system

Abstract

We provide the system. [Solution] Means for collecting and analyzing behavioral data, A means of evaluating environmental impact based on collected data, A means for generating personalized suggestions regarding improving user behavior, Means for setting and managing environmental targets, A means to facilitate information sharing with other 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] Conventionally, there is a lack of specific guidelines and incentives for individuals and enterprises to spontaneously implement environmentally friendly actions, and sustainable measures have not been taken for environmental problems such as climate change and resource depletion. In addition, since action improvement measures suitable for each user are not provided and evaluations of environmental loads and presentation of improvement measures based on individual lifestyles are not made, it has been difficult for users to obtain specific action guidelines.

Means for Solving the Problems

[0005] This invention provides a system that collects user behavioral data and evaluates environmental impact based on that data. The system includes means to generate personalized improvement suggestions based on the analyzed data, supporting users in taking specific and effective environmental measures. It also provides means to enable users to set environmental goals, manage their progress, and be motivated to achieve them. Furthermore, it provides community support to facilitate learning from success stories and promoting further improvements by allowing users to share information with others. This enables individuals and businesses to implement specific and sustainable actions toward the environment.

[0006] "Behavioral data" refers to information related to users' daily activities, and includes specific data such as energy consumption, product purchase history, and means of transportation.

[0007] "Environmental impact" refers to the degree of influence that an individual or company's activities have on the environment, and is defined by factors such as carbon dioxide emissions and resource consumption.

[0008] "Personalized suggestions" refer to specific and appropriate advice and ideas for behavioral improvement that are generated based on individual user behavior data and preferences.

[0009] "Environmental targets" refer to specific environmental conservation goals set by the user, and include plans aimed at achieving results such as reducing energy consumption or waste.

[0010] "Progress management" refers to the process of tracking, appropriately recording, and evaluating the achievement status of set environmental objectives.

[0011] "Information sharing" refers to the act of mutually sharing and learning from other users and companies about useful knowledge and experience, such as environmental data, success stories, and improvement proposals.

[0012] "Community support" refers to a collaborative system in which multiple users and companies come together to share information about improving the environment and jointly promote further improvements. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Implementing the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered 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 disk (e.g., hard disk), or magnetic tape, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system for promoting environmentally friendly behavior among individuals and businesses, evaluating environmental impact and providing improvement measures through the collection and analysis of behavioral data. This system primarily consists of a server, terminals, and users.

[0035] Server Role

[0036] The server receives behavioral data periodically transmitted from the user's device. This data includes the user's daily activities, energy consumption, product purchase records, and means of transportation. The server analyzes this data and calculates environmental impacts such as carbon emissions and resource consumption. Furthermore, it generates personalized suggestions that take into account the user's unique characteristics and tendencies. These suggestions include specific actions to reduce energy consumption and waste.

[0037] Terminal role

[0038] The terminal functions as the user interface, receiving and displaying suggestions from the server. On the terminal, users can set environmental goals and track their progress. Rewards and badges are displayed on the terminal based on goal achievement, providing users with a sense of accomplishment.

[0039] User roles

[0040] Users are expected to input behavioral data through their devices, review suggestions for environmental improvement, and take action. They can also set their own environmental goals and adjust their daily activities based on those goals. Furthermore, they can utilize community features to share information with other users and learn from successful examples to further improve their behavior.

[0041] Specific example

[0042] For example, if a user wants to reduce their daily electricity consumption, they input their electricity usage data into a device. Based on this data, the server evaluates the energy efficiency of their current appliances and suggests replacing them with energy-efficient appliances. They can also set a goal of "reducing electricity consumption by 20% per month," and the device continuously tracks their progress. When the goal is finally achieved, the user can confirm their achievement on the device and receive an eco-badge, providing further motivation.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input data related to their daily activities, such as electricity usage, modes of transportation, and purchased products, via their devices. This allows for the accumulation of user behavioral data.

[0046] Step 2:

[0047] The device converts collected behavioral data into data packets and prepares them for transmission to a server over the network. The data is encrypted to protect privacy. The device also schedules data transmissions periodically.

[0048] Step 3:

[0049] The server receives data packets sent from the terminal and stores them in a database. It then feeds this data into an analysis engine to calculate the user's carbon dioxide emissions and resource consumption. An AI model is used in this calculation to provide a precise assessment of the environmental impact.

[0050] Step 4:

[0051] Based on the analysis results, the server generates personalized suggestions that are most relevant to the user. Specifically, these suggestions include lists of energy-saving products and tips for reducing waste. These suggestions are customized based on the user's past behavioral patterns.

[0052] Step 5:

[0053] The server sends the generated proposal along with the environmental impact assessment results to the terminal. The transmitted data is visually displayed on the user interface, allowing the user to check the details of the proposal and its environmental impact.

[0054] Step 6:

[0055] Users review the suggestions provided on their devices and act accordingly. For example, they might decide to purchase new energy-saving appliances. Users also set environmental goals and regularly check their progress on their devices.

[0056] Step 7:

[0057] The device tracks the user's progress toward environmental goals in real time and automatically generates rewards and badges based on the level of achievement. This provides users with a sense of accomplishment and additional motivation.

[0058] Step 8:

[0059] The server anonymously registers users' success stories and helpful improvement suggestions in a community database. This expands the information resources that other users can refer to. Information sharing among users is facilitated, and mutual learning is promoted.

[0060] (Example 1)

[0061] 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."

[0062] In modern society, there is a need to accurately assess the environmental impact of individual and organizational activities and propose concrete improvement measures. However, traditional methods have resulted in fragmented data collection and analysis, making it difficult to provide personalized recommendations. Furthermore, environmental goals are not adequately set and progress is not managed, and effective information sharing with other users is lacking. As a result, providing effective means to achieve sustainable behavioral change remains a challenge.

[0063] 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.

[0064] In this invention, the server includes means for analyzing behavioral information, means for evaluating environmental impact, and means for generating personalized suggestions. This makes it possible to comprehensively analyze user behavioral data and provide environmental improvement suggestions based on individual characteristics.

[0065] "Behavioral information" refers to data related to the daily activities of an individual or organization, and mainly includes energy consumption, purchase history, and means of transportation.

[0066] "Means of analysis" refers to a set of methods and tools used to process and analyze collected behavioral information, and may include statistical methods and machine learning algorithms.

[0067] "Means for evaluating environmental impact" refers to methods for calculating and evaluating environmental burdens, such as carbon dioxide emissions and resource consumption, based on behavioral information.

[0068] "Personalized suggestions" refer to specific guidelines and action plans generated based on each user's specific behavioral patterns and preferences, designed to promote environmental improvements.

[0069] A "server" refers to a central computer system that performs information analysis and generates suggestions, and is equipped with a database and processing power.

[0070] This system promotes environmentally friendly behavior among users by evaluating environmental impact and suggesting improvements. The system mainly consists of three elements: servers, terminals, and users.

[0071] The server plays a central role in receiving and analyzing behavioral information transmitted from users' terminals. This behavioral information includes electricity usage, mode of transportation, and purchase history. The server is equipped with data analysis software such as Python and R, which is used for data cleansing and analysis. Machine learning algorithms (for example, linear regression models) are also implemented to analyze each user's behavioral patterns and generate personalized environmental improvement suggestions. The generated suggestions recommend specific actions aimed at improving energy efficiency and reducing waste.

[0072] The term "device" refers to a smartphone or tablet that functions as an interface with the user, and includes a dedicated mobile application. The device can display environmental impact assessment results and improvement suggestions sent from the server. Through the device, users can set environmental goals and track their progress. Furthermore, they can collect feedback on the suggestions and send it to the server.

[0073] Users input data about their daily activities through their devices or collect behavioral information using automatic synchronization settings. Users are then asked to review suggested environmental improvement measures and take concrete action. Furthermore, through community features, users can share information with other users, learning from successful examples and improving their own behavior. For example, if a user wants to reduce their monthly electricity consumption, they can input their daily electricity usage into the app and receive suggestions, such as purchasing energy-efficient appliances, based on that data.

[0074] An example of a prompt would be: "Provide an idea for designing a recommendation algorithm that allows users to evaluate the energy efficiency of home appliances. Specifically, explain how you would collect and analyze data and make unique recommendations."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives behavioral information transmitted from the terminal. This input includes data related to the user's daily life (e.g., electricity usage, mode of transportation, purchase history). The server stores this data in a database in preparation for subsequent analysis. Upon receipt, the data is normalized and cleansed to prepare it for analysis.

[0078] Step 2:

[0079] The server evaluates environmental impact using stored behavioral data. It takes normalized behavioral data as input and executes statistical methods and machine learning algorithms to calculate carbon dioxide emissions and resource consumption. As output, it generates environmental impact assessment results for each user and stores them in a database.

[0080] Step 3:

[0081] The server uses a generative AI model to create personalized suggestions for reducing each user's environmental impact. Input includes environmental impact assessment results and past behavioral patterns, which the AI ​​model analyzes. The output generates specific action plans for improving energy efficiency and reducing waste.

[0082] Step 4:

[0083] The terminal receives personalized suggestions from the server and displays them to the user. It receives suggestion data as input and displays it visually and clearly on the user interface. The output presents suggestions that the user can review and act upon.

[0084] Step 5:

[0085] Users review suggested environmental improvements using their devices and provide feedback. They receive suggestions from the server as input and provide feedback including their own impressions and evaluations of their actual actions. This feedback is then sent back from the device to the server and used to improve the accuracy of the suggestions.

[0086] (Application Example 1)

[0087] 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."

[0088] In modern society, urban sustainability and the reduction of environmental impact are urgent priorities. However, there is a lack of concrete suggestions tailored to individual lifestyles on how individuals can effectively contribute to these goals in their daily lives. As a result, it is difficult to integrate energy consumption and waste reduction into individual routines. Furthermore, while sharing and cooperation among communities are essential for achieving environmental goals across cities, mechanisms to facilitate this are not adequately in place.

[0089] 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.

[0090] In this invention, the server includes means for collecting and analyzing behavioral information, means for evaluating environmental impact based on the collected information, and means for generating personalized improvement suggestions. This makes it possible to propose environmental improvement methods suited to each individual's daily life and to support the achievement of environmental goals for the entire city.

[0091] "Behavioral information" refers to data about a user's daily activities, means of transportation, energy consumption, and so on.

[0092] "Means of analysis" refers to the function of analyzing collected data and evaluating its environmental impact.

[0093] "Personalized improvement suggestions" are proposals that provide specific guidance for improving the environment based on the user's unique behavioral patterns.

[0094] "Environmental impact" refers to the effects on the global environment, such as carbon dioxide emissions and resource consumption, resulting from an activity.

[0095] "Means for setting and managing goals" refers to functions for tracking and managing the achievement status of environmental goals set by the user.

[0096] "Means of promoting information sharing" refer to mechanisms for sharing environmental success stories and data among users and companies to facilitate cooperation and communication.

[0097] "Urban lifestyle data" refers to behavioral data and information about lifestyle patterns of urban residents.

[0098] "Sustainable living proposals" refer to the presentation of practical advice and methods for realizing an environmentally friendly lifestyle.

[0099] This invention relates to a system for promoting sustainable living in a smart city environment. The system mainly consists of a server, terminals, and users.

[0100] The server receives behavioral information periodically transmitted from devices such as smartphones and smart glasses. This includes data on the user's daily activities, energy consumption, and mode of transportation. The server analyzes this data, uses a generative AI model to assess the environmental impact, and generates personalized improvement suggestions. Software tools such as Python and TENSORFLOW® are used for the analysis. Encrypted communication is also used to ensure the secure transmission of data.

[0101] The terminal functions as a user interface, receiving suggestions from the server and displaying them to the user. Based on these suggestions, the user can select and implement actions to improve their environment in their daily life. Furthermore, the terminal allows users to set goals and monitor their progress in real time.

[0102] Users participate in the system by recording the improvement actions they take in their own lives. Furthermore, by using the community function to share information with other users and learning from successful examples in urban areas, they can further deepen their contribution to the environment.

[0103] For example, the server suggests CO2 reductions that can be achieved by using public transportation during commutes and motivates the user on their terminal with a message like, "Let's try eco-commuting options." An example of a prompt is, "Please suggest how I can live a more eco-friendly life in the near future."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] Users input information about their daily activities using devices such as smartphones. Specifically, they record things like the distance they walk, the mode of transportation they use, and their electricity consumption. The entered data is formatted on the device and prepared to be sent to the server.

[0107] Step 2:

[0108] The server receives behavioral information from the terminal. This input data includes details about the user's daily activities. Next, the server preprocesses the data, transforming it into a format suitable for the generative AI model. This ensures consistency in the information used for analysis.

[0109] Step 3:

[0110] The server analyzes the received data using a generative AI model. Specifically, it uses Python and TensorFlow to evaluate CO2 emissions and energy consumption. The output of this analysis is an evaluation result regarding the user's environmental impact.

[0111] Step 4:

[0112] The server generates personalized environmental improvement suggestions for the user based on the analysis results. For example, it might generate specific suggestions such as "how much CO2 can be reduced by using public transportation." These suggestions are formatted as text data and sent to the terminal.

[0113] Step 5:

[0114] The device receives suggestions sent from the server and displays them on the user interface. Users can easily refer to these suggestions for improvement from the displayed information. This allows users to re-evaluate their behavior in daily life and make more environmentally friendly choices.

[0115] Step 6:

[0116] Based on environmental improvement suggestions provided on the device, users select and implement specific actions. For example, they might try a recommended eco-friendly commuting route. The improvement actions taken by the user are recorded again through the device and used as input data for the next cycle.

[0117] 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.

[0118] This invention is a system for users and businesses to reduce environmental impact and promote sustainable behavior, and further incorporates an emotion engine that optimizes suggestions for behavioral improvement while taking user emotions into consideration. The system consists of key components including a server, terminals, and users.

[0119] Server Role

[0120] The server receives behavioral and emotional data transmitted from the terminal and analyzes it. Using the analysis engine, it calculates carbon dioxide emissions and resource consumption, and performs a comprehensive assessment of the environmental impact, including the user's emotional state. The emotional engine analyzes user feedback and input emotional data, and is responsible for generating suggestions that are more tailored to the user.

[0121] Terminal role

[0122] The device functions as the user interface, receiving and displaying suggestions from the server. The emotion engine allows users to input emotion data, which is then transmitted to the server via the device. Emotion-based suggestion notifications are timed and delivered in a manner best suited to the user's current emotional state. Users can also set environmental goals and monitor their progress on the device at any time.

[0123] User roles

[0124] Users input behavioral and emotional data via their devices. They also review environmental improvement suggestions provided on their devices and take concrete actions based on them. Users not only set environmental goals and manage their progress, but also utilize emotional data to make the suggestions more tailored to them. Furthermore, by sharing information with other users and learning from each other, the aim is to raise environmental awareness throughout the community.

[0125] Specific example

[0126] If the system detects that the user is tired, it offers the option of reducing activities for the day and suggests choosing energy-saving foods as a simple eco-friendly activity. Conversely, if the emotional engine determines that the user is highly motivated, it suggests more proactive actions, such as promoting the use of public transportation or investing in energy-efficient equipment. In this way, by presenting the most suitable behavioral improvement measures according to the user's emotional state, it is possible to reduce the burden on the user to take action and maximize the positive impact on the environment.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users use their devices to input daily activity data and their emotional state at the time. Activity data includes power consumption, purchase history, and mode of transportation, while emotional data includes self-reports and voice input.

[0130] Step 2:

[0131] The terminal processes the entered data, encrypts it, and then sends it to the server. Data transmission is performed periodically and in a way that protects privacy.

[0132] Step 3:

[0133] The server stores and analyzes behavioral and emotional data received from the terminal. Environmental impact is calculated using behavioral data, and emotional data is analyzed using an emotion engine to understand the user's current emotional state.

[0134] Step 4:

[0135] The server utilizes the results of the emotion engine to generate personalized suggestions that best suit the user's current emotional state. For example, if the user is feeling stressed, it will suggest simple and easy-to-implement eco-friendly activities.

[0136] Step 5:

[0137] The server sends the generated suggestions to the terminal, making them available to the user. The suggestions are displayed graphically through the user interface.

[0138] Step 6:

[0139] Users review suggested actions via their devices and take action as needed. They also set environmental goals and adjust their daily actions while monitoring their progress toward those goals on their devices.

[0140] Step 7:

[0141] The device tracks the achievement of environmental goals by comparing them with the user's behavioral records and updates the data in real time. It also provides feedback tailored to the user's emotional state.

[0142] Step 8:

[0143] The server anonymously records user success stories and effective improvement strategies in a community database, organizing and providing this information for other users to refer to. Users can then use this information to further improve their actions.

[0144] (Example 2)

[0145] 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".

[0146] In the modern era, promoting sustainable behavior and reducing environmental impact are crucial social issues. However, conventional methods have failed to adequately consider user emotions when proposing behavioral improvements, making it difficult to provide suggestions to individual users at the optimal time and in the appropriate form. Furthermore, raising environmental awareness throughout the community through information sharing among users has not been sufficiently achieved.

[0147] 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.

[0148] In this invention, the server includes means for collecting and analyzing behavioral data and emotional data; means for evaluating environmental impact based on the collected data and performing a comprehensive evaluation that takes emotional state into account; and means for generating personalized behavioral improvement suggestions according to the user's emotional data. This makes it possible to provide optimal behavioral improvement suggestions that take into account the emotional state of each individual user.

[0149] "Behavioral data" refers to records of specific activities in a user's daily life, including information such as means of transportation and consumption behavior.

[0150] "Emotional data" refers to information that indicates a user's mental state, and includes emotional indicators such as stress, happiness, and motivation.

[0151] "Environmental impact" refers to the degree of influence that human activities have on the natural environment, and includes carbon dioxide emissions and resource consumption.

[0152] An "emotion engine" refers to an algorithm and program that analyzes a user's emotional data and generates suggestions tailored to that emotional state.

[0153] "Personalized behavioral improvement suggestions" refer to specific instructions and action plans for improving the environment, optimized for a particular user, generated based on the behavioral and emotional data of individual users.

[0154] "Means of promoting information sharing" refer to functions and methods that facilitate the exchange of success stories and know-how among users and organizations, and that promote collaborative environmental awareness.

[0155] This system collects user behavioral and emotional data through server, terminal, and user interactions, evaluates environmental impact based on that data, and generates personalized recommendations.

[0156] Server Role

[0157] The server receives behavioral and emotional data transmitted from the terminal and processes this data using an analysis engine. It uses data analysis libraries such as Python and R to cleanse the data and calculate carbon dioxide emissions and resource consumption. It also utilizes natural language processing libraries to analyze emotional data. Based on this, the emotional engine works in conjunction with a generative AI model to generate specific behavioral improvement suggestions tailored to the user's emotional state. An example of a prompt might be, "Create eco-friendly activity suggestions based on the user's emotional state."

[0158] Terminal role

[0159] The device acts as the user interface, notifying the user of suggestions received from the server. These notifications are displayed as push notifications or in-app messages on devices such as smartphones and tablets. Users can input sentiment data through the interface, which is securely transmitted from the device to the server. Secure communication is ensured by using the HTTPS protocol for data transfer.

[0160] User roles

[0161] Users input behavioral and emotional data using a device during their daily lives. This includes transportation methods, consumption patterns, and emotional states. They also take specific actions based on the suggestions provided and input the results as feedback into the device. For example, they may be recommended to use eco-bags, utilize public transportation, or choose energy-saving foods. This user feedback is then reflected in future suggestions, resulting in more appropriate action plans being provided.

[0162] This invention aims to enable users to take sustainable actions without difficulty by utilizing emotional data, and to raise environmental awareness throughout the community.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Terminal role

[0166] The device collects behavioral and emotional data from the user. Users input their daily activities (e.g., modes of transportation used and products purchased) and emotional states (e.g., motivation level and stress level) through applications on the device. The input data is structured in JSON format.

[0167] Step 2:

[0168] Terminal role

[0169] The device sends collected behavioral and emotional data to the server. The data is securely transmitted to the server using the HTTPS protocol. This ensures the safe transfer of data, and the server receives the input data necessary for subsequent analysis.

[0170] Step 3:

[0171] Server Role

[0172] The server analyzes the data received from the terminal using an analysis engine. Using Python libraries such as Pandas and NumPy, behavioral data is preprocessed to calculate carbon dioxide emissions and resource consumption. Emotional data is analyzed using a natural language processing library to evaluate the user's mental state. The results of this analysis become the output for use in the next step.

[0173] Step 4:

[0174] Server Role

[0175] The server uses a generative AI model to generate behavioral improvement suggestions based on the user's emotional state. The specific prompt used is in the format of "Create eco-friendly activity suggestions based on the user's emotional state." The generated suggestions are personalized for each user and are passed on to the next step as input to encourage behavioral improvement.

[0176] Step 5:

[0177] Terminal role

[0178] The device notifies the user of behavioral improvement suggestions received from the server. These notifications are delivered via push notifications or in-app messages at the optimal time, taking into account the user's emotional state. Based on these notifications, the user can then take the recommended actions.

[0179] Step 6:

[0180] User roles

[0181] Users take specific actions based on suggestions provided by their devices. For example, they might bring their own reusable shopping bag or choose energy-efficient home appliances. Feedback on these actions is entered into the device and sent to the server, where it is reused as input data for generating suggestions in the future.

[0182] (Application Example 2)

[0183] 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".

[0184] Conventional environmental impact reduction systems have a problem in that they offer uniform behavioral improvement suggestions without considering user feelings, which reduces user motivation to follow the suggestions. Furthermore, the achievement of environmental goals and the promotion of information sharing are insufficient, making it difficult to expand sustainable activities throughout the community.

[0185] 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.

[0186] In this invention, the server includes a device for collecting and analyzing behavioral data, a device for generating suggestions for behavioral improvement that take into account the user's emotional state, and a device for facilitating information sharing with other users. This enables the suggestion of optimal eco-friendly activities based on the user's emotions and the sharing of best practices throughout the community.

[0187] "Behavioral data" refers to information about users' daily actions and activities, and is used to evaluate environmental impact and suggest improvements to behavior.

[0188] "Environmental impact" is an indicator that shows the impact and damage to the natural environment, and it evaluates the degree of ecological impact caused by actions and consumption activities.

[0189] "User emotional state" refers to the psychological and emotional state that a user is currently experiencing, and it influences their willingness to participate in eco-activities and their acceptance of proposals.

[0190] "Suggestions for behavioral improvement" are specific actions that users should take to enhance sustainability, and are personalized and generated to support users in achieving their environmental goals.

[0191] "Information sharing" refers to the act of mutually exchanging useful knowledge and success stories among users or organizations, with the aim of improving eco-friendly activities for individuals and communities as a whole.

[0192] The system for realizing this invention consists of three main components: a server, a terminal, and a user. The server analyzes behavioral and emotional data and, based on this, generates an assessment of environmental impact and suggestions for behavioral improvements optimized for the user. This process utilizes Google Cloud's Natural Language API and analytical models developed in Python. This allows the server to analyze the user's emotional state and provide more effective suggestions.

[0193] The device functions as an interface with the user, displaying suggestions sent from the server. Users input their emotional state via their smartphone, and this data is transmitted to the server in real time. The device also has the function of setting and managing the user's environmental goals. This allows users to constantly monitor their own behavior and gain incentives for improvement.

[0194] The user is the central figure in this system, and by inputting emotional and behavioral data from their device, they can receive suggestions that are best suited to them. For example, a user can receive suggestions for eco-friendly activities to do on the weekend. For instance, by sending a prompt such as, "Based on my emotional state today, what eco-friendly activity would be best?" to the system, they can receive suggestions for the most suitable eco-friendly activity.

[0195] This system will enable users to promote sustainable behaviors tailored to their individual emotional states and spread best practices for eco-friendly activities through information sharing across the community.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The device acquires the user's daily activities and emotional state. Users can manually input emotional and behavioral data using their smartphones, or automatically transmit collected data. This input includes text information as emotional data and GPS information as behavioral data. This data is sent to the server.

[0199] Step 2:

[0200] The server analyzes the emotional state based on the received data. This involves using Google Cloud's Natural Language API to perform text sentiment analysis, classifying the emotional data entered by the user into positive, negative, neutral, etc. As a result, an evaluation of the user's current emotional state is output.

[0201] Step 3:

[0202] The server uses the analyzed emotional state and behavioral data to generate suggestions for behavioral improvements optimized for the user. Using an algorithm developed in Python, it compares the user's emotional state with their behavioral history to suggest appropriate eco-friendly activities. The output consists of these eco-friendly activity suggestions, which are then sent to the terminal.

[0203] Step 4:

[0204] The device displays suggestions received from the server to the user. For example, if the user is highly motivated, it might display a notification recommending jogging. This suggestion is tailored to the user's emotional state and may include specific steps for implementation.

[0205] Step 5:

[0206] The user selects and performs an action based on the displayed suggestion. This action is tracked through the device, and the data is fed back to the server. For example, after jogging, the device records the activity and sense of accomplishment, and sends the data for the next suggestion.

[0207] Step 6:

[0208] The server receives feedback while simultaneously facilitating information sharing among users. Success stories and achievement data are shared, encouraging eco-friendly activities throughout the community. This output is stored in a database as best practices and made available to other users as needed.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention relates to a system for individuals and businesses to promote environmentally friendly behavior, evaluating environmental impact and providing improvement measures through the collection and analysis of behavioral data. This system primarily consists of a server, terminals, and users.

[0226] Server Role

[0227] The server receives behavioral data periodically transmitted from the user's device. This data includes the user's daily activities, energy consumption, product purchase records, and means of transportation. The server analyzes this data and calculates environmental impacts such as carbon emissions and resource consumption. Furthermore, it generates personalized suggestions that take into account the user's unique characteristics and tendencies. These suggestions include specific actions to reduce energy consumption and waste.

[0228] Terminal role

[0229] The terminal functions as the user interface, receiving and displaying suggestions from the server. On the terminal, users can set environmental goals and track their progress. Rewards and badges are displayed on the terminal based on goal achievement, providing users with a sense of accomplishment.

[0230] User roles

[0231] Users are expected to input behavioral data through their devices, review suggestions for environmental improvement, and take action. They can also set their own environmental goals and adjust their daily activities based on those goals. Furthermore, they can utilize community features to share information with other users and learn from successful examples to further improve their behavior.

[0232] Specific example

[0233] For example, if a user wants to reduce their daily electricity consumption, they input their electricity usage data into a device. Based on this data, the server evaluates the energy efficiency of their current appliances and suggests replacing them with energy-efficient appliances. They can also set a goal of "reducing electricity consumption by 20% per month," and the device continuously tracks their progress. When the goal is finally achieved, the user can confirm their achievement on the device and receive an eco-badge, providing further motivation.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users input data related to their daily activities, such as electricity usage, modes of transportation, and purchased products, via their devices. This allows for the accumulation of user behavioral data.

[0237] Step 2:

[0238] The device converts collected behavioral data into data packets and prepares them for transmission to a server over the network. The data is encrypted to protect privacy. The device also schedules data transmissions periodically.

[0239] Step 3:

[0240] The server receives data packets sent from the terminal and stores them in a database. It then feeds this data into an analysis engine to calculate the user's carbon dioxide emissions and resource consumption. An AI model is used in this calculation to provide a precise assessment of the environmental impact.

[0241] Step 4:

[0242] Based on the analysis results, the server generates personalized suggestions that are most relevant to the user. Specifically, these suggestions include lists of energy-saving products and tips for reducing waste. These suggestions are customized based on the user's past behavioral patterns.

[0243] Step 5:

[0244] The server sends the generated proposal along with the environmental impact assessment results to the terminal. The transmitted data is visually displayed on the user interface, allowing the user to check the details of the proposal and its environmental impact.

[0245] Step 6:

[0246] Users review the suggestions provided on their devices and act accordingly. For example, they might decide to purchase new energy-saving appliances. Users also set environmental goals and regularly check their progress on their devices.

[0247] Step 7:

[0248] The device tracks the user's progress toward environmental goals in real time and automatically generates rewards and badges based on the level of achievement. This provides users with a sense of accomplishment and additional motivation.

[0249] Step 8:

[0250] The server anonymously registers users' success stories and helpful improvement suggestions in a community database. This expands the information resources that other users can refer to. Information sharing among users is facilitated, and mutual learning is promoted.

[0251] (Example 1)

[0252] 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."

[0253] In modern society, there is a need to accurately assess the environmental impact of individual and organizational activities and propose concrete improvement measures. However, traditional methods have resulted in fragmented data collection and analysis, making it difficult to provide personalized recommendations. Furthermore, environmental goals are not adequately set and progress is not managed, and effective information sharing with other users is lacking. As a result, providing effective means to achieve sustainable behavioral change remains a challenge.

[0254] 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.

[0255] In this invention, the server includes means for analyzing behavioral information, means for evaluating environmental impact, and means for generating personalized suggestions. This makes it possible to comprehensively analyze user behavioral data and provide environmental improvement suggestions based on individual characteristics.

[0256] "Behavioral information" refers to data related to the daily activities of an individual or organization, and mainly includes energy consumption, purchase history, and means of transportation.

[0257] "Means of analysis" refers to a set of methods and tools used to process and analyze collected behavioral information, and may include statistical methods and machine learning algorithms.

[0258] "Means for evaluating environmental impact" refers to methods for calculating and evaluating environmental burdens, such as carbon dioxide emissions and resource consumption, based on behavioral information.

[0259] "Personalized suggestions" refer to specific guidelines and action plans generated based on each user's specific behavioral patterns and preferences, designed to promote environmental improvements.

[0260] A "server" refers to a central computer system that performs information analysis and generates suggestions, and is equipped with a database and processing power.

[0261] This system promotes environmentally friendly behavior among users by evaluating environmental impact and suggesting improvements. The system mainly consists of three elements: servers, terminals, and users.

[0262] The server plays a central role in receiving and analyzing behavioral information transmitted from users' terminals. This behavioral information includes electricity usage, mode of transportation, and purchase history. The server is equipped with data analysis software such as Python and R, which is used for data cleansing and analysis. Machine learning algorithms (for example, linear regression models) are also implemented to analyze each user's behavioral patterns and generate personalized environmental improvement suggestions. The generated suggestions recommend specific actions aimed at improving energy efficiency and reducing waste.

[0263] The term "device" refers to a smartphone or tablet that functions as an interface with the user, and includes a dedicated mobile application. The device can display environmental impact assessment results and improvement suggestions sent from the server. Through the device, users can set environmental goals and track their progress. Furthermore, they can collect feedback on the suggestions and send it to the server.

[0264] Users input data about their daily activities through their devices or collect behavioral information using automatic synchronization settings. Users are then asked to review suggested environmental improvement measures and take concrete action. Furthermore, through community features, users can share information with other users, learning from successful examples and improving their own behavior. For example, if a user wants to reduce their monthly electricity consumption, they can input their daily electricity usage into the app and receive suggestions, such as purchasing energy-efficient appliances, based on that data.

[0265] An example of a prompt would be: "Provide an idea for designing a recommendation algorithm that allows users to evaluate the energy efficiency of home appliances. Specifically, explain how you would collect and analyze data and make unique recommendations."

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server receives behavioral information transmitted from the terminal. This input includes data related to the user's daily life (e.g., electricity usage, mode of transportation, purchase history). The server stores this data in a database in preparation for subsequent analysis. Upon receipt, the data is normalized and cleansed to prepare it for analysis.

[0269] Step 2:

[0270] The server evaluates environmental impact using stored behavioral data. It takes normalized behavioral data as input and executes statistical methods and machine learning algorithms to calculate carbon dioxide emissions and resource consumption. As output, it generates environmental impact assessment results for each user and stores them in a database.

[0271] Step 3:

[0272] The server uses a generative AI model to create personalized suggestions for reducing each user's environmental impact. Input includes environmental impact assessment results and past behavioral patterns, which the AI ​​model analyzes. The output generates specific action plans for improving energy efficiency and reducing waste.

[0273] Step 4:

[0274] The terminal receives personalized suggestions from the server and displays them to the user. It receives suggestion data as input and displays it visually and clearly on the user interface. The output presents suggestions that the user can review and act upon.

[0275] Step 5:

[0276] Users review suggested environmental improvements using their devices and provide feedback. They receive suggestions from the server as input and provide feedback including their own impressions and evaluations of their actual actions. This feedback is then sent back from the device to the server and used to improve the accuracy of the suggestions.

[0277] (Application Example 1)

[0278] 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."

[0279] In modern society, urban sustainability and the reduction of environmental impact are urgent priorities. However, there is a lack of concrete suggestions tailored to individual lifestyles on how individuals can effectively contribute to these goals in their daily lives. As a result, it is difficult to integrate energy consumption and waste reduction into individual routines. Furthermore, while sharing and cooperation among communities are essential for achieving environmental goals across cities, mechanisms to facilitate this are not adequately in place.

[0280] 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.

[0281] In this invention, the server includes means for collecting and analyzing behavioral information, means for evaluating environmental impact based on the collected information, and means for generating personalized improvement suggestions. This makes it possible to propose environmental improvement methods suited to each individual's daily life and to support the achievement of environmental goals for the entire city.

[0282] "Behavioral information" refers to data about a user's daily activities, means of transportation, energy consumption, and so on.

[0283] "Means of analysis" refers to the function of analyzing collected data and evaluating its environmental impact.

[0284] An "individualized improvement proposal" is a proposal that provides specific guidance for environmental improvement based on the user's unique behavior patterns.

[0285] "Environmental impact" refers to the impact on the global environment such as carbon dioxide emissions and resource consumption caused by activities.

[0286] "Means for setting goals and managing progress" is a function for tracking and managing the achievement status of environmental goals set by the user.

[0287] "Means for promoting information sharing" is a mechanism for sharing successful cases and data related to the environment among users and enterprises, and promoting cooperation and communication.

[0288] "Urban life data" refers to behavioral data related to urban residents and information on living patterns.

[0289] "Sustainable life proposal" is the presentation of practical advice and methods for realizing an environmentally friendly lifestyle.

[0290] This invention relates to a system for promoting sustainable life in a smart city environment. The system mainly consists of a server, a terminal, and a user.

[0291] The server receives behavioral information regularly transmitted from terminals such as smartphones and smart glasses. This includes data related to the user's daily activities, energy consumption, and means of transportation. The server analyzes these data, evaluates the environmental impact using a generated AI model, and generates individualized improvement proposals. Software tools such as Python and TensorFlow are used for the analysis. Also, encrypted communication is performed for the secure transmission of data.

[0292] The terminal functions as a user interface, receiving suggestions from the server and displaying them to the user. Based on these suggestions, the user can select and implement actions to improve their environment in their daily life. Furthermore, the terminal allows users to set goals and monitor their progress in real time.

[0293] Users participate in the system by recording the improvement actions they take in their own lives. Furthermore, by using the community function to share information with other users and learning from successful examples in urban areas, they can further deepen their contribution to the environment.

[0294] For example, the server suggests CO2 reductions that can be achieved by using public transportation during commutes and motivates the user on their terminal with a message like, "Let's try eco-commuting options." An example of a prompt is, "Please suggest how I can live a more eco-friendly life in the near future."

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] Users input daily activity information using devices such as smartphones. Specifically, they record things like the distance walked, the mode of transportation used, and electricity consumption. The entered data is formatted on the device and prepared to be sent to the server.

[0298] Step 2:

[0299] The server receives behavioral information from the terminal. This input data includes details about the user's daily activities. Next, the server preprocesses the data, transforming it into a format suitable for the generative AI model. This ensures consistency in the information used for analysis.

[0300] Step 3:

[0301] The server analyzes the data received using the generative AI model. Specifically, it conducts an assessment of CO2 emissions and energy consumption using Python and TensorFlow. As an output of this analysis, an evaluation result regarding the user's environmental impact is obtained.

[0302] Step 4:

[0303] Based on the analysis results, the server generates personalized environmental improvement proposals for the user. For example, it generates specific proposals such as "how much CO2 can be reduced by using public transportation". This proposal is formatted as text data and sent to the terminal.

[0304] Step 5:

[0305] The terminal receives the proposal sent from the server and displays it on the user interface. The user can easily refer to the improvement proposal from this displayed information. As a result, the user can review their actions in daily life and make more environmentally friendly choices.

[0306] Step 6:

[0307] The user selects and implements specific actions based on the environmental improvement proposals provided by the terminal. For example, the user takes actions such as trying the recommended eco-commuting route. The improvement actions taken by the user are recorded again through the terminal and used as input data for the next cycle.

[0308] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0309] This invention is a system for users and businesses to reduce environmental impact and promote sustainable behavior, and further incorporates an emotion engine that optimizes suggestions for behavioral improvement while taking user emotions into consideration. The system consists of key components including a server, terminals, and users.

[0310] Server Role

[0311] The server receives behavioral and emotional data transmitted from the terminal and analyzes it. Using the analysis engine, it calculates carbon dioxide emissions and resource consumption, and performs a comprehensive assessment of the environmental impact, including the user's emotional state. The emotional engine analyzes user feedback and input emotional data, and is responsible for generating suggestions that are more tailored to the user.

[0312] Terminal role

[0313] The device functions as the user interface, receiving and displaying suggestions from the server. The emotion engine allows users to input emotion data, which is then transmitted to the server via the device. Emotion-based suggestion notifications are timed and delivered in a manner best suited to the user's current emotional state. Users can also set environmental goals and monitor their progress on the device at any time.

[0314] User roles

[0315] Users input behavioral and emotional data via their devices. They also review environmental improvement suggestions provided on their devices and take concrete actions based on them. Users not only set environmental goals and manage their progress, but also utilize emotional data to make the suggestions more tailored to them. Furthermore, by sharing information with other users and learning from each other, the aim is to raise environmental awareness throughout the community.

[0316] Specific example

[0317] If the system detects that the user is tired, it offers the option of reducing activities for the day and suggests choosing energy-saving foods as a simple eco-friendly activity. Conversely, if the emotional engine determines that the user is highly motivated, it suggests more proactive actions, such as promoting the use of public transportation or investing in energy-efficient equipment. In this way, by presenting the most suitable behavioral improvement measures according to the user's emotional state, it is possible to reduce the burden on the user to take action and maximize the positive impact on the environment.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] Users use their devices to input daily activity data and their emotional state at the time. Activity data includes power consumption, purchase history, and mode of transportation, while emotional data includes self-reports and voice input.

[0321] Step 2:

[0322] The terminal processes the entered data, encrypts it, and then sends it to the server. Data transmission is performed periodically and in a way that protects privacy.

[0323] Step 3:

[0324] The server stores and analyzes behavioral and emotional data received from the terminal. Environmental impact is calculated using behavioral data, and emotional data is analyzed using an emotion engine to understand the user's current emotional state.

[0325] Step 4:

[0326] The server utilizes the results of the emotion engine to generate personalized suggestions that best suit the user's current emotional state. For example, if the user is feeling stressed, it will suggest simple and easy-to-implement eco-friendly activities.

[0327] Step 5:

[0328] The server sends the generated suggestions to the terminal, making them available to the user. The suggestions are displayed graphically through the user interface.

[0329] Step 6:

[0330] Users review suggested actions via their devices and take action as needed. They also set environmental goals and adjust their daily actions while monitoring their progress toward those goals on their devices.

[0331] Step 7:

[0332] The device tracks the achievement of environmental goals by comparing them with the user's behavioral records and updates the data in real time. It also provides feedback tailored to the user's emotional state.

[0333] Step 8:

[0334] The server anonymously records user success stories and effective improvement strategies in a community database, organizing and providing this information for other users to refer to. Users can then use this information to further improve their actions.

[0335] (Example 2)

[0336] 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".

[0337] In the modern era, promoting sustainable behavior and reducing environmental impact are crucial social issues. However, conventional methods have failed to adequately consider user emotions when proposing behavioral improvements, making it difficult to provide suggestions to individual users at the optimal time and in the appropriate form. Furthermore, raising environmental awareness throughout the community through information sharing among users has not been sufficiently achieved.

[0338] 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.

[0339] In this invention, the server includes means for collecting and analyzing behavioral data and emotional data; means for evaluating environmental impact based on the collected data and performing a comprehensive evaluation that takes emotional state into account; and means for generating personalized behavioral improvement suggestions according to the user's emotional data. This makes it possible to provide optimal behavioral improvement suggestions that take into account the emotional state of each individual user.

[0340] "Behavioral data" refers to records of specific activities in a user's daily life, including information such as means of transportation and consumption behavior.

[0341] "Emotional data" refers to information that indicates a user's mental state, and includes emotional indicators such as stress, happiness, and motivation.

[0342] "Environmental impact" refers to the degree of influence that human activities have on the natural environment, and includes carbon dioxide emissions and resource consumption.

[0343] An "emotion engine" refers to an algorithm and program that analyzes a user's emotional data and generates suggestions tailored to that emotional state.

[0344] "Personalized behavioral improvement suggestions" refer to specific instructions and action plans for improving the environment, optimized for a particular user, generated based on the behavioral and emotional data of individual users.

[0345] "Means of promoting information sharing" refer to functions and methods that facilitate the exchange of success stories and know-how among users and organizations, and that promote collaborative environmental awareness.

[0346] This system collects user behavioral and emotional data through server, terminal, and user interactions, evaluates environmental impact based on that data, and generates personalized recommendations.

[0347] Server Role

[0348] The server receives behavioral and emotional data transmitted from the terminal and processes this data using an analysis engine. It uses data analysis libraries such as Python and R to cleanse the data and calculate carbon dioxide emissions and resource consumption. It also utilizes natural language processing libraries to analyze emotional data. Based on this, the emotional engine works in conjunction with a generative AI model to generate specific behavioral improvement suggestions tailored to the user's emotional state. An example of a prompt might be, "Create eco-friendly activity suggestions based on the user's emotional state."

[0349] Terminal role

[0350] The device acts as the user interface, notifying the user of suggestions received from the server. These notifications are displayed as push notifications or in-app messages on devices such as smartphones and tablets. Users can input sentiment data through the interface, which is securely transmitted from the device to the server. Secure communication is ensured by using the HTTPS protocol for data transfer.

[0351] User roles

[0352] Users input behavioral and emotional data using a device during their daily lives. This includes transportation methods, consumption patterns, and emotional states. They also take specific actions based on the suggestions provided and input the results as feedback into the device. For example, they may be recommended to use eco-bags, utilize public transportation, or choose energy-saving foods. This user feedback is then reflected in future suggestions, resulting in more appropriate action plans being provided.

[0353] This invention aims to enable users to take sustainable actions without difficulty by utilizing emotional data, and to raise environmental awareness throughout the community.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] Terminal role

[0357] The device collects behavioral and emotional data from the user. Users input their daily activities (e.g., modes of transportation used and products purchased) and emotional states (e.g., motivation level and stress level) through applications on the device. The input data is structured in JSON format.

[0358] Step 2:

[0359] Terminal role

[0360] The device sends collected behavioral and emotional data to the server. The data is securely transmitted to the server using the HTTPS protocol. This ensures the safe transfer of data, and the server receives the input data necessary for subsequent analysis.

[0361] Step 3:

[0362] Server Role

[0363] The server analyzes the data received from the terminal using an analysis engine. Using Python libraries such as Pandas and NumPy, behavioral data is preprocessed to calculate carbon dioxide emissions and resource consumption. Emotional data is analyzed using a natural language processing library to evaluate the user's mental state. The results of this analysis become the output for use in the next step.

[0364] Step 4:

[0365] Server Role

[0366] The server uses a generative AI model to generate behavioral improvement suggestions based on the user's emotional state. The specific prompt used is in the format of "Create eco-friendly activity suggestions based on the user's emotional state." The generated suggestions are personalized for each user and are passed on to the next step as input to encourage behavioral improvement.

[0367] Step 5:

[0368] Terminal role

[0369] The device notifies the user of behavioral improvement suggestions received from the server. These notifications are delivered via push notifications or in-app messages at the optimal time, taking into account the user's emotional state. Based on these notifications, the user can then take the recommended actions.

[0370] Step 6:

[0371] User roles

[0372] Users take specific actions based on suggestions provided by their devices. For example, they might bring their own reusable shopping bag or choose energy-efficient home appliances. Feedback on these actions is entered into the device and sent to the server, where it is reused as input data for generating suggestions in the future.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] Conventional environmental impact reduction systems have a problem in that they offer uniform behavioral improvement suggestions without considering user feelings, which reduces user motivation to follow the suggestions. Furthermore, the achievement of environmental goals and the promotion of information sharing are insufficient, making it difficult to expand sustainable activities throughout the community.

[0376] 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.

[0377] In this invention, the server includes a device for collecting and analyzing behavioral data, a device for generating suggestions for behavioral improvement that take into account the user's emotional state, and a device for facilitating information sharing with other users. This enables the suggestion of optimal eco-friendly activities based on the user's emotions and the sharing of best practices throughout the community.

[0378] "Behavioral data" refers to information about users' daily actions and activities, and is used to evaluate environmental impact and suggest improvements to behavior.

[0379] "Environmental impact" is an indicator that shows the impact and damage to the natural environment, and it evaluates the degree of ecological impact caused by actions and consumption activities.

[0380] "User emotional state" refers to the psychological and emotional state that a user is currently experiencing, and it influences their willingness to participate in eco-activities and their acceptance of proposals.

[0381] "Suggestions for behavioral improvement" are specific actions that users should take to enhance sustainability, and are personalized and generated to support users in achieving their environmental goals.

[0382] "Information sharing" refers to the act of mutually exchanging useful knowledge and success stories among users or organizations, with the aim of improving eco-friendly activities for individuals and communities as a whole.

[0383] The system for realizing this invention consists of three main components: a server, a terminal, and a user. The server analyzes behavioral and emotional data and, based on this, generates an assessment of environmental impact and suggestions for behavioral improvements optimized for the user. This process utilizes Google Cloud's Natural Language API and analytical models developed in Python. This allows the server to analyze the user's emotional state and provide more effective suggestions.

[0384] The device functions as an interface with the user, displaying suggestions sent from the server. Users input their emotional state via their smartphone, and this data is transmitted to the server in real time. The device also has the function of setting and managing the user's environmental goals. This allows users to constantly monitor their own behavior and gain incentives for improvement.

[0385] The user is the central figure in this system, and by inputting emotional and behavioral data from their device, they can receive suggestions that are best suited to them. For example, a user can receive suggestions for eco-friendly activities to do on the weekend. For instance, by sending a prompt such as, "Based on my emotional state today, what eco-friendly activity would be best?" to the system, they can receive suggestions for the most suitable eco-friendly activity.

[0386] This system will enable users to promote sustainable behaviors tailored to their individual emotional states and spread best practices for eco-friendly activities through information sharing across the community.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The device acquires the user's daily activities and emotional state. Users can manually input emotional and behavioral data using their smartphones, or automatically transmit collected data. This input includes text information as emotional data and GPS information as behavioral data. This data is sent to the server.

[0390] Step 2:

[0391] The server analyzes the emotional state based on the received data. This involves using Google Cloud's Natural Language API to perform text sentiment analysis, classifying the emotional data entered by the user into positive, negative, neutral, etc. As a result, an evaluation of the user's current emotional state is output.

[0392] Step 3:

[0393] The server uses the analyzed emotional state and behavioral data to generate suggestions for behavioral improvements optimized for the user. Using an algorithm developed in Python, it compares the user's emotional state with their behavioral history to suggest appropriate eco-friendly activities. The output consists of these eco-friendly activity suggestions, which are then sent to the terminal.

[0394] Step 4:

[0395] The device displays suggestions received from the server to the user. For example, if the user is highly motivated, it might display a notification recommending jogging. This suggestion is tailored to the user's emotional state and may include specific steps for implementation.

[0396] Step 5:

[0397] The user selects and performs an action based on the displayed suggestion. This action is tracked through the device, and the data is fed back to the server. For example, after jogging, the device records the activity and sense of accomplishment, and sends the data for the next suggestion.

[0398] Step 6:

[0399] The server receives feedback while simultaneously facilitating information sharing among users. Success stories and achievement data are shared, encouraging eco-friendly activities throughout the community. This output is stored in a database as best practices and made available to other users as needed.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] This invention relates to a system for individuals and businesses to promote environmentally friendly behavior, evaluating environmental impact and providing improvement measures through the collection and analysis of behavioral data. This system primarily consists of a server, terminals, and users.

[0417] Server Role

[0418] The server receives behavioral data periodically transmitted from the user's device. This data includes the user's daily activities, energy consumption, product purchase records, and means of transportation. The server analyzes this data and calculates environmental impacts such as carbon emissions and resource consumption. Furthermore, it generates personalized suggestions that take into account the user's unique characteristics and tendencies. These suggestions include specific actions to reduce energy consumption and waste.

[0419] Terminal role

[0420] The terminal functions as the user interface, receiving and displaying suggestions from the server. On the terminal, users can set environmental goals and track their progress. Rewards and badges are displayed on the terminal based on goal achievement, providing users with a sense of accomplishment.

[0421] User roles

[0422] Users are expected to input behavioral data through their devices, review suggestions for environmental improvement, and take action. They can also set their own environmental goals and adjust their daily activities based on those goals. Furthermore, they can utilize community features to share information with other users and learn from successful examples to further improve their behavior.

[0423] Specific example

[0424] For example, if a user wants to reduce their daily electricity consumption, they input their electricity usage data into a device. Based on this data, the server evaluates the energy efficiency of their current appliances and suggests replacing them with energy-efficient appliances. They can also set a goal of "reducing electricity consumption by 20% per month," and the device continuously tracks their progress. When the goal is finally achieved, the user can confirm their achievement on the device and receive an eco-badge, providing further motivation.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] Users input data related to their daily activities, such as electricity usage, modes of transportation, and purchased products, via their devices. This allows for the accumulation of user behavioral data.

[0428] Step 2:

[0429] The device converts collected behavioral data into data packets and prepares them for transmission to a server over the network. The data is encrypted to protect privacy. The device also schedules data transmissions periodically.

[0430] Step 3:

[0431] The server receives data packets sent from the terminal and stores them in a database. It then feeds this data into an analysis engine to calculate the user's carbon dioxide emissions and resource consumption. An AI model is used in this calculation to provide a precise assessment of the environmental impact.

[0432] Step 4:

[0433] Based on the analysis results, the server generates personalized suggestions that are most relevant to the user. Specifically, these suggestions include lists of energy-saving products and tips for reducing waste. These suggestions are customized based on the user's past behavioral patterns.

[0434] Step 5:

[0435] The server sends the generated proposal along with the environmental impact assessment results to the terminal. The transmitted data is visually displayed on the user interface, allowing the user to check the details of the proposal and its environmental impact.

[0436] Step 6:

[0437] Users review the suggestions provided on their devices and act accordingly. For example, they might decide to purchase new energy-saving appliances. Users also set environmental goals and regularly check their progress on their devices.

[0438] Step 7:

[0439] The device tracks the user's progress toward environmental goals in real time and automatically generates rewards and badges based on the level of achievement. This provides users with a sense of accomplishment and additional motivation.

[0440] Step 8:

[0441] The server anonymously registers users' success stories and helpful improvement suggestions in a community database. This expands the information resources that other users can refer to. Information sharing among users is facilitated, and mutual learning is promoted.

[0442] (Example 1)

[0443] 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."

[0444] In modern society, there is a need to accurately assess the environmental impact of individual and organizational activities and propose concrete improvement measures. However, traditional methods have resulted in fragmented data collection and analysis, making it difficult to provide personalized recommendations. Furthermore, environmental goals are not adequately set and progress is not managed, and effective information sharing with other users is lacking. As a result, providing effective means to achieve sustainable behavioral change remains a challenge.

[0445] 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.

[0446] In this invention, the server includes means for analyzing behavioral information, means for evaluating environmental impact, and means for generating personalized suggestions. This makes it possible to comprehensively analyze user behavioral data and provide environmental improvement suggestions based on individual characteristics.

[0447] "Behavioral information" refers to data related to the daily activities of an individual or organization, and mainly includes energy consumption, purchase history, and means of transportation.

[0448] "Means of analysis" refers to a set of methods and tools used to process and analyze collected behavioral information, and may include statistical methods and machine learning algorithms.

[0449] "Means for evaluating environmental impact" refers to methods for calculating and evaluating environmental burdens, such as carbon dioxide emissions and resource consumption, based on behavioral information.

[0450] "Personalized suggestions" refer to specific guidelines and action plans generated based on each user's specific behavioral patterns and preferences, designed to promote environmental improvements.

[0451] A "server" refers to a central computer system that performs information analysis and generates suggestions, and is equipped with a database and processing power.

[0452] This system promotes environmentally friendly behavior among users by evaluating environmental impact and suggesting improvements. The system mainly consists of three elements: servers, terminals, and users.

[0453] The server plays a central role in receiving and analyzing behavioral information transmitted from users' terminals. This behavioral information includes electricity usage, mode of transportation, and purchase history. The server is equipped with data analysis software such as Python and R, which is used for data cleansing and analysis. Machine learning algorithms (for example, linear regression models) are also implemented to analyze each user's behavioral patterns and generate personalized environmental improvement suggestions. The generated suggestions recommend specific actions aimed at improving energy efficiency and reducing waste.

[0454] The term "device" refers to a smartphone or tablet that functions as an interface with the user, and includes a dedicated mobile application. The device can display environmental impact assessment results and improvement suggestions sent from the server. Through the device, users can set environmental goals and track their progress. Furthermore, they can collect feedback on the suggestions and send it to the server.

[0455] Users input data about their daily activities through their devices or collect behavioral information using automatic synchronization settings. Users are then asked to review suggested environmental improvement measures and take concrete action. Furthermore, through community features, users can share information with other users, learning from successful examples and improving their own behavior. For example, if a user wants to reduce their monthly electricity consumption, they can input their daily electricity usage into the app and receive suggestions, such as purchasing energy-efficient appliances, based on that data.

[0456] An example of a prompt would be: "Provide an idea for designing a recommendation algorithm that allows users to evaluate the energy efficiency of home appliances. Specifically, explain how you would collect and analyze data and make unique recommendations."

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server receives behavioral information transmitted from the terminal. This input includes data related to the user's daily life (e.g., electricity usage, mode of transportation, purchase history). The server stores this data in a database in preparation for subsequent analysis. Upon receipt, the data is normalized and cleansed to prepare it for analysis.

[0460] Step 2:

[0461] The server evaluates environmental impact using stored behavioral data. It takes normalized behavioral data as input and executes statistical methods and machine learning algorithms to calculate carbon dioxide emissions and resource consumption. As output, it generates environmental impact assessment results for each user and stores them in a database.

[0462] Step 3:

[0463] The server uses a generative AI model to create personalized suggestions for reducing each user's environmental impact. Input includes environmental impact assessment results and past behavioral patterns, which the AI ​​model analyzes. The output generates specific action plans for improving energy efficiency and reducing waste.

[0464] Step 4:

[0465] The terminal receives personalized suggestions from the server and displays them to the user. It receives suggestion data as input and displays it visually and clearly on the user interface. The output presents suggestions that the user can review and act upon.

[0466] Step 5:

[0467] Users review suggested environmental improvements using their devices and provide feedback. They receive suggestions from the server as input and provide feedback including their own impressions and evaluations of their actual actions. This feedback is then sent back from the device to the server and used to improve the accuracy of the suggestions.

[0468] (Application Example 1)

[0469] 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."

[0470] In modern society, urban sustainability and the reduction of environmental impact are urgent priorities. However, there is a lack of concrete suggestions tailored to individual lifestyles on how individuals can effectively contribute to these goals in their daily lives. As a result, it is difficult to integrate energy consumption and waste reduction into individual routines. Furthermore, while sharing and cooperation among communities are essential for achieving environmental goals across cities, mechanisms to facilitate this are not adequately in place.

[0471] 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.

[0472] In this invention, the server includes means for collecting and analyzing behavioral information, means for evaluating environmental impact based on the collected information, and means for generating personalized improvement suggestions. This makes it possible to propose environmental improvement methods suited to each individual's daily life and to support the achievement of environmental goals for the entire city.

[0473] "Behavioral information" refers to data about a user's daily activities, means of transportation, energy consumption, and so on.

[0474] "Means of analysis" refers to the function of analyzing collected data and evaluating its environmental impact.

[0475] "Personalized improvement suggestions" are proposals that provide specific guidance for improving the environment based on the user's unique behavioral patterns.

[0476] "Environmental impact" refers to the effects on the global environment, such as carbon dioxide emissions and resource consumption, resulting from an activity.

[0477] "Means for setting and managing goals" refers to functions for tracking and managing the achievement status of environmental goals set by the user.

[0478] "Means of promoting information sharing" refer to mechanisms for sharing environmental success stories and data among users and companies to facilitate cooperation and communication.

[0479] "Urban lifestyle data" refers to behavioral data and information about lifestyle patterns of urban residents.

[0480] "Sustainable living proposals" refer to the presentation of practical advice and methods for realizing an environmentally friendly lifestyle.

[0481] This invention relates to a system for promoting sustainable living in a smart city environment. The system mainly consists of a server, terminals, and users.

[0482] The server receives behavioral information periodically transmitted from devices such as smartphones and smart glasses. This includes data on the user's daily activities, energy consumption, and mode of transportation. The server analyzes this data, uses a generative AI model to assess the environmental impact, and generates personalized improvement suggestions. Software tools such as Python and TensorFlow are used for the analysis. Encrypted communication is also used to ensure the secure transmission of data.

[0483] The terminal functions as a user interface, receiving suggestions from the server and displaying them to the user. Based on these suggestions, the user can select and implement actions to improve their environment in their daily life. Furthermore, the terminal allows users to set goals and monitor their progress in real time.

[0484] Users participate in the system by recording the improvement actions they take in their own lives. Furthermore, by using the community function to share information with other users and learning from successful examples in urban areas, they can further deepen their contribution to the environment.

[0485] For example, the server suggests CO2 reductions that can be achieved by using public transportation during commutes and motivates the user on their terminal with a message like, "Let's try eco-commuting options." An example of a prompt is, "Please suggest how I can live a more eco-friendly life in the near future."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] Users input daily activity information using devices such as smartphones. Specifically, they record things like the distance walked, the mode of transportation used, and electricity consumption. The entered data is formatted on the device and prepared to be sent to the server.

[0489] Step 2:

[0490] The server receives behavioral information from the terminal. This input data includes details about the user's daily activities. Next, the server preprocesses the data, transforming it into a format suitable for the generative AI model. This ensures consistency in the information used for analysis.

[0491] Step 3:

[0492] The server analyzes the received data using a generative AI model. Specifically, it uses Python and TensorFlow to evaluate CO2 emissions and energy consumption. The output of this analysis is an evaluation result regarding the user's environmental impact.

[0493] Step 4:

[0494] The server generates personalized environmental improvement suggestions for the user based on the analysis results. For example, it might generate specific suggestions such as "how much CO2 can be reduced by using public transportation." These suggestions are formatted as text data and sent to the terminal.

[0495] Step 5:

[0496] The device receives suggestions sent from the server and displays them on the user interface. Users can easily refer to these suggestions for improvement from the displayed information. This allows users to re-evaluate their behavior in daily life and make more environmentally friendly choices.

[0497] Step 6:

[0498] Based on environmental improvement suggestions provided on the device, users select and implement specific actions. For example, they might try a recommended eco-friendly commuting route. The improvement actions taken by the user are recorded again through the device and used as input data for the next cycle.

[0499] 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.

[0500] This invention is a system for users and businesses to reduce environmental impact and promote sustainable behavior, and further incorporates an emotion engine that optimizes suggestions for behavioral improvement while taking user emotions into consideration. The system consists of key components including a server, terminals, and users.

[0501] Server Role

[0502] The server receives behavioral and emotional data transmitted from the terminal and analyzes it. Using the analysis engine, it calculates carbon dioxide emissions and resource consumption, and performs a comprehensive assessment of the environmental impact, including the user's emotional state. The emotional engine analyzes user feedback and input emotional data, and is responsible for generating suggestions that are more tailored to the user.

[0503] Terminal role

[0504] The device functions as the user interface, receiving and displaying suggestions from the server. The emotion engine allows users to input emotion data, which is then transmitted to the server via the device. Emotion-based suggestion notifications are timed and delivered in a manner best suited to the user's current emotional state. Users can also set environmental goals and monitor their progress on the device at any time.

[0505] User roles

[0506] Users input behavioral and emotional data via their devices. They also review environmental improvement suggestions provided on their devices and take concrete actions based on them. Users not only set environmental goals and manage their progress, but also utilize emotional data to make the suggestions more tailored to them. Furthermore, by sharing information with other users and learning from each other, the aim is to raise environmental awareness throughout the community.

[0507] Specific example

[0508] If the system detects that the user is tired, it offers the option of reducing activities for the day and suggests choosing energy-saving foods as a simple eco-friendly activity. Conversely, if the emotional engine determines that the user is highly motivated, it suggests more proactive actions, such as promoting the use of public transportation or investing in energy-efficient equipment. In this way, by presenting the most suitable behavioral improvement measures according to the user's emotional state, it is possible to reduce the burden on the user to take action and maximize the positive impact on the environment.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] Users use their devices to input daily activity data and their emotional state at the time. Activity data includes power consumption, purchase history, and mode of transportation, while emotional data includes self-reports and voice input.

[0512] Step 2:

[0513] The terminal processes the entered data, encrypts it, and then sends it to the server. Data transmission is performed periodically and in a way that protects privacy.

[0514] Step 3:

[0515] The server stores and analyzes behavioral and emotional data received from the terminal. Environmental impact is calculated using behavioral data, and emotional data is analyzed using an emotion engine to understand the user's current emotional state.

[0516] Step 4:

[0517] The server utilizes the results of the emotion engine to generate personalized suggestions that best suit the user's current emotional state. For example, if the user is feeling stressed, it will suggest simple and easy-to-implement eco-friendly activities.

[0518] Step 5:

[0519] The server sends the generated suggestions to the terminal, making them available to the user. The suggestions are displayed graphically through the user interface.

[0520] Step 6:

[0521] Users review suggested actions via their devices and take action as needed. They also set environmental goals and adjust their daily actions while monitoring their progress toward those goals on their devices.

[0522] Step 7:

[0523] The device tracks the achievement of environmental goals by comparing them with the user's behavioral records and updates the data in real time. It also provides feedback tailored to the user's emotional state.

[0524] Step 8:

[0525] The server anonymously records user success stories and effective improvement strategies in a community database, organizing and providing this information for other users to refer to. Users can then use this information to further improve their actions.

[0526] (Example 2)

[0527] 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."

[0528] In the modern era, promoting sustainable behavior and reducing environmental impact are crucial social issues. However, conventional methods have failed to adequately consider user emotions when proposing behavioral improvements, making it difficult to provide suggestions to individual users at the optimal time and in the appropriate form. Furthermore, raising environmental awareness throughout the community through information sharing among users has not been sufficiently achieved.

[0529] 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.

[0530] In this invention, the server includes means for collecting and analyzing behavioral data and emotional data; means for evaluating environmental impact based on the collected data and performing a comprehensive evaluation that takes emotional state into account; and means for generating personalized behavioral improvement suggestions according to the user's emotional data. This makes it possible to provide optimal behavioral improvement suggestions that take into account the emotional state of each individual user.

[0531] "Behavioral data" refers to records of specific activities in a user's daily life, including information such as means of transportation and consumption behavior.

[0532] "Emotional data" refers to information that indicates a user's mental state, and includes emotional indicators such as stress, happiness, and motivation.

[0533] "Environmental impact" refers to the degree of influence that human activities have on the natural environment, and includes carbon dioxide emissions and resource consumption.

[0534] An "emotion engine" refers to an algorithm and program that analyzes a user's emotional data and generates suggestions tailored to that emotional state.

[0535] "Personalized behavioral improvement suggestions" refer to specific instructions and action plans for improving the environment, optimized for a particular user, generated based on the behavioral and emotional data of individual users.

[0536] "Means of promoting information sharing" refer to functions and methods that facilitate the exchange of success stories and know-how among users and organizations, and that promote collaborative environmental awareness.

[0537] This system collects user behavioral and emotional data through server, terminal, and user interactions, evaluates environmental impact based on that data, and generates personalized recommendations.

[0538] Server Role

[0539] The server receives behavioral and emotional data transmitted from the terminal and processes this data using an analysis engine. It uses data analysis libraries such as Python and R to cleanse the data and calculate carbon dioxide emissions and resource consumption. It also utilizes natural language processing libraries to analyze emotional data. Based on this, the emotional engine works in conjunction with a generative AI model to generate specific behavioral improvement suggestions tailored to the user's emotional state. An example of a prompt might be, "Create eco-friendly activity suggestions based on the user's emotional state."

[0540] Terminal role

[0541] The device acts as the user interface, notifying the user of suggestions received from the server. These notifications are displayed as push notifications or in-app messages on devices such as smartphones and tablets. Users can input sentiment data through the interface, which is securely transmitted from the device to the server. Secure communication is ensured by using the HTTPS protocol for data transfer.

[0542] User roles

[0543] Users input behavioral and emotional data using a device during their daily lives. This includes transportation methods, consumption patterns, and emotional states. They also take specific actions based on the suggestions provided and input the results as feedback into the device. For example, they may be recommended to use eco-bags, utilize public transportation, or choose energy-saving foods. This user feedback is then reflected in future suggestions, resulting in more appropriate action plans being provided.

[0544] This invention aims to enable users to take sustainable actions without difficulty by utilizing emotional data, and to raise environmental awareness throughout the community.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] Terminal role

[0548] The device collects behavioral and emotional data from the user. Users input their daily activities (e.g., modes of transportation used and products purchased) and emotional states (e.g., motivation level and stress level) through applications on the device. The input data is structured in JSON format.

[0549] Step 2:

[0550] Terminal role

[0551] The device sends collected behavioral and emotional data to the server. The data is securely transmitted to the server using the HTTPS protocol. This ensures the safe transfer of data, and the server receives the input data necessary for subsequent analysis.

[0552] Step 3:

[0553] Server Role

[0554] The server analyzes the data received from the terminal using an analysis engine. Using Python libraries such as Pandas and NumPy, behavioral data is preprocessed to calculate carbon dioxide emissions and resource consumption. Emotional data is analyzed using a natural language processing library to evaluate the user's mental state. The results of this analysis become the output for use in the next step.

[0555] Step 4:

[0556] Server Role

[0557] The server uses a generative AI model to generate behavioral improvement suggestions based on the user's emotional state. The specific prompt used is in the format of "Create eco-friendly activity suggestions based on the user's emotional state." The generated suggestions are personalized for each user and are passed on to the next step as input to encourage behavioral improvement.

[0558] Step 5:

[0559] Terminal role

[0560] The device notifies the user of behavioral improvement suggestions received from the server. These notifications are delivered via push notifications or in-app messages at the optimal time, taking into account the user's emotional state. Based on these notifications, the user can then take the recommended actions.

[0561] Step 6:

[0562] User roles

[0563] Users take specific actions based on suggestions provided by their devices. For example, they might bring their own reusable shopping bag or choose energy-efficient home appliances. Feedback on these actions is entered into the device and sent to the server, where it is reused as input data for generating suggestions in the future.

[0564] (Application Example 2)

[0565] 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."

[0566] Conventional environmental impact reduction systems have a problem in that they offer uniform behavioral improvement suggestions without considering user feelings, which reduces user motivation to follow the suggestions. Furthermore, the achievement of environmental goals and the promotion of information sharing are insufficient, making it difficult to expand sustainable activities throughout the community.

[0567] 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.

[0568] In this invention, the server includes a device for collecting and analyzing behavioral data, a device for generating suggestions for behavioral improvement that take into account the user's emotional state, and a device for facilitating information sharing with other users. This enables the suggestion of optimal eco-friendly activities based on the user's emotions and the sharing of best practices throughout the community.

[0569] "Behavioral data" refers to information about users' daily actions and activities, and is used to evaluate environmental impact and suggest improvements to behavior.

[0570] "Environmental impact" is an indicator that shows the impact and damage to the natural environment, and it evaluates the degree of ecological impact caused by actions and consumption activities.

[0571] "User emotional state" refers to the psychological and emotional state that a user is currently experiencing, and it influences their willingness to participate in eco-activities and their acceptance of proposals.

[0572] "Suggestions for behavioral improvement" are specific actions that users should take to enhance sustainability, and are personalized and generated to support users in achieving their environmental goals.

[0573] "Information sharing" refers to the act of mutually exchanging useful knowledge and success stories among users or organizations, with the aim of improving eco-friendly activities for individuals and communities as a whole.

[0574] The system for realizing this invention consists of three main components: a server, a terminal, and a user. The server analyzes behavioral and emotional data and, based on this, generates an assessment of environmental impact and suggestions for behavioral improvements optimized for the user. This process utilizes Google Cloud's Natural Language API and analytical models developed in Python. This allows the server to analyze the user's emotional state and provide more effective suggestions.

[0575] The device functions as an interface with the user, displaying suggestions sent from the server. Users input their emotional state via their smartphone, and this data is transmitted to the server in real time. The device also has the function of setting and managing the user's environmental goals. This allows users to constantly monitor their own behavior and gain incentives for improvement.

[0576] The user is the central figure in this system, and by inputting emotional and behavioral data from their device, they can receive suggestions that are best suited to them. For example, a user can receive suggestions for eco-friendly activities to do on the weekend. For instance, by sending a prompt such as, "Based on my emotional state today, what eco-friendly activity would be best?" to the system, they can receive suggestions for the most suitable eco-friendly activity.

[0577] This system will enable users to promote sustainable behaviors tailored to their individual emotional states and spread best practices for eco-friendly activities through information sharing across the community.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The device acquires the user's daily activities and emotional state. Users can manually input emotional and behavioral data using their smartphones, or automatically transmit collected data. This input includes text information as emotional data and GPS information as behavioral data. This data is sent to the server.

[0581] Step 2:

[0582] The server analyzes the emotional state based on the received data. This involves using Google Cloud's Natural Language API to perform text sentiment analysis, classifying the emotional data entered by the user into positive, negative, neutral, etc. As a result, an evaluation of the user's current emotional state is output.

[0583] Step 3:

[0584] The server uses the analyzed emotional state and behavioral data to generate suggestions for behavioral improvements optimized for the user. Using an algorithm developed in Python, it compares the user's emotional state with their behavioral history to suggest appropriate eco-friendly activities. The output consists of these eco-friendly activity suggestions, which are then sent to the terminal.

[0585] Step 4:

[0586] The device displays suggestions received from the server to the user. For example, if the user is highly motivated, it might display a notification recommending jogging. This suggestion is tailored to the user's emotional state and may include specific steps for implementation.

[0587] Step 5:

[0588] The user selects and performs an action based on the displayed suggestion. This action is tracked through the device, and the data is fed back to the server. For example, after jogging, the device records the activity and sense of accomplishment, and sends the data for the next suggestion.

[0589] Step 6:

[0590] The server receives feedback while simultaneously facilitating information sharing among users. Success stories and achievement data are shared, encouraging eco-friendly activities throughout the community. This output is stored in a database as best practices and made available to other users as needed.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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".

[0608] This invention relates to a system for individuals and businesses to promote environmentally friendly behavior, evaluating environmental impact and providing improvement measures through the collection and analysis of behavioral data. This system primarily consists of a server, terminals, and users.

[0609] Server Role

[0610] The server receives behavioral data periodically transmitted from the user's device. This data includes the user's daily activities, energy consumption, product purchase records, and means of transportation. The server analyzes this data and calculates environmental impacts such as carbon emissions and resource consumption. Furthermore, it generates personalized suggestions that take into account the user's unique characteristics and tendencies. These suggestions include specific actions to reduce energy consumption and waste.

[0611] Terminal role

[0612] The terminal functions as the user interface, receiving and displaying suggestions from the server. On the terminal, users can set environmental goals and track their progress. Rewards and badges are displayed on the terminal based on goal achievement, providing users with a sense of accomplishment.

[0613] User roles

[0614] Users are expected to input behavioral data through their devices, review suggestions for environmental improvement, and take action. They can also set their own environmental goals and adjust their daily activities based on those goals. Furthermore, they can utilize community features to share information with other users and learn from successful examples to further improve their behavior.

[0615] Specific example

[0616] For example, if a user wants to reduce their daily electricity consumption, they input their electricity usage data into a device. Based on this data, the server evaluates the energy efficiency of their current appliances and suggests replacing them with energy-efficient appliances. They can also set a goal of "reducing electricity consumption by 20% per month," and the device continuously tracks their progress. When the goal is finally achieved, the user can confirm their achievement on the device and receive an eco-badge, providing further motivation.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] Users input data related to their daily activities, such as electricity usage, modes of transportation, and purchased products, via their devices. This allows for the accumulation of user behavioral data.

[0620] Step 2:

[0621] The device converts collected behavioral data into data packets and prepares them for transmission to a server over the network. The data is encrypted to protect privacy. The device also schedules data transmissions periodically.

[0622] Step 3:

[0623] The server receives data packets sent from the terminal and stores them in a database. It then feeds this data into an analysis engine to calculate the user's carbon dioxide emissions and resource consumption. An AI model is used in this calculation to provide a precise assessment of the environmental impact.

[0624] Step 4:

[0625] Based on the analysis results, the server generates personalized suggestions that are most relevant to the user. Specifically, these suggestions include lists of energy-saving products and tips for reducing waste. These suggestions are customized based on the user's past behavioral patterns.

[0626] Step 5:

[0627] The server sends the generated proposal along with the environmental impact assessment results to the terminal. The transmitted data is visually displayed on the user interface, allowing the user to check the details of the proposal and its environmental impact.

[0628] Step 6:

[0629] Users review the suggestions provided on their devices and act accordingly. For example, they might decide to purchase new energy-saving appliances. Users also set environmental goals and regularly check their progress on their devices.

[0630] Step 7:

[0631] The device tracks the user's progress toward environmental goals in real time and automatically generates rewards and badges based on the level of achievement. This provides users with a sense of accomplishment and additional motivation.

[0632] Step 8:

[0633] The server anonymously registers users' success stories and helpful improvement suggestions in a community database. This expands the information resources that other users can refer to. Information sharing among users is facilitated, and mutual learning is promoted.

[0634] (Example 1)

[0635] 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".

[0636] In modern society, there is a need to accurately assess the environmental impact of individual and organizational activities and propose concrete improvement measures. However, traditional methods have resulted in fragmented data collection and analysis, making it difficult to provide personalized recommendations. Furthermore, environmental goals are not adequately set and progress is not managed, and effective information sharing with other users is lacking. As a result, providing effective means to achieve sustainable behavioral change remains a challenge.

[0637] 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.

[0638] In this invention, the server includes means for analyzing behavioral information, means for evaluating environmental impact, and means for generating personalized suggestions. This makes it possible to comprehensively analyze user behavioral data and provide environmental improvement suggestions based on individual characteristics.

[0639] "Behavioral information" refers to data related to the daily activities of an individual or organization, and mainly includes energy consumption, purchase history, and means of transportation.

[0640] "Means of analysis" refers to a set of methods and tools used to process and analyze collected behavioral information, and may include statistical methods and machine learning algorithms.

[0641] "Means for evaluating environmental impact" refers to methods for calculating and evaluating environmental burdens, such as carbon dioxide emissions and resource consumption, based on behavioral information.

[0642] "Personalized suggestions" refer to specific guidelines and action plans generated based on each user's specific behavioral patterns and preferences, designed to promote environmental improvements.

[0643] A "server" refers to a central computer system that performs information analysis and generates suggestions, and is equipped with a database and processing power.

[0644] This system promotes environmentally friendly behavior among users by evaluating environmental impact and suggesting improvements. The system mainly consists of three elements: servers, terminals, and users.

[0645] The server plays a central role in receiving and analyzing behavioral information transmitted from users' terminals. This behavioral information includes electricity usage, mode of transportation, and purchase history. The server is equipped with data analysis software such as Python and R, which is used for data cleansing and analysis. Machine learning algorithms (for example, linear regression models) are also implemented to analyze each user's behavioral patterns and generate personalized environmental improvement suggestions. The generated suggestions recommend specific actions aimed at improving energy efficiency and reducing waste.

[0646] The term "device" refers to a smartphone or tablet that functions as an interface with the user, and includes a dedicated mobile application. The device can display environmental impact assessment results and improvement suggestions sent from the server. Through the device, users can set environmental goals and track their progress. Furthermore, they can collect feedback on the suggestions and send it to the server.

[0647] Users input data about their daily activities through their devices or collect behavioral information using automatic synchronization settings. Users are then asked to review suggested environmental improvement measures and take concrete action. Furthermore, through community features, users can share information with other users, learning from successful examples and improving their own behavior. For example, if a user wants to reduce their monthly electricity consumption, they can input their daily electricity usage into the app and receive suggestions, such as purchasing energy-efficient appliances, based on that data.

[0648] An example of a prompt would be: "Provide an idea for designing a recommendation algorithm that allows users to evaluate the energy efficiency of home appliances. Specifically, explain how you would collect and analyze data and make unique recommendations."

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server receives behavioral information transmitted from the terminal. This input includes data related to the user's daily life (e.g., electricity usage, mode of transportation, purchase history). The server stores this data in a database in preparation for subsequent analysis. Upon receipt, the data is normalized and cleansed to prepare it for analysis.

[0652] Step 2:

[0653] The server evaluates environmental impact using stored behavioral data. It takes normalized behavioral data as input and executes statistical methods and machine learning algorithms to calculate carbon dioxide emissions and resource consumption. As output, it generates environmental impact assessment results for each user and stores them in a database.

[0654] Step 3:

[0655] The server uses a generative AI model to create personalized suggestions for reducing each user's environmental impact. Input includes environmental impact assessment results and past behavioral patterns, which the AI ​​model analyzes. The output generates specific action plans for improving energy efficiency and reducing waste.

[0656] Step 4:

[0657] The terminal receives personalized suggestions from the server and displays them to the user. It receives suggestion data as input and displays it visually and clearly on the user interface. The output presents suggestions that the user can review and act upon.

[0658] Step 5:

[0659] Users review suggested environmental improvements using their devices and provide feedback. They receive suggestions from the server as input and provide feedback including their own impressions and evaluations of their actual actions. This feedback is then sent back from the device to the server and used to improve the accuracy of the suggestions.

[0660] (Application Example 1)

[0661] 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".

[0662] In modern society, urban sustainability and the reduction of environmental impact are urgent priorities. However, there is a lack of concrete suggestions tailored to individual lifestyles on how individuals can effectively contribute to these goals in their daily lives. As a result, it is difficult to integrate energy consumption and waste reduction into individual routines. Furthermore, while sharing and cooperation among communities are essential for achieving environmental goals across cities, mechanisms to facilitate this are not adequately in place.

[0663] 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.

[0664] In this invention, the server includes means for collecting and analyzing behavioral information, means for evaluating environmental impact based on the collected information, and means for generating personalized improvement suggestions. This makes it possible to propose environmental improvement methods suited to each individual's daily life and to support the achievement of environmental goals for the entire city.

[0665] "Behavioral information" refers to data about a user's daily activities, means of transportation, energy consumption, and so on.

[0666] "Means of analysis" refers to the function of analyzing collected data and evaluating its environmental impact.

[0667] "Personalized improvement suggestions" are proposals that provide specific guidance for improving the environment based on the user's unique behavioral patterns.

[0668] "Environmental impact" refers to the effects on the global environment, such as carbon dioxide emissions and resource consumption, resulting from an activity.

[0669] "Means for setting and managing goals" refers to functions for tracking and managing the achievement status of environmental goals set by the user.

[0670] "Means of promoting information sharing" refer to mechanisms for sharing environmental success stories and data among users and companies to facilitate cooperation and communication.

[0671] "Urban lifestyle data" refers to behavioral data and information about lifestyle patterns of urban residents.

[0672] "Sustainable living proposals" refer to the presentation of practical advice and methods for realizing an environmentally friendly lifestyle.

[0673] This invention relates to a system for promoting sustainable living in a smart city environment. The system mainly consists of a server, terminals, and users.

[0674] The server receives behavioral information periodically transmitted from devices such as smartphones and smart glasses. This includes data on the user's daily activities, energy consumption, and mode of transportation. The server analyzes this data, uses a generative AI model to assess the environmental impact, and generates personalized improvement suggestions. Software tools such as Python and TensorFlow are used for the analysis. Encrypted communication is also used to ensure the secure transmission of data.

[0675] The terminal functions as a user interface, receiving suggestions from the server and displaying them to the user. Based on these suggestions, the user can select and implement actions to improve their environment in their daily life. Furthermore, the terminal allows users to set goals and monitor their progress in real time.

[0676] Users participate in the system by recording the improvement actions they take in their own lives. Furthermore, by using the community function to share information with other users and learning from successful examples in urban areas, they can further deepen their contribution to the environment.

[0677] For example, the server suggests CO2 reductions that can be achieved by using public transportation during commutes and motivates the user on their terminal with a message like, "Let's try eco-commuting options." An example of a prompt is, "Please suggest how I can live a more eco-friendly life in the near future."

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] Users input daily activity information using devices such as smartphones. Specifically, they record things like the distance walked, the mode of transportation used, and electricity consumption. The entered data is formatted on the device and prepared to be sent to the server.

[0681] Step 2:

[0682] The server receives behavioral information from the terminal. This input data includes details about the user's daily activities. Next, the server preprocesses the data, transforming it into a format suitable for the generative AI model. This ensures consistency in the information used for analysis.

[0683] Step 3:

[0684] The server analyzes the received data using a generative AI model. Specifically, it uses Python and TensorFlow to evaluate CO2 emissions and energy consumption. The output of this analysis is an evaluation result regarding the user's environmental impact.

[0685] Step 4:

[0686] The server generates personalized environmental improvement suggestions for the user based on the analysis results. For example, it might generate specific suggestions such as "how much CO2 can be reduced by using public transportation." These suggestions are formatted as text data and sent to the terminal.

[0687] Step 5:

[0688] The device receives suggestions sent from the server and displays them on the user interface. Users can easily refer to these suggestions for improvement from the displayed information. This allows users to re-evaluate their behavior in daily life and make more environmentally friendly choices.

[0689] Step 6:

[0690] Based on environmental improvement suggestions provided on the device, users select and implement specific actions. For example, they might try a recommended eco-friendly commuting route. The improvement actions taken by the user are recorded again through the device and used as input data for the next cycle.

[0691] 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.

[0692] This invention is a system for users and businesses to reduce environmental impact and promote sustainable behavior, and further incorporates an emotion engine that optimizes suggestions for behavioral improvement while taking user emotions into consideration. The system consists of key components including a server, terminals, and users.

[0693] Server Role

[0694] The server receives behavioral and emotional data transmitted from the terminal and analyzes it. Using the analysis engine, it calculates carbon dioxide emissions and resource consumption, and performs a comprehensive assessment of the environmental impact, including the user's emotional state. The emotional engine analyzes user feedback and input emotional data, and is responsible for generating suggestions that are more tailored to the user.

[0695] Terminal role

[0696] The device functions as the user interface, receiving and displaying suggestions from the server. The emotion engine allows users to input emotion data, which is then transmitted to the server via the device. Emotion-based suggestion notifications are timed and delivered in a manner best suited to the user's current emotional state. Users can also set environmental goals and monitor their progress on the device at any time.

[0697] User roles

[0698] Users input behavioral and emotional data via their devices. They also review environmental improvement suggestions provided on their devices and take concrete actions based on them. Users not only set environmental goals and manage their progress, but also utilize emotional data to make the suggestions more tailored to them. Furthermore, by sharing information with other users and learning from each other, the aim is to raise environmental awareness throughout the community.

[0699] Specific example

[0700] If the system detects that the user is tired, it offers the option of reducing activities for the day and suggests choosing energy-saving foods as a simple eco-friendly activity. Conversely, if the emotional engine determines that the user is highly motivated, it suggests more proactive actions, such as promoting the use of public transportation or investing in energy-efficient equipment. In this way, by presenting the most suitable behavioral improvement measures according to the user's emotional state, it is possible to reduce the burden on the user to take action and maximize the positive impact on the environment.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] Users use their devices to input daily activity data and their emotional state at the time. Activity data includes power consumption, purchase history, and mode of transportation, while emotional data includes self-reports and voice input.

[0704] Step 2:

[0705] The terminal processes the entered data, encrypts it, and then sends it to the server. Data transmission is performed periodically and in a way that protects privacy.

[0706] Step 3:

[0707] The server stores and analyzes behavioral and emotional data received from the terminal. Environmental impact is calculated using behavioral data, and emotional data is analyzed using an emotion engine to understand the user's current emotional state.

[0708] Step 4:

[0709] The server utilizes the results of the emotion engine to generate personalized suggestions that best suit the user's current emotional state. For example, if the user is feeling stressed, it will suggest simple and easy-to-implement eco-friendly activities.

[0710] Step 5:

[0711] The server sends the generated suggestions to the terminal, making them available to the user. The suggestions are displayed graphically through the user interface.

[0712] Step 6:

[0713] Users review suggested actions via their devices and take action as needed. They also set environmental goals and adjust their daily actions while monitoring their progress toward those goals on their devices.

[0714] Step 7:

[0715] The device tracks the achievement of environmental goals by comparing them with the user's behavioral records and updates the data in real time. It also provides feedback tailored to the user's emotional state.

[0716] Step 8:

[0717] The server anonymously records user success stories and effective improvement strategies in a community database, organizing and providing this information for other users to refer to. Users can then use this information to further improve their actions.

[0718] (Example 2)

[0719] 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".

[0720] In the modern era, promoting sustainable behavior and reducing environmental impact are crucial social issues. However, conventional methods have failed to adequately consider user emotions when proposing behavioral improvements, making it difficult to provide suggestions to individual users at the optimal time and in the appropriate form. Furthermore, raising environmental awareness throughout the community through information sharing among users has not been sufficiently achieved.

[0721] 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.

[0722] In this invention, the server includes means for collecting and analyzing behavioral data and emotional data; means for evaluating environmental impact based on the collected data and performing a comprehensive evaluation that takes emotional state into account; and means for generating personalized behavioral improvement suggestions according to the user's emotional data. This makes it possible to provide optimal behavioral improvement suggestions that take into account the emotional state of each individual user.

[0723] "Behavioral data" refers to records of specific activities in a user's daily life, including information such as means of transportation and consumption behavior.

[0724] "Emotional data" refers to information that indicates a user's mental state, and includes emotional indicators such as stress, happiness, and motivation.

[0725] "Environmental impact" refers to the degree of influence that human activities have on the natural environment, and includes carbon dioxide emissions and resource consumption.

[0726] An "emotion engine" refers to an algorithm and program that analyzes a user's emotional data and generates suggestions tailored to that emotional state.

[0727] "Personalized behavioral improvement suggestions" refer to specific instructions and action plans for improving the environment, optimized for a particular user, generated based on the behavioral and emotional data of individual users.

[0728] "Means of promoting information sharing" refer to functions and methods that facilitate the exchange of success stories and know-how among users and organizations, and that promote collaborative environmental awareness.

[0729] This system collects user behavioral and emotional data through server, terminal, and user interactions, evaluates environmental impact based on that data, and generates personalized recommendations.

[0730] Server Role

[0731] The server receives behavioral and emotional data transmitted from the terminal and processes this data using an analysis engine. It uses data analysis libraries such as Python and R to cleanse the data and calculate carbon dioxide emissions and resource consumption. It also utilizes natural language processing libraries to analyze emotional data. Based on this, the emotional engine works in conjunction with a generative AI model to generate specific behavioral improvement suggestions tailored to the user's emotional state. An example of a prompt might be, "Create eco-friendly activity suggestions based on the user's emotional state."

[0732] Terminal role

[0733] The device acts as the user interface, notifying the user of suggestions received from the server. These notifications are displayed as push notifications or in-app messages on devices such as smartphones and tablets. Users can input sentiment data through the interface, which is securely transmitted from the device to the server. Secure communication is ensured by using the HTTPS protocol for data transfer.

[0734] User roles

[0735] Users input behavioral and emotional data using a device during their daily lives. This includes transportation methods, consumption patterns, and emotional states. They also take specific actions based on the suggestions provided and input the results as feedback into the device. For example, they may be recommended to use eco-bags, utilize public transportation, or choose energy-saving foods. This user feedback is then reflected in future suggestions, resulting in more appropriate action plans being provided.

[0736] This invention aims to enable users to take sustainable actions without difficulty by utilizing emotional data, and to raise environmental awareness throughout the community.

[0737] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0738] Step 1:

[0739] Terminal role

[0740] The device collects behavioral and emotional data from the user. Users input their daily activities (e.g., modes of transportation used and products purchased) and emotional states (e.g., motivation level and stress level) through applications on the device. The input data is structured in JSON format.

[0741] Step 2:

[0742] Terminal role

[0743] The device sends collected behavioral and emotional data to the server. The data is securely transmitted to the server using the HTTPS protocol. This ensures the safe transfer of data, and the server receives the input data necessary for subsequent analysis.

[0744] Step 3:

[0745] Server Role

[0746] The server analyzes the data received from the terminal using an analysis engine. Using Python libraries such as Pandas and NumPy, behavioral data is preprocessed to calculate carbon dioxide emissions and resource consumption. Emotional data is analyzed using a natural language processing library to evaluate the user's mental state. The results of this analysis become the output for use in the next step.

[0747] Step 4:

[0748] Server Role

[0749] The server uses a generative AI model to generate behavioral improvement suggestions based on the user's emotional state. The specific prompt used is in the format of "Create eco-friendly activity suggestions based on the user's emotional state." The generated suggestions are personalized for each user and are passed on to the next step as input to encourage behavioral improvement.

[0750] Step 5:

[0751] Terminal role

[0752] The device notifies the user of behavioral improvement suggestions received from the server. These notifications are delivered via push notifications or in-app messages at the optimal time, taking into account the user's emotional state. Based on these notifications, the user can then take the recommended actions.

[0753] Step 6:

[0754] User roles

[0755] Users take specific actions based on suggestions provided by their devices. For example, they might bring their own reusable shopping bag or choose energy-efficient home appliances. Feedback on these actions is entered into the device and sent to the server, where it is reused as input data for generating suggestions in the future.

[0756] (Application Example 2)

[0757] 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".

[0758] Conventional environmental impact reduction systems have a problem in that they offer uniform behavioral improvement suggestions without considering user feelings, which reduces user motivation to follow the suggestions. Furthermore, the achievement of environmental goals and the promotion of information sharing are insufficient, making it difficult to expand sustainable activities throughout the community.

[0759] 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.

[0760] In this invention, the server includes a device for collecting and analyzing behavioral data, a device for generating suggestions for behavioral improvement that take into account the user's emotional state, and a device for facilitating information sharing with other users. This enables the suggestion of optimal eco-friendly activities based on the user's emotions and the sharing of best practices throughout the community.

[0761] "Behavioral data" refers to information about users' daily actions and activities, and is used to evaluate environmental impact and suggest improvements to behavior.

[0762] "Environmental impact" is an indicator that shows the impact and damage to the natural environment, and it evaluates the degree of ecological impact caused by actions and consumption activities.

[0763] "User emotional state" refers to the psychological and emotional state that a user is currently experiencing, and it influences their willingness to participate in eco-activities and their acceptance of proposals.

[0764] "Suggestions for behavioral improvement" are specific actions that users should take to enhance sustainability, and are personalized and generated to support users in achieving their environmental goals.

[0765] "Information sharing" refers to the act of mutually exchanging useful knowledge and success stories among users or organizations, with the aim of improving eco-friendly activities for individuals and communities as a whole.

[0766] The system for realizing this invention consists of three main components: a server, a terminal, and a user. The server analyzes behavioral and emotional data and, based on this, generates an assessment of environmental impact and suggestions for behavioral improvements optimized for the user. This process utilizes Google Cloud's Natural Language API and analytical models developed in Python. This allows the server to analyze the user's emotional state and provide more effective suggestions.

[0767] The device functions as an interface with the user, displaying suggestions sent from the server. Users input their emotional state via their smartphone, and this data is transmitted to the server in real time. The device also has the function of setting and managing the user's environmental goals. This allows users to constantly monitor their own behavior and gain incentives for improvement.

[0768] The user is the central figure in this system, and by inputting emotional and behavioral data from their device, they can receive suggestions that are best suited to them. For example, a user can receive suggestions for eco-friendly activities to do on the weekend. For instance, by sending a prompt such as, "Based on my emotional state today, what eco-friendly activity would be best?" to the system, they can receive suggestions for the most suitable eco-friendly activity.

[0769] This system will enable users to promote sustainable behaviors tailored to their individual emotional states and spread best practices for eco-friendly activities through information sharing across the community.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The device acquires the user's daily activities and emotional state. Users can manually input emotional and behavioral data using their smartphones, or automatically transmit collected data. This input includes text information as emotional data and GPS information as behavioral data. This data is sent to the server.

[0773] Step 2:

[0774] The server analyzes the emotional state based on the received data. This involves using Google Cloud's Natural Language API to perform text sentiment analysis, classifying the emotional data entered by the user into positive, negative, neutral, etc. As a result, an evaluation of the user's current emotional state is output.

[0775] Step 3:

[0776] The server uses the analyzed emotional state and behavioral data to generate suggestions for behavioral improvements optimized for the user. Using an algorithm developed in Python, it compares the user's emotional state with their behavioral history to suggest appropriate eco-friendly activities. The output consists of these eco-friendly activity suggestions, which are then sent to the terminal.

[0777] Step 4:

[0778] The device displays suggestions received from the server to the user. For example, if the user is highly motivated, it might display a notification recommending jogging. This suggestion is tailored to the user's emotional state and may include specific steps for implementation.

[0779] Step 5:

[0780] The user selects and performs an action based on the displayed suggestion. This action is tracked through the device, and the data is fed back to the server. For example, after jogging, the device records the activity and sense of accomplishment, and sends the data for the next suggestion.

[0781] Step 6:

[0782] The server receives feedback while simultaneously facilitating information sharing among users. Success stories and achievement data are shared, encouraging eco-friendly activities throughout the community. This output is stored in a database as best practices and made available to other users as needed.

[0783] 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.

[0784] 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.

[0785] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0786] 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.

[0787] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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."

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] Means for collecting and analyzing behavioral data,

[0807] A means of evaluating environmental impact based on collected data,

[0808] A means for generating personalized suggestions regarding improving user behavior,

[0809] Means for setting and managing environmental targets,

[0810] A means to facilitate information sharing with other users,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the personalized suggestions propose specific actions relating to reducing energy consumption or waste.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the means for facilitating the sharing of information enables the sharing of success stories among users and among companies.

[0816] "Example 1"

[0817] (Claim 1)

[0818] Means for collecting and analyzing behavioral information,

[0819] A means of assessing the environmental impact based on the collected information,

[0820] A means for generating personalized suggestions regarding improving user behavior,

[0821] Means for setting and managing environmental targets,

[0822] A means to facilitate information sharing among other users,

[0823] A means of transmitting and receiving behavioral information using communication means,

[0824] A means of collecting user feedback on the generated proposals,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the personalized proposals propose specific actions relating to reducing energy use or waste.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the means for facilitating the sharing of information enables the sharing of success stories among users and among organizations.

[0830] "Application Example 1"

[0831] (Claim 1)

[0832] Means for collecting and analyzing behavioral information,

[0833] A means of evaluating environmental impact based on collected information,

[0834] A means of generating personalized improvement suggestions,

[0835] Means for setting goals and managing progress,

[0836] A means to facilitate the sharing of information among other users,

[0837] A means of analyzing urban life data and providing sustainable lifestyle proposals,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the personalized suggestions include specific actions related to reducing energy use and waste, and include suggestions regarding the selection of means of transportation within a city.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the means for promoting the information sharing enables the sharing of success stories among users and among companies, and includes the provision of incentive information related to urban policies.

[0843] "Example 2 of combining an emotion engine"

[0844] (Claim 1)

[0845] Means for collecting and analyzing behavioral and emotional data,

[0846] A means of evaluating environmental impact based on collected data and conducting a comprehensive assessment that takes emotional states into account,

[0847] A means for generating personalized behavioral improvement suggestions based on user sentiment data,

[0848] Means for setting and managing environmental targets,

[0849] A means to facilitate information sharing with other users,

[0850] A means of notifying users of suggestions at the optimal time using an emotion engine,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the personalized suggestions propose specific actions related to reducing energy consumption and waste in accordance with the user's emotional state.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the means for promoting information sharing enables the sharing of success stories among users and organizations, thereby raising environmental awareness throughout the community.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] A device for collecting and analyzing behavioral data,

[0859] A device that evaluates environmental impact based on collected data,

[0860] A device that generates suggestions for behavioral improvement while taking into account the user's emotional state,

[0861] A device for setting and managing environmental targets,

[0862] A device that facilitates information sharing among users,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, wherein the proposed method suggests eco-friendly activities at the optimal timing according to the user's emotional state.

[0866] (Claim 3)

[0867] The system according to claim 1, wherein the device that facilitates information sharing enables the sharing of best practices among users and among organizations. [Explanation of symbols]

[0868] 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 analyzing behavioral data, A means of evaluating environmental impact based on collected data, A means for generating personalized suggestions regarding improving user behavior, Means for setting and managing environmental targets, A means to facilitate information sharing with other users, A system that includes this.

2. The system according to claim 1, wherein the personalized suggestions propose specific actions related to reducing energy consumption and waste.

3. The system according to claim 1, wherein the means for facilitating the sharing of information enables the sharing of success stories among users and among companies.

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