of the action suggestions provided, taking into account the user's emotional response.

[0853] "Natural language processing technology" is a technology that processes human language in order to propose sustainable actions in a format that is easy for users to understand.

[0854] A "dashboard" is a display screen that visualizes the progress of a user's environmental contributions and behavioral patterns.

[0855] One embodiment of this invention is a system that proposes sustainable living by using user activity data and emotional data. This system primarily utilizes a server, a user terminal, and an artificial intelligence algorithm.

[0856] The user's device provides a dedicated application for acquiring activity and emotion data. This application aggregates emotion data detected by an emotion engine, in addition to data entered by the user. This data is encrypted using the DataEncryption library and securely transmitted to the server.

[0857] The server stores the received data in a database and begins analysis using an artificial intelligence algorithm called AIEngine. This analysis generates sustainable action suggestions optimized for the user. These suggestions are presented to the user in an easy-to-understand format through natural language processing technology. Specifically, it takes emotional states into consideration and proposes sustainable actions in a manageable way.

[0858] For example, users who tend to expend a lot of energy on weekends could be suggested to use a bicycle to conserve energy. If a user is experiencing negative emotions such as stress, they might be suggested to engage in relaxing activities.

[0859] An example of a prompt message would be input to the generating AI model in the form of: "User activity data: Increased energy consumption on weekends. User emotional state: High stress level. Generate sustainable action suggestions."

[0860] Feedback is sent from the device to the server, and detailed analysis results based on the user's environmental contribution and emotional state are visualized as a dashboard to help plan the next sustainable actions.

[0861] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0862] Step 1:

[0863] Users input activity data and emotional data using a dedicated application on their device. Activity data concerns the user's daily activities, such as exercise levels and commuting methods. Emotional data includes information such as stress levels and happiness levels, detected by the device's built-in emotion engine. This data is encrypted within the device using the DataEncryption library and prepared for transmission to the server.

[0864] Step 2:

[0865] The device transmits user activity and sentiment data to the server in an encrypted state. The server securely stores the received data in a database. At this stage, the input is encrypted data, and the output is decrypted structured data. The data is then processed into a format that can be used in subsequent analysis.

[0866] Step 3:

[0867] The server analyzes the data stored in the database using AIEngine. AIEngine considers the user's current activity patterns and emotional state to generate an optimal, sustainable action plan. Past data history is also considered, and the suggestions are adjusted based on medium- to long-term trends. The input is activity patterns and emotional state obtained from the user database, and the output is a sustainable action plan.

[0868] Step 4:

[0869] The generated action suggestions are sent from the server to the terminal, where they are converted into a user-friendly format using natural language processing technology. At this stage, a generative AI model is used to generate prompt sentences. An example prompt sentence is: "User activity data: Energy consumption increases on weekends. User emotional state: High stress level. Please generate sustainable action suggestions." The input is sustainable action suggestions, and the output is specific actions presented to the user.

[0870] Step 5:

[0871] The user performs the suggested action and sends the results and their comments as feedback from their device to the server. The feedback includes detailed information reflecting the user's emotional state. The server analyzes this feedback and quantifies the user's environmental contribution. This strengthens the system's foundational data for generating personalized future suggestions based on the user's behavioral history. The input is feedback information, and the output is updated environmental contribution data.

[0872] Step 6:

[0873] The server visualizes data based on updated environmental contributions and emotional states on a dashboard and provides it to the user. Users can refer to this and develop new action plans. The input is the updated analysis results, and the output is the visualized dashboard information. Based on this information, users can strive to improve their sustainable lifestyle.

[0874] 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.

[0875] 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.

[0876] 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 robot 414.

[0877] 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.

[0878] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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."

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] The following is further disclosed regarding the embodiments described above.

[0896] (Claim 1)

[0897] Means for collecting and securely storing user activity data,

[0898] A method that uses artificial intelligence algorithms to analyze collected user activity data and propose sustainable actions,

[0899] A means of receiving user feedback and quantifying environmental contribution,

[0900] A means of visualizing and presenting analysis results and contribution data to users,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, which uses natural language processing technology to generate individual sustainable behaviors based on the user's activity patterns.

[0904] (Claim 3)

[0905] The system according to claim 1, which provides a dashboard that periodically updates the user's environmental contribution and facilitates improvements to action plans.

[0906] "Example 1"

[0907] (Claim 1)

[0908] A means of acquiring and securely storing user activity information,

[0909] A means of using artificial intelligence algorithms to analyze acquired user activity information and propose sustainable activities,

[0910] A means of receiving user responses and quantifying the degree of environmental contribution,

[0911] A means of visualizing and displaying analysis results and contribution information to users,

[0912] A means of creating sustainable activities tailored to users by utilizing generative AI models,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, which uses natural language processing technology to generate individual sustainable activities based on the activity trends of users.

[0916] (Claim 3)

[0917] The system according to claim 1, which provides a display device for periodically updating the user's environmental contribution and promoting the improvement of activity plans.

[0918] "Application Example 1"

[0919] (Claim 1)

[0920] Means for collecting and securely storing user activity data,

[0921] A method that uses artificial intelligence algorithms to analyze collected user activity data and propose sustainable actions,

[0922] A means of analyzing user behavior in combination with urban infrastructure information to propose optimal eco-friendly activities,

[0923] A means of receiving user feedback and quantifying environmental contribution,

[0924] A means of visualizing and presenting analysis results and contribution data to users,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, which uses natural language processing technology to generate individual sustainable behaviors based on the user's activity patterns.

[0928] (Claim 3)

[0929] The system according to claim 1, which provides a dashboard that regularly updates users' environmental contributions and encourages the improvement of action plans, thereby promoting the use of public facilities and transportation.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] Means for collecting and securely storing user activity and sentiment information,

[0933] A means of determining a user's emotional state using an emotion engine,

[0934] A means of using artificial intelligence algorithms to analyze collected user activity and emotional information and propose sustainable behaviors,

[0935] A means of receiving user feedback based on proposed actions and quantifying the degree of environmental contribution,

[0936] A means of visualizing and presenting analysis results and contribution information to users,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, which uses natural language processing technology to generate individual sustainable behaviors based on the user's activity patterns and emotional state.

[0940] (Claim 3)

[0941] The system according to claim 1, which provides an interface for periodically updating the user's environmental contribution and promoting improvements to action plans.

[0942] "Application example 2 when combining with an emotional engine"

[0943] (Claim 1)

[0944] Means for collecting and securely storing user activity data and sentiment data,

[0945] A means of using artificial intelligence algorithms to analyze collected user activity data and emotional data and propose sustainable behaviors,

[0946] A means of receiving user feedback and quantifying evaluations based on environmental contribution and emotional state,

[0947] A means of visualizing and presenting analysis results, contribution data, and sentiment data to users,

[0948] ...

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, which uses natural language processing technology to generate individual sustainable behaviors based on the user's activity patterns and emotional state.

[0952] (Claim 3)

[0953] The system according to claim 1, which provides a dashboard that periodically updates evaluations based on the user's environmental contribution and emotional state, and facilitates improvements to action plans. [Explanation of Symbols]

[0954] 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 for collecting and securely storing user activity data, A method that uses artificial intelligence algorithms to analyze collected user activity data and propose sustainable actions, A means of analyzing user behavior in combination with urban infrastructure information to propose optimal eco-friendly activities, A means of receiving user feedback and quantifying environmental contribution, A means of visualizing and presenting analysis results and contribution data to users, A system that includes this.

2. The system according to claim 1, which uses natural language processing technology to generate individual sustainable behaviors based on the user's activity patterns.

3. The system according to claim 1, which provides a dashboard that regularly updates users' environmental contributions and promotes improvements to action plans, thereby encouraging the use of public facilities and transportation.

Images