system

Abstract

We provide the system. [Solution] Data collection means for collecting environmental data, Data processing means for cleansing and analyzing collected data, A proposal generation method that provides users with options to minimize environmental impact, A notification method for notifying the user terminal of the proposal, A means of collecting user feedback and updating the model, 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】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Among the global environmental problems such as global warming, deforestation, and marine litter problems that are intensifying, it is difficult for individual consumers to choose actions that reduce the environmental burden in daily life without sacrificing convenience. Therefore, there is a demand for the provision of a system that enables consumers to easily make environmentally friendly choices. 【Means for Solving the Problems】 【0005】 The present invention solves the above problems by providing an environmental conservation system that utilizes generative AI. This system includes data collection means for collecting environmental data, data processing means for cleansing and analyzing the collected data, proposal generation means for providing users with options that minimize environmental impact, notification means for notifying the user terminal of the proposal, and learning means for collecting user feedback and updating the model. As a result, users will be able to choose environmentally conscious actions in their daily lives without even realizing it. 【0006】 "Environmental data" is a general term for various types of data related to the environment, such as weather information, energy consumption information, and traffic information. 【0007】 "Data collection means" refers to devices and methods equipped with a mechanism for acquiring environmental data from diverse data sources. 【0008】 "Data processing means" refers to devices or methods that have functions for data cleansing and format conversion to prepare collected raw data for analysis. 【0009】 "Proposal generation means" refers to a device or method that has a mechanism for creating the most suitable environmentally conscious action options for the user based on analyzed data. 【0010】 "Notification means" refers to a device or method equipped with means for communicating generated suggestions to the user. 【0011】 "Learning method" refers to a device or method that incorporates a mechanism for continuously learning and improving the system's generating AI based on user behavioral feedback. [Brief explanation of the drawing] 【0012】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0013】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings. 【0014】 First, the language used in the following description will be explained. 【0015】 In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0016】 In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0018】 In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0019】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0023】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0024】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0025】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0026】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0027】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0030】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0031】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0032】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0033】 This invention is a system that proposes environmentally friendly options to users using generational AI, and operates in cooperation with a server, terminal, and user. 【0034】 In implementing this system, the server first acquires environmental data from various data sources. Specifically, it collects weather information and energy consumption data from APIs and systems of various specialized organizations. This allows the server to maintain the necessary data in real time. 【0035】 Next, the server cleanses the acquired data and converts it into a format suitable for analysis. This involves processes such as imputing missing values ​​and removing outliers. This prepares high-quality data for the generative AI model to function effectively. 【0036】 Next, the server uses a generative AI model to perform analysis and generate the optimal choices for the user. The generative AI uses a reinforcement learning algorithm to construct suggestions based on each user's past actions and current environmental conditions. 【0037】 Subsequently, this suggestion is sent to the device and presented to the user via push notifications, etc. The user reviews the multiple options displayed on the device screen and decides which option to adopt in their daily life. For example, in the case of suggestions for commuting transportation, the server notifies the user of transportation options and estimated arrival times based on traffic information and weather conditions collected by the server. In this way, users can take actions that ensure convenience while minimizing environmental impact. 【0038】 Finally, the user sends feedback about their chosen actions and their results to the server via their device. The server uses this data to train its AI model, improving the accuracy of future suggestions. Furthermore, by considering changes in the user's behavior patterns and preferences, the entire system can be continuously improved. 【0039】 This allows the system to efficiently and effectively support users in making environmentally friendly choices naturally. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The server connects to various data sources to collect necessary information in real time, such as weather data, energy consumption data, and traffic information. For example, it retrieves weather conditions via an API and extracts the latest usage data from an energy consumption database. 【0043】 Step 2: 【0044】 The server cleanses the collected raw data and prepares it for analysis. Specifically, it fills in missing data and removes obviously abnormal values. This process is crucial for ensuring the reliability and consistency of the data. 【0045】 Step 3: 【0046】 The server applies a generative AI model based on the cleansed data to generate optimal action options for the user. During this process, it uses reinforcement learning algorithms to analyze past user behavior data and formulate proposals to minimize environmental impact. 【0047】 Step 4: 【0048】 The server sends the generated suggestions to the device. The device communicates these suggestions to the user using push notifications or in-app notifications. The user can then see specific action options on the device screen. 【0049】 Step 5: 【0050】 Users review the suggestions they receive from their devices and decide which options to adopt in their daily activities. For example, they might choose whether or not to use the suggested mode of transportation for their commute. 【0051】 Step 6: 【0052】 Users send feedback about their chosen actions and their results to the server via their device. This records the user's actual behavior. 【0053】 Step 7: 【0054】 The server incorporates user feedback into its AI model, using it as training data for future suggestions. This allows the system to improve the accuracy of its suggestions over time, enabling it to present users with more appropriate options. 【0055】 (Example 1) 【0056】 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." 【0057】 In recent years, with growing awareness of environmental protection, individuals are expected to make choices that reduce their environmental impact in their daily lives. However, for users to make environmentally conscious choices, they need to collect and analyze a large amount of information, which is a significant burden. Furthermore, because users have limited opportunities to receive personalized recommendations in their decision-making, there are challenges in promoting concrete actions to reduce environmental impact. 【0058】 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. 【0059】 In this invention, the server includes information acquisition means for acquiring environmental information, information processing means for cleansing and processing the acquired information, and option generation means for proposing options to the user that reduce environmental impact. As a result, users can easily obtain appropriate options for environmentally conscious daily activities without having to perform complex information gathering and analysis. 【0060】 "Environmental information" refers to data such as weather conditions and energy usage in the natural world, and is information directly related to resource consumption and environmental impact. 【0061】 "Information acquisition means" refers to devices or methods that have the function of collecting necessary information from various data sources. 【0062】 "Information processing means" refers to a method that has the function of analyzing acquired information and processing data into an optimal format. 【0063】 A "choice generation means" is a method that has the function of creating the option that best suits specific conditions using available information and algorithms. 【0064】 "Communication means" refers to technologies and devices used to exchange information between a server and a user's terminal. 【0065】 "Update methods" refer to processes and methods for training artificial intelligence models based on feedback data to improve the accuracy and suitability of suggestions. 【0066】 One specific embodiment of the present invention is a system in which a server, a terminal, and a user cooperate to provide environmentally friendly options. 【0067】 First, the server receives environmental information from various data sources through information acquisition means. For example, APIs for weather information services and energy consumption monitoring systems are used. Subsequently, the server performs data cleansing using information processing means. This involves using data science languages ​​such as Python and R, and their libraries, to perform processes such as imputing missing values ​​and removing outliers. 【0068】 The server then uses a generative AI model to generate the best options for the user. Reinforcement learning algorithms are used in this process. Specifically, past behavioral data and current environmental conditions are input to the model as prompts. For example, "Consider the user's past commute choices, current weather information, and energy consumption data, and suggest the best commute option." 【0069】 Next, the device receives suggestions from the server via communication and presents them to the user using push notifications. Based on this, the user chooses specific actions to reduce their environmental impact. A concrete example in daily life might be that, based on weather information provided by the server, the user is recommended to commute by bicycle. 【0070】 Finally, the user sends feedback from their device to the server regarding the choices they made and the results. This feedback is used to retrain the AI ​​model through an update mechanism, which improves the accuracy of the suggestions and provides choices that better suit the user's preferences in the future. 【0071】 Thus, the present invention is a system that facilitates environmental considerations and supports users in naturally taking actions that reduce their environmental impact in their daily lives. 【0072】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0073】 Step 1: 【0074】 The server acquires environmental information. It uses weather information and energy consumption data obtained from specialized organizations' APIs and databases as input. Specifically, the server calls the API every hour to collect the latest data. The output is raw data collected in its unprocessed state. 【0075】 Step 2: 【0076】 The server cleanses the collected environmental information. It uses the raw data obtained in step 1 as input. The server imputes missing values ​​and removes statistical outliers. Specifically, it uses the Python Pandas library to impute missing values ​​from the dataframe with the mean. The output is a consistent dataset suitable for analysis. 【0077】 Step 3: 【0078】 The server generates optimal choices using a generative AI model. It uses consistent data processed in step 2 and the user's past behavioral history as input. The server inputs this information into the model as prompts and utilizes a reinforcement learning algorithm. Specifically, it integrates past data with current environmental data and generates a prompt for the AI ​​model: "Considering the user's past commuting choices, current weather information, and energy consumption data, please suggest the optimal commuting method." The output is an optimized action suggestion for each user. 【0079】 Step 4: 【0080】 The device receives suggestions from the server and notifies the user. The input is the suggestions generated in step 3. The device uses push notifications to present the user with options. Specifically, it displays a notification on the user's smartphone such as, "Cycling is recommended for your commute today." The output is a visual notification to the user. 【0081】 Step 5: 【0082】 The user receives a notification from their device and chooses their action. The input is the information presented by the device. Specifically, the user chooses to commute by bicycle based on the notification. The output is the selected action itself. 【0083】 Step 6: 【0084】 Users send feedback about the results of their actions to the server via their device. As input, they enter their actual experience and evaluation into a form. Specifically, users evaluate their cycling commute experience within the app in the format of "satisfaction with cycling commute." The output is stored on the server as feedback data. 【0085】 Step 7: 【0086】 The server collects feedback data and retrains the AI ​​model. The feedback data obtained in step 6 is used as input. Based on this, the server updates the algorithm parameters and improves the accuracy of the suggestions. Specifically, the feedback data is fed into a reinforcement learning algorithm, and a new model is trained during the server's nighttime downtime. The output is the newly tuned AI model. 【0087】 (Application Example 1) 【0088】 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." 【0089】 As environmental problems worsen, there is a growing need to promote actions that reduce the impact on the natural environment in daily life. However, it is difficult for individuals to judge and act on appropriate choices in their daily activities, and an effective support system is needed for this purpose. 【0090】 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. 【0091】 In this invention, the server includes information gathering means for collecting environmental information, information processing means for cleaning and analyzing the collected information, and choice generation means for providing options that reduce the impact on the natural environment in daily activities. This makes it possible for users to easily choose environmentally conscious actions. 【0092】 "Information gathering means for collecting environmental information" refers to methods and devices for acquiring environmental data such as weather conditions and energy consumption. 【0093】 "Information processing means for cleansing and analyzing collected information" refers to methods or devices that perform data acquisition, such as imputing missing values ​​and removing outliers, and then converting the data into an analyzable format. 【0094】 "Means for generating options that reduce the impact on the natural environment in daily life activities" refers to methods or devices that, based on collected and processed data, create options for users to choose environmentally conscious actions in their daily lives. 【0095】 "Notification means for mobile devices" refers to methods or devices for conveying generated environmental consideration options to communication devices such as smartphones. 【0096】 "Methods for collecting responses and updating predictive models" refer to methods or devices that collect user-selected actions and their outcomes, and use that information to improve predictive models in order to enhance the accuracy of suggestions. 【0097】 The system in this invention helps reduce the environmental impact in daily life by providing users with environmentally friendly options. The system mainly consists of three elements: a server, a terminal, and a user. 【0098】 The server first collects environmental information, such as weather conditions and energy consumption, via APIs and other means. This uses data collection methods that run on a cloud platform. Next, the collected data is cleansed using Python and formatted into an appropriate format for analysis. Specifically, this includes imputing missing data and removing inappropriate data. 【0099】 Subsequently, a generative AI model is used to analyze the collected and cleansed data, and reinforcement learning techniques are used to generate the optimal choices for each user. Using libraries such as TENSORFLOW® enables efficient data processing and AI model execution. 【0100】 The generated options are sent to the device as push notifications or in-app messages. The device is a mobile communication device such as a smartphone or tablet, and this notification allows the user to receive suggestions in real time. 【0101】 Users can incorporate the options they receive into their daily lives, for example, by choosing to commute by bicycle on certain days, thus taking environmentally conscious actions. These choices and responses regarding their results are sent back to the server via the terminal, and the server uses this information to train an AI model, building a system that improves the accuracy of future suggestions. 【0102】 As a concrete example, by inputting a prompt message into the AI ​​model—"Based on today's weather and traffic information, suggest the best commuting method for the user"—the system will derive a commuting method that suits the user. In this way, the system helps users make choices that are mindful of nature and the environment. 【0103】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0104】 Step 1: 【0105】 The server retrieves weather information and energy consumption data from APIs of various specialized organizations. Inputs include API keys and query parameters, while output is environmental information in raw data format. This step involves the specific operation of collecting data using HTTP requests. 【0106】 Step 2: 【0107】 The server cleanses the acquired data and formats it into a format suitable for analysis. The input is raw data, and the output is formatted data. Data missing values ​​are imputed and outliers are removed; specifically, data processing is performed using Python scripts. 【0108】 Step 3: 【0109】 The server inputs the formatted data into an AI model for analysis. Specifically, it uses a reinforcement learning algorithm to generate the optimal choices for the user. The input is formatted data, and the output is data representing environmentally conscious choices. The TensorFlow library is used for model execution and data processing. 【0110】 Step 4: 【0111】 The server notifies the device of the generated choices. The input for this step is the data of the generated choices, and the output is the information displayed on the device. Specifically, this involves sending data using a push notification API. 【0112】 Step 5: 【0113】 The user reviews the options provided through the terminal and makes a selection. The input is the presented options, and the output is the user's selection information. This step includes user interface operations as concrete actions. 【0114】 Step 6: 【0115】 The terminal sends user selection information to the server. The input is the user's selection information, and the output is response data stored on the server. Specifically, data transmission is performed using a communication protocol. 【0116】 Step 7: 【0117】 The server trains a generative AI model based on collected user selection information, improving the accuracy of future suggestions. The input is user selection information, and the output is the improved AI model. Specific operations include model training. 【0118】 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. 【0119】 This invention is a system that utilizes generative AI and an emotion engine to provide users with environmentally conscious choices, and can make suggestions that take into account the user's emotional state. 【0120】 In implementing this system, the server first collects environmental data. Specifically, it obtains weather information, energy consumption information, traffic information, etc., from various data sources and updates them in real time. This makes it possible to process data based on the latest environmental conditions. 【0121】 Next, the server cleanses the collected data and converts it into a format suitable for analysis. This process includes imputing missing values ​​and removing outliers to ensure data reliability. Then, a generative AI model is used to generate appropriate behavioral options for the environment. Reinforcement learning algorithms analyze the user's past behavioral data to provide optimal suggestions. 【0122】 Next, the device analyzes data such as voice and facial expressions using an emotion engine to recognize the user's emotions. Based on this data, the emotion engine has the ability to determine the user's emotional state in real time. For example, if it determines that the user is in a happy mood, suggestions will be made that align with that emotion. 【0123】 The suggestion generation mechanism takes the output of the emotion engine and generates options optimized for the user's emotional state. The device then communicates these generated suggestions to the user as notifications. The user can view the specific suggestions on the device screen and choose which action to take. For example, when feeling happy, the user might be suggested to take a short trip to a nature observation spot within walking distance. 【0124】 Subsequently, the user's actions and feedback are sent to the server via the device. The server uses this feedback data to continuously train the AI ​​model, improving the quality of future suggestions. This continuous learning enables flexible suggestions that respond to changes in the user's emotions and behavior. 【0125】 This system aims to reduce environmental impact while providing users with optimal choices that take their emotional state into consideration, thereby helping them live a more comfortable and environmentally friendly life. 【0126】 The following describes the processing flow. 【0127】 Step 1: 【0128】 The server aggregates environmental information such as weather data, energy consumption data, and traffic data from various data sources. This includes a process of obtaining real-time data using API connections to reflect the latest conditions. 【0129】 Step 2: 【0130】 The server cleanses the collected data, imputing outliers and missing values. It also uses deep learning algorithms and other techniques to preprocess the data and prepare it for analysis. 【0131】 Step 3: 【0132】 The device uses the user's voice input and images captured by the camera to analyze the user's emotions using an emotion engine. This analysis is then used to understand the user's mood and state in real time. 【0133】 Step 4: 【0134】 The server uses a generative AI model to generate optimal action options for the user, based on cleansed data and the results of the emotion engine's analysis. In this process, reinforcement learning algorithms are used to consider past behavioral data and the current environmental conditions. 【0135】 Step 5: 【0136】 The server sends the generated suggestions to the terminal. The terminal provides visual and audio feedback to the user, clearly explaining the advantages and reasons for each option. 【0137】 Step 6: 【0138】 The user reviews the options presented on the device and selects an action that suits their current situation. Based on the selected action, they then carry out actual travel or consumption activities. 【0139】 Step 7: 【0140】 Users send feedback about their chosen actions to the server via their device. This includes their impressions of the experience and their evaluation of the choices they made. 【0141】 Step 8: 【0142】 The server uses the feedback data to retrain the generating AI model, updating it to improve the accuracy of future suggestions. This feedback loop improves the overall accuracy and usability of the system. 【0143】 (Example 2) 【0144】 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 will be referred to as the "terminal." 【0145】 Reducing the environmental impact of individual lifestyles is crucial as a measure against environmental problems. However, conventional systems have been unable to provide flexible suggestions that take into account the user's emotional state, making it difficult to encourage sustainable behavioral change. Furthermore, there is a need to efficiently analyze the vast amount of environmental data and user information collected and provide individually optimized suggestions. 【0146】 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. 【0147】 In this invention, the server includes data collection means for collecting environmental data, data processing means for cleansing and analyzing the collected data, and emotion recognition means for recognizing the user's emotional state. This makes it possible to provide the user with choices that minimize environmental impact and are adapted to their emotional state. 【0148】 A "data collection means" is a mechanism for acquiring environmental data, enabling the real-time collection of weather information, energy consumption information, and other data. 【0149】 A "data processing means" is a mechanism for properly cleansing collected data and preparing it for analysis. It performs tasks such as imputing missing values ​​and removing outliers to ensure data reliability. 【0150】 An "emotion recognition system" is a mechanism that analyzes the user's voice and facial expression data to determine the user's emotional state in real time. 【0151】 A "proposal generation mechanism" is a system that uses a generation AI model to provide users with optimal action options that reduce environmental impact and are adapted to their emotional state. 【0152】 A "notification means" is a mechanism for communicating the options generated by the suggestion generation means to the user, and displays the notification on the user's terminal. 【0153】 A "learning tool" is a mechanism for collecting user feedback and continuously updating the learning model based on that feedback, thereby improving the accuracy of individually optimized suggestions. 【0154】 The embodiments for carrying out the present invention are shown below. 【0155】 This system combines a generative AI model with an emotion recognition engine to provide users with environmentally conscious choices. The server acquires diverse environmental data, such as weather information, energy consumption information, and traffic information, through data collection methods. In this process, it uses the data provider's API to record the information in a database in real time. 【0156】 Next, the server uses data processing tools to cleanse the collected environmental data and convert it into a parseable format. The Python programming language and its related libraries (e.g., Pandas, NumPy) are used to format the data and impute missing values. 【0157】 Subsequently, a generative AI model is used to prepare environmentally conscious action options. The server uses the TensorFlow library to implement reinforcement learning algorithms and predicts the optimal action based on past user behavior data. Examples of prompts used in this process include "Suggest activities that can be done at home while minimizing energy consumption when the user is emotionally calm." 【0158】 Meanwhile, the device evaluates the user's emotions using emotion recognition mechanisms. It analyzes emotions in real time from voice and facial expressions using input data from the device's built-in microphone and camera. This analysis utilizes image processing libraries such as OpenCV, as well as voice analysis tools. 【0159】 The suggestion generation method uses a generative AI model and emotion recognition results to create optimized options that are appropriate to the user's emotions. For example, if the user is in a happy mood, it suggests visiting a nearby park and notifies the user on their device. The user can then confirm and select this suggestion through the notification displayed on the screen. 【0160】 The system collects feedback from the device regarding the user's actions and sends it to the server. This feedback is used to update the AI ​​model through a learning mechanism, resulting in more personalized suggestions for the next time. 【0161】 This process in the system allows users to receive emotionally sensitive support while reducing their environmental impact, which is expected to improve their quality of life. 【0162】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0163】 Step 1: 【0164】 The server collects environmental data. It obtains data from APIs of weather, energy consumption, and traffic information providers as its information sources. API request parameters are used as input. The output is stored as raw data in the server's database. Specifically, it periodically retrieves data from each provider using HTTP requests and stores it in the database in JSON or XML format. 【0165】 Step 2: 【0166】 The server cleanses the collected data. It receives raw data as input and processes missing values ​​and outliers. For data processing, it manipulates dataframes using the Pandas library, imputes missing values ​​using statistical methods, and removes outliers. As output, the clean data is passed on to the next analysis step. Specifically, it applies methods such as mean imputation and outlier removal. 【0167】 Step 3: 【0168】 The server generates action options using a generative AI model based on clean data. It receives past user behavior history and current environmental data as input, which is then analyzed using a reinforcement learning algorithm. The output is an optimized list of action options. Specifically, TensorFlow is used, with a neural network evaluating actions, and the generated options are then evaluated and selected by the AI ​​model. 【0169】 Step 4: 【0170】 The device recognizes the user's emotional state. It receives audio data and camera footage in real time as input. For data processing, it uses video analysis and audio emotion recognition tools based on OpenCV to determine the emotional state. The output is stored on the device as user emotional state information. Specific operations include analyzing emotional tone from audio and identifying emotions from facial expressions. 【0171】 Step 5: 【0172】 Based on the generated action options and the user's sentiment information, the device generates suggestions and notifies the user. It receives options and sentiment recognition results from an AI model as input and optimizes the suggestions. The output is a notification message to the user, which includes specific action suggestions. Specific actions may include displaying suggestions on the user screen using a notification-centric UI design. 【0173】 Step 6: 【0174】 The user performs the suggested action and inputs feedback into the device. This input includes the user's actions and impressions. This input data is sent to the server and used to train the AI ​​model. The output is the feedback data necessary for training. Specifically, forms or simple questionnaires are provided on the device, through which the user provides feedback. 【0175】 Step 7: 【0176】 The server updates the generated AI model using the collected feedback data to improve the accuracy of the proposals. It continues reinforcement learning using the feedback data and the current model parameters as input. The output is the updated AI model, which will be used for the next proposal. Specifically, the model parameters are adjusted and retrained to improve accuracy. 【0177】 (Application Example 2) 【0178】 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 device 14 will be referred to as the "terminal." 【0179】 In modern urban environments, residents seek choices that allow them to live comfortably while reducing their environmental impact. However, general information systems do not consider the user's emotional state in their individual suggestions, making it difficult to propose environmentally friendly behavioral choices optimized for the individual. This invention realizes a system that enables residents to raise their environmental awareness in their daily choices by utilizing real-time emotional data of users and providing suggestions that integrate with environmental data. 【0180】 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. 【0181】 In this invention, the server includes information gathering means for comprehensively acquiring environmental data, information processing means for purifying and analyzing the acquired information, and suggestion generation means for generating suggestions based on the user's emotional state using an emotion analysis function. This allows for the provision of optimal action suggestions in real time by combining environmental data and the user's emotional state, making it easy for residents to make environmentally conscious choices in their daily lives. 【0182】 "Information gathering means" refers to a system for comprehensively acquiring environmental data related to climate and energy consumption. 【0183】 "Information processing means" refers to a function that cleanses and analyzes acquired information to enable accurate suggestions to the user. 【0184】 The "proposal generation method" is a system that uses emotion analysis functionality to generate optimal action suggestions based on the user's emotional state. 【0185】 "Communication means" refers to technology for transmitting generated proposals to user devices and providing real-time notifications. 【0186】 "Update methods" refer to the process of collecting user reactions and feedback and adaptively updating the learning model. 【0187】 A "machine learning algorithm" is a type of artificial intelligence technique used to generate appropriate suggestions for users. 【0188】 This invention is an information system for raising environmental awareness among residents in smart cities, and is implemented based on the respective roles of the server, terminal, and user. 【0189】 The server is equipped with an information gathering mechanism for comprehensively acquiring environmental data, and periodically collects climate-related and energy consumption-related information from external data sources. The collected data is purified by an information processing mechanism, which fills in missing data and removes outliers. This enables highly accurate data analysis in real time. 【0190】 Furthermore, the server uses a generative AI model to combine user behavior data and environmental data to create environmentally conscious behavioral suggestions. The suggestion generation method utilizes sentiment analysis functionality to reflect the user's emotional state and generate optimal suggestions. In this process, reinforcement learning algorithms are used to improve the model so that it can provide suggestions that are more suitable for the user. 【0191】 The device uses its camera and microphone to analyze the user's emotions in real time. It performs on-device emotion recognition using technologies such as the Emotion API and TensorFlow Lite. The analyzed emotion data is combined with environment suggestions received from the server to provide content tailored to the user's psychological state. 【0192】 Users receive notifications from their devices and choose actions based on the suggested actions. User feedback is sent to the server via the device, which continuously improves the accuracy of the suggestions. By providing personalized suggestions, residents can naturally choose actions in their daily lives that raise environmental awareness. 【0193】 For example, on a sunny day, a suggestion might be presented such as, "Today is a perfect day for going outside, so we recommend a walk to a nature observation spot." Another example of a prompt might be, "Based on today's weather and your mood, what are some recommended places to visit while being mindful of the environment?" Through such specific suggestions, residents can consciously choose environmentally friendly actions. 【0194】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0195】 Step 1: 【0196】 The server periodically collects environmental data from external data sources such as climate and energy consumption. Input is raw data from multiple APIs, and output is an integrated environmental dataset. Once data is acquired, it is stored in a database on the server. 【0197】 Step 2: 【0198】 The server cleanses and analyzes the collected environmental data. The input is the raw data obtained in step 1, and the output is a clean dataset. Specifically, it uses Pandas to fill in missing values, remove outliers, and convert the data into an analyzable format. 【0199】 Step 3: 【0200】 The server generates environmentally conscious suggestions using a generative AI model. The input consists of a clean dataset and data on the user's past behavior, while the output is specific action suggestions. The generative AI model uses a reinforcement learning algorithm to generate suggestions that lead to the user's optimal action. 【0201】 Step 4: 【0202】 The device uses its camera and microphone to acquire and analyze user emotion data. Input is real-time audio and image data, and output is the user's emotional state. Emotion analysis is performed on-device using the Emotion API and TensorFlow Lite. 【0203】 Step 5: 【0204】 The server optimizes suggestions based on the user's emotional state. The input is the suggestions generated in step 3 and the emotional state obtained in step 4, and the output is an action suggestion optimized for the emotional state. The suggestion generation means considers the emotional analysis data and customizes the suggestions. 【0205】 Step 6: 【0206】 The device communicates the final action suggestion to the user as a notification. The input is an optimized suggestion, and the output is a notification presented to the user visually or audibly. Information is conveyed to the user through the device screen or audio output. 【0207】 Step 7: 【0208】 The user receives suggestions and chooses an action. The input is a notification from the device, and the output is the user's action choice. The user selects what they believe to be the best option from the presented choices and puts it into action. 【0209】 Step 8: 【0210】 The device acquires user feedback and sends it to the server. Input is user feedback on their actions and satisfaction levels, while output is training data sent to the server. Feedback is collected through an in-app interface. 【0211】 Step 9: 【0212】 The server updates the AI ​​model using user feedback. The input is the feedback data obtained in step 8, and the output is the updated AI model. The server continuously updates the model based on reinforcement learning to improve the accuracy of future suggestions. 【0213】 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. 【0214】 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. 【0215】 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. 【0216】 [Second Embodiment] 【0217】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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). 【0223】 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. 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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". 【0229】 This invention is a system that proposes environmentally friendly options to users using generational AI, and operates in cooperation with a server, terminal, and user. 【0230】 In implementing this system, the server first acquires environmental data from various data sources. Specifically, it collects weather information and energy consumption data from APIs and systems of various specialized organizations. This allows the server to maintain the necessary data in real time. 【0231】 Next, the server cleanses the acquired data and converts it into a format suitable for analysis. This involves processes such as imputing missing values ​​and removing outliers. This prepares high-quality data for the generative AI model to function effectively. 【0232】 Next, the server uses a generative AI model to perform analysis and generate the optimal choices for the user. The generative AI uses a reinforcement learning algorithm to construct suggestions based on each user's past actions and current environmental conditions. 【0233】 Subsequently, this suggestion is sent to the device and presented to the user via push notifications, etc. The user reviews the multiple options displayed on the device screen and decides which option to adopt in their daily life. For example, in the case of suggestions for commuting transportation, the server notifies the user of transportation options and estimated arrival times based on traffic information and weather conditions collected by the server. In this way, users can take actions that ensure convenience while minimizing environmental impact. 【0234】 Finally, the user sends feedback about their chosen actions and their results to the server via their device. The server uses this data to train its AI model, improving the accuracy of future suggestions. Furthermore, by considering changes in the user's behavior patterns and preferences, the entire system can be continuously improved. 【0235】 This allows the system to efficiently and effectively support users in making environmentally friendly choices naturally. 【0236】 The following describes the processing flow. 【0237】 Step 1: 【0238】 The server connects to various data sources to collect necessary information in real time, such as weather data, energy consumption data, and traffic information. For example, it retrieves weather conditions via an API and extracts the latest usage data from an energy consumption database. 【0239】 Step 2: 【0240】 The server cleanses the collected raw data and prepares it for analysis. Specifically, it fills in missing data and removes obviously abnormal values. This process is crucial for ensuring the reliability and consistency of the data. 【0241】 Step 3: 【0242】 The server applies a generative AI model based on the cleansed data to generate optimal action options for the user. During this process, it uses reinforcement learning algorithms to analyze past user behavior data and formulate proposals to minimize environmental impact. 【0243】 Step 4: 【0244】 The server sends the generated suggestions to the device. The device communicates these suggestions to the user using push notifications or in-app notifications. The user can then see specific action options on the device screen. 【0245】 Step 5: 【0246】 Users review the suggestions they receive from their devices and decide which options to adopt in their daily activities. For example, they might choose whether or not to use the suggested mode of transportation for their commute. 【0247】 Step 6: 【0248】 Users send feedback about their chosen actions and their results to the server via their device. This records the user's actual behavior. 【0249】 Step 7: 【0250】 The server incorporates user feedback into its AI model, using it as training data for future suggestions. This allows the system to improve the accuracy of its suggestions over time, enabling it to present users with more appropriate options. 【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 recent years, with growing awareness of environmental protection, individuals are expected to make choices that reduce their environmental impact in their daily lives. However, for users to make environmentally conscious choices, they need to collect and analyze a large amount of information, which is a significant burden. Furthermore, because users have limited opportunities to receive personalized recommendations in their decision-making, there are challenges in promoting concrete actions to reduce environmental impact. 【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 information acquisition means for acquiring environmental information, information processing means for cleansing and processing the acquired information, and option generation means for proposing options to the user that reduce environmental impact. As a result, users can easily obtain appropriate options for environmentally conscious daily activities without having to perform complex information gathering and analysis. 【0256】 "Environmental information" refers to data such as weather conditions and energy usage in the natural world, and is information directly related to resource consumption and environmental impact. 【0257】 "Information acquisition means" refers to devices or methods that have the function of collecting necessary information from various data sources. 【0258】 "Information processing means" refers to a method that has the function of analyzing acquired information and processing data into an optimal format. 【0259】 A "choice generation means" is a method that has the function of creating the option that best suits specific conditions using available information and algorithms. 【0260】 "Communication means" refers to technologies and devices used to exchange information between a server and a user's terminal. 【0261】 "Update methods" refer to processes and methods for training artificial intelligence models based on feedback data to improve the accuracy and suitability of suggestions. 【0262】 One specific embodiment of the present invention is a system in which a server, a terminal, and a user cooperate to provide environmentally friendly options. 【0263】 First, the server receives environmental information from various data sources through information acquisition means. For example, APIs for weather information services and energy consumption monitoring systems are used. Subsequently, the server performs data cleansing using information processing means. This involves using data science languages ​​such as Python and R, and their libraries, to perform processes such as imputing missing values ​​and removing outliers. 【0264】 The server then uses a generative AI model to generate the best options for the user. Reinforcement learning algorithms are used in this process. Specifically, past behavioral data and current environmental conditions are input to the model as prompts. For example, "Consider the user's past commute choices, current weather information, and energy consumption data, and suggest the best commute option." 【0265】 Next, the device receives suggestions from the server via communication and presents them to the user using push notifications. Based on this, the user chooses specific actions to reduce their environmental impact. A concrete example in daily life might be that, based on weather information provided by the server, the user is recommended to commute by bicycle. 【0266】 Finally, the user sends feedback from their device to the server regarding the choices they made and the results. This feedback is used to retrain the AI ​​model through an update mechanism, which improves the accuracy of the suggestions and provides choices that better suit the user's preferences in the future. 【0267】 Thus, the present invention is a system that facilitates environmental considerations and supports users in naturally taking actions that reduce their environmental impact in their daily lives. 【0268】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0269】 Step 1: 【0270】 The server acquires environmental information. It uses weather information and energy consumption data obtained from specialized organizations' APIs and databases as input. Specifically, the server calls the API every hour to collect the latest data. The output is raw data collected in its unprocessed state. 【0271】 Step 2: 【0272】 The server cleanses the collected environmental information. It uses the raw data obtained in step 1 as input. The server imputes missing values ​​and removes statistical outliers. Specifically, it uses the Python Pandas library to impute missing values ​​from the dataframe with the mean. The output is a consistent dataset suitable for analysis. 【0273】 Step 3: 【0274】 The server generates optimal choices using a generative AI model. It uses consistent data processed in step 2 and the user's past behavioral history as input. The server inputs this information into the model as prompts and utilizes a reinforcement learning algorithm. Specifically, it integrates past data with current environmental data and generates a prompt for the AI ​​model: "Considering the user's past commuting choices, current weather information, and energy consumption data, please suggest the optimal commuting method." The output is an optimized action suggestion for each user. 【0275】 Step 4: 【0276】 The device receives suggestions from the server and notifies the user. The input is the suggestions generated in step 3. The device uses push notifications to present the user with options. Specifically, it displays a notification on the user's smartphone such as, "Cycling is recommended for your commute today." The output is a visual notification to the user. 【0277】 Step 5: 【0278】 The user receives a notification from their device and chooses their action. The input is the information presented by the device. Specifically, the user chooses to commute by bicycle based on the notification. The output is the selected action itself. 【0279】 Step 6: 【0280】 Users send feedback about the results of their actions to the server via their device. As input, they enter their actual experience and evaluation into a form. Specifically, users evaluate their cycling commute experience within the app in the format of "satisfaction with cycling commute." The output is stored on the server as feedback data. 【0281】 Step 7: 【0282】 The server collects feedback data and retrains the AI model. As input, it uses the feedback data obtained in step 6. Based on this, the server updates the parameters of the algorithm to improve the accuracy of the proposal. As a specific operation, it feeds the feedback data into a reinforcement learning algorithm and trains a new model during the server downtime at night. The output is the newly adjusted AI model. 【0283】 (Application Example 1) 【0284】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0285】 With the deepening of environmental problems, it is required to promote actions that reduce the impact on the natural environment in daily life. However, it is difficult for individuals to judge and execute appropriate options in their daily activities, so an effective support system is needed. 【0286】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0287】 In this invention, the server includes an information collection means for collecting environmental information, an information processing means for cleansing and analyzing the collected information, and an option generation means for providing options to reduce the impact on the natural environment in daily activities. Thereby, it becomes possible for the user to easily select actions that consider the environment. 【0288】 The "information collection means for collecting environmental information" is a method or device for acquiring data related to the environment such as weather conditions and energy consumption status. 【0289】 The "information processing means for cleansing and analyzing the collected information" is a method or device for complementing missing values and removing outliers of the acquired environmental data, and further converting it into an analyzable format. 【0290】 "Means for generating options that reduce the impact on the natural environment in daily life activities" refers to methods or devices that, based on collected and processed data, create options for users to choose environmentally conscious actions in their daily lives. 【0291】 "Notification means for mobile devices" refers to methods or devices for conveying generated environmental consideration options to communication devices such as smartphones. 【0292】 "Methods for collecting responses and updating predictive models" refer to methods or devices that collect user-selected actions and their outcomes, and use that information to improve predictive models in order to enhance the accuracy of suggestions. 【0293】 The system in this invention helps reduce the environmental impact in daily life by providing users with environmentally friendly options. The system mainly consists of three elements: a server, a terminal, and a user. 【0294】 The server first collects environmental information, such as weather conditions and energy consumption, via APIs and other means. This uses data collection methods that run on a cloud platform. Next, the collected data is cleansed using Python and formatted into an appropriate format for analysis. Specifically, this includes imputing missing data and removing inappropriate data. 【0295】 Subsequently, a generative AI model is used to analyze the collected and cleansed data, and reinforcement learning techniques are used to generate the optimal choices for each user. Using libraries such as TensorFlow enables efficient data processing and AI model execution. 【0296】 The generated options are sent to the device as push notifications or in-app messages. The device is a mobile communication device such as a smartphone or tablet, and this notification allows the user to receive suggestions in real time. 【0297】 Users can incorporate the options they receive into their daily lives, for example, by choosing to commute by bicycle on certain days, thus taking environmentally conscious actions. These choices and responses regarding their results are sent back to the server via the terminal, and the server uses this information to train an AI model, building a system that improves the accuracy of future suggestions. 【0298】 As a concrete example, by inputting a prompt message into the AI ​​model—"Based on today's weather and traffic information, suggest the best commuting method for the user"—the system will derive a commuting method that suits the user. In this way, the system helps users make choices that are mindful of nature and the environment. 【0299】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0300】 Step 1: 【0301】 The server retrieves weather information and energy consumption data from APIs of various specialized organizations. Inputs include API keys and query parameters, while output is environmental information in raw data format. This step involves the specific operation of collecting data using HTTP requests. 【0302】 Step 2: 【0303】 The server cleanses the acquired data and formats it into a format suitable for analysis. The input is raw data, and the output is formatted data. Data missing values ​​are imputed and outliers are removed; specifically, data processing is performed using Python scripts. 【0304】 Step 3: 【0305】 The server inputs the formatted data into the generative AI model for analysis. Specifically, it uses a reinforcement learning algorithm to generate optimal options for the user. The input is the formatted data, and the output is data as options considering the environment. The TensorFlow library is used to execute the model and process the data. 【0306】 Step 4: 【0307】 The server notifies the terminal of the generated options. The input for this step is the data of the generated options, and the output is the information displayed on the terminal. Specific operations include sending data using the push notification API. 【0308】 Step 5: 【0309】 The user checks the options provided through the terminal and makes a selection. The input is the presented options, and the output is the user's selection information. In this step, the user's interface operations are included as specific actions. 【0310】 Step 6: 【0311】 The terminal sends the user's selection information to the server. The input is the user's selection information, and the output is the response data stored in the server. As a specific operation, data transmission using a communication protocol is performed. 【0312】 Step 7: 【0313】 Based on the collected user selection information, the server trains the generative AI model to improve the accuracy of future proposals. The input is the user selection information, and the output is the improved AI model. Specific operations include training the model. 【0314】 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. 【0315】 This invention is a system that utilizes generative AI and an emotion engine to provide users with environmentally conscious choices, and can make suggestions that take into account the user's emotional state. 【0316】 In implementing this system, the server first collects environmental data. Specifically, it obtains weather information, energy consumption information, traffic information, etc., from various data sources and updates them in real time. This makes it possible to process data based on the latest environmental conditions. 【0317】 Next, the server cleanses the collected data and converts it into a format suitable for analysis. This process includes imputing missing values ​​and removing outliers to ensure data reliability. Then, a generative AI model is used to generate appropriate behavioral options for the environment. Reinforcement learning algorithms analyze the user's past behavioral data to provide optimal suggestions. 【0318】 Next, the device analyzes data such as voice and facial expressions using an emotion engine to recognize the user's emotions. Based on this data, the emotion engine has the ability to determine the user's emotional state in real time. For example, if it determines that the user is in a happy mood, suggestions will be made that align with that emotion. 【0319】 The suggestion generation mechanism takes the output of the emotion engine and generates options optimized for the user's emotional state. The device then communicates these generated suggestions to the user as notifications. The user can view the specific suggestions on the device screen and choose which action to take. For example, when feeling happy, the user might be suggested to take a short trip to a nature observation spot within walking distance. 【0320】 Subsequently, the user's actions and feedback are sent to the server via the device. The server uses this feedback data to continuously train the AI ​​model, improving the quality of future suggestions. This continuous learning enables flexible suggestions that respond to changes in the user's emotions and behavior. 【0321】 This system aims to reduce environmental impact while providing users with optimal choices that take their emotional state into consideration, thereby helping them live a more comfortable and environmentally friendly life. 【0322】 The following describes the processing flow. 【0323】 Step 1: 【0324】 The server aggregates environmental information such as weather data, energy consumption data, and traffic data from various data sources. This includes a process of obtaining real-time data using API connections to reflect the latest conditions. 【0325】 Step 2: 【0326】 The server cleanses the collected data, imputing outliers and missing values. It also uses deep learning algorithms and other techniques to preprocess the data and prepare it for analysis. 【0327】 Step 3: 【0328】 The device uses the user's voice input and images captured by the camera to analyze the user's emotions using an emotion engine. This analysis is then used to understand the user's mood and state in real time. 【0329】 Step 4: 【0330】 The server uses a generative AI model to generate optimal action options for the user, based on cleansed data and the results of the emotion engine's analysis. In this process, reinforcement learning algorithms are used to consider past behavioral data and the current environmental conditions. 【0331】 Step 5: 【0332】 The server sends the generated suggestions to the terminal. The terminal provides visual and audio feedback to the user, clearly explaining the advantages and reasons for each option. 【0333】 Step 6: 【0334】 The user reviews the options presented on the device and selects an action that suits their current situation. Based on the selected action, they then carry out actual travel or consumption activities. 【0335】 Step 7: 【0336】 Users send feedback about their chosen actions to the server via their device. This includes their impressions of the experience and their evaluation of the choices they made. 【0337】 Step 8: 【0338】 The server uses the feedback data to retrain the generating AI model, updating it to improve the accuracy of future suggestions. This feedback loop improves the overall accuracy and usability of the system. 【0339】 (Example 2) 【0340】 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". 【0341】 Reducing the environmental impact of individual lifestyles is crucial as a measure against environmental problems. However, conventional systems have been unable to provide flexible suggestions that take into account the user's emotional state, making it difficult to encourage sustainable behavioral change. Furthermore, there is a need to efficiently analyze the vast amount of environmental data and user information collected and provide individually optimized suggestions. 【0342】 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. 【0343】 In this invention, the server includes data collection means for collecting environmental data, data processing means for cleansing and analyzing the collected data, and emotion recognition means for recognizing the user's emotional state. This makes it possible to provide the user with choices that minimize environmental impact and are adapted to their emotional state. 【0344】 A "data collection means" is a mechanism for acquiring environmental data, enabling the real-time collection of weather information, energy consumption information, and other data. 【0345】 A "data processing means" is a mechanism for properly cleansing collected data and preparing it for analysis. It performs tasks such as imputing missing values ​​and removing outliers to ensure data reliability. 【0346】 An "emotion recognition system" is a mechanism that analyzes the user's voice and facial expression data to determine the user's emotional state in real time. 【0347】 A "proposal generation mechanism" is a system that uses a generation AI model to provide users with optimal action options that reduce environmental impact and are adapted to their emotional state. 【0348】 A "notification means" is a mechanism for communicating the options generated by the suggestion generation means to the user, and displays the notification on the user's terminal. 【0349】 A "learning tool" is a mechanism for collecting user feedback and continuously updating the learning model based on that feedback, thereby improving the accuracy of individually optimized suggestions. 【0350】 The embodiments for carrying out the present invention are shown below. 【0351】 This system combines a generative AI model with an emotion recognition engine to provide users with environmentally conscious choices. The server acquires diverse environmental data, such as weather information, energy consumption information, and traffic information, through data collection methods. In this process, it uses the data provider's API to record the information in a database in real time. 【0352】 Next, the server uses data processing tools to cleanse the collected environmental data and convert it into a parseable format. The Python programming language and its related libraries (e.g., Pandas, NumPy) are used to format the data and impute missing values. 【0353】 Subsequently, a generative AI model is used to prepare environmentally conscious action options. The server uses the TensorFlow library to implement reinforcement learning algorithms and predicts the optimal action based on past user behavior data. Examples of prompts used in this process include "Suggest activities that can be done at home while minimizing energy consumption when the user is emotionally calm." 【0354】 Meanwhile, the device evaluates the user's emotions using emotion recognition mechanisms. It analyzes emotions in real time from voice and facial expressions using input data from the device's built-in microphone and camera. This analysis utilizes image processing libraries such as OpenCV, as well as voice analysis tools. 【0355】 The suggestion generation method uses a generative AI model and emotion recognition results to create optimized options that are appropriate to the user's emotions. For example, if the user is in a happy mood, it suggests visiting a nearby park and notifies the user on their device. The user can then confirm and select this suggestion through the notification displayed on the screen. 【0356】 The system collects feedback from the device regarding the user's actions and sends it to the server. This feedback is used to update the AI ​​model through a learning mechanism, resulting in more personalized suggestions for the next time. 【0357】 This process in the system allows users to receive emotionally sensitive support while reducing their environmental impact, which is expected to improve their quality of life. 【0358】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0359】 Step 1: 【0360】 The server collects environmental data. It obtains data from APIs of weather, energy consumption, and traffic information providers as its information sources. API request parameters are used as input. The output is stored as raw data in the server's database. Specifically, it periodically retrieves data from each provider using HTTP requests and stores it in the database in JSON or XML format. 【0361】 Step 2: 【0362】 The server cleanses the collected data. It receives raw data as input and processes missing values ​​and outliers. For data processing, it manipulates dataframes using the Pandas library, imputes missing values ​​using statistical methods, and removes outliers. As output, the clean data is passed on to the next analysis step. Specifically, it applies methods such as mean imputation and outlier removal. 【0363】 Step 3: 【0364】 The server generates action options using a generative AI model based on clean data. It receives past user behavior history and current environmental data as input, which is then analyzed using a reinforcement learning algorithm. The output is an optimized list of action options. Specifically, TensorFlow is used, with a neural network evaluating actions, and the generated options are then evaluated and selected by the AI ​​model. 【0365】 Step 4: 【0366】 The device recognizes the user's emotional state. It receives audio data and camera footage in real time as input. For data processing, it uses video analysis and audio emotion recognition tools based on OpenCV to determine the emotional state. The output is stored on the device as user emotional state information. Specific operations include analyzing emotional tone from audio and identifying emotions from facial expressions. 【0367】 Step 5: 【0368】 Based on the generated action options and the user's sentiment information, the device generates suggestions and notifies the user. It receives options and sentiment recognition results from an AI model as input and optimizes the suggestions. The output is a notification message to the user, which includes specific action suggestions. Specific actions may include displaying suggestions on the user screen using a notification-centric UI design. 【0369】 Step 6: 【0370】 The user performs the suggested action and inputs feedback into the device. This input includes the user's actions and impressions. This input data is sent to the server and used to train the AI ​​model. The output is the feedback data necessary for training. Specifically, forms or simple questionnaires are provided on the device, through which the user provides feedback. 【0371】 Step 7: 【0372】 The server updates the generated AI model using the collected feedback data to improve the accuracy of the proposals. It continues reinforcement learning using the feedback data and the current model parameters as input. The output is the updated AI model, which will be used for the next proposal. Specifically, the model parameters are adjusted and retrained to improve accuracy. 【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】 In modern urban environments, residents seek choices that allow them to live comfortably while reducing their environmental impact. However, general information systems do not consider the user's emotional state in their individual suggestions, making it difficult to propose environmentally friendly behavioral choices optimized for the individual. This invention realizes a system that enables residents to raise their environmental awareness in their daily choices by utilizing real-time emotional data of users and providing suggestions that integrate with environmental data. 【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 information gathering means for comprehensively acquiring environmental data, information processing means for purifying and analyzing the acquired information, and suggestion generation means for generating suggestions based on the user's emotional state using an emotion analysis function. This allows for the provision of optimal action suggestions in real time by combining environmental data and the user's emotional state, making it easy for residents to make environmentally conscious choices in their daily lives. 【0378】 "Information gathering means" refers to a system for comprehensively acquiring environmental data related to climate and energy consumption. 【0379】 "Information processing means" refers to a function that cleanses and analyzes acquired information to enable accurate suggestions to the user. 【0380】 The "proposal generation method" is a system that uses emotion analysis functionality to generate optimal action suggestions based on the user's emotional state. 【0381】 "Communication means" refers to technology for transmitting generated proposals to user devices and providing real-time notifications. 【0382】 "Update methods" refer to the process of collecting user reactions and feedback and adaptively updating the learning model. 【0383】 A "machine learning algorithm" is a type of artificial intelligence technique used to generate appropriate suggestions for users. 【0384】 This invention is an information system for raising environmental awareness among residents in smart cities, and is implemented based on the respective roles of the server, terminal, and user. 【0385】 The server is equipped with an information gathering mechanism for comprehensively acquiring environmental data, and periodically collects climate-related and energy consumption-related information from external data sources. The collected data is purified by an information processing mechanism, which fills in missing data and removes outliers. This enables highly accurate data analysis in real time. 【0386】 Furthermore, the server uses a generative AI model to combine user behavior data and environmental data to create environmentally conscious behavioral suggestions. The suggestion generation method utilizes sentiment analysis functionality to reflect the user's emotional state and generate optimal suggestions. In this process, reinforcement learning algorithms are used to improve the model so that it can provide suggestions that are more suitable for the user. 【0387】 The device uses its camera and microphone to analyze the user's emotions in real time. It performs on-device emotion recognition using technologies such as the Emotion API and TensorFlow Lite. The analyzed emotion data is combined with environment suggestions received from the server to provide content tailored to the user's psychological state. 【0388】 Users receive notifications from their devices and choose actions based on the suggested actions. User feedback is sent to the server via the device, which continuously improves the accuracy of the suggestions. By providing personalized suggestions, residents can naturally choose actions in their daily lives that raise environmental awareness. 【0389】 For example, on a sunny day, a suggestion might be presented such as, "Today is a perfect day for going outside, so we recommend a walk to a nature observation spot." Another example of a prompt might be, "Based on today's weather and your mood, what are some recommended places to visit while being mindful of the environment?" Through such specific suggestions, residents can consciously choose environmentally friendly actions. 【0390】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0391】 Step 1: 【0392】 The server periodically collects environmental data from external data sources such as climate and energy consumption. Input is raw data from multiple APIs, and output is an integrated environmental dataset. Once data is acquired, it is stored in a database on the server. 【0393】 Step 2: 【0394】 The server cleanses and analyzes the collected environmental data. The input is the raw data obtained in step 1, and the output is a clean dataset. Specifically, it uses Pandas to fill in missing values, remove outliers, and convert the data into an analyzable format. 【0395】 Step 3: 【0396】 The server generates environmentally conscious suggestions using a generative AI model. The input consists of a clean dataset and data on the user's past behavior, while the output is specific action suggestions. The generative AI model uses a reinforcement learning algorithm to generate suggestions that lead to the user's optimal action. 【0397】 Step 4: 【0398】 The device uses its camera and microphone to acquire and analyze user emotion data. Input is real-time audio and image data, and output is the user's emotional state. Emotion analysis is performed on-device using the Emotion API and TensorFlow Lite. 【0399】 Step 5: 【0400】 The server optimizes suggestions based on the user's emotional state. The input is the suggestions generated in step 3 and the emotional state obtained in step 4, and the output is an action suggestion optimized for the emotional state. The suggestion generation means considers the emotional analysis data and customizes the suggestions. 【0401】 Step 6: 【0402】 The device communicates the final action suggestion to the user as a notification. The input is an optimized suggestion, and the output is a notification presented to the user visually or audibly. Information is conveyed to the user through the device screen or audio output. 【0403】 Step 7: 【0404】 The user receives suggestions and chooses an action. The input is a notification from the device, and the output is the user's action choice. The user selects what they believe to be the best option from the presented choices and puts it into action. 【0405】 Step 8: 【0406】 The device acquires user feedback and sends it to the server. Input is user feedback on their actions and satisfaction levels, while output is training data sent to the server. Feedback is collected through an in-app interface. 【0407】 Step 9: 【0408】 The server updates the AI ​​model using user feedback. The input is the feedback data obtained in step 8, and the output is the updated AI model. The server continuously updates the model based on reinforcement learning to improve the accuracy of future suggestions. 【0409】 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. 【0410】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0411】 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. 【0412】 [Third Embodiment] 【0413】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0414】 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. 【0415】 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). 【0416】 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. 【0417】 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. 【0418】 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). 【0419】 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. 【0420】 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. 【0421】 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. 【0422】 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. 【0423】 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. 【0424】 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". 【0425】 This invention is a system that proposes environmentally friendly options to users using generational AI, and operates in cooperation with a server, terminal, and user. 【0426】 In implementing this system, the server first acquires environmental data from various data sources. Specifically, it collects weather information and energy consumption data from APIs and systems of various specialized organizations. This allows the server to maintain the necessary data in real time. 【0427】 Next, the server cleanses the acquired data and converts it into a format suitable for analysis. This involves processes such as imputing missing values ​​and removing outliers. This prepares high-quality data for the generative AI model to function effectively. 【0428】 Next, the server uses a generative AI model to perform analysis and generate the optimal choices for the user. The generative AI uses a reinforcement learning algorithm to construct suggestions based on each user's past actions and current environmental conditions. 【0429】 Subsequently, this suggestion is sent to the device and presented to the user via push notifications, etc. The user reviews the multiple options displayed on the device screen and decides which option to adopt in their daily life. For example, in the case of suggestions for commuting transportation, the server notifies the user of transportation options and estimated arrival times based on traffic information and weather conditions collected by the server. In this way, users can take actions that ensure convenience while minimizing environmental impact. 【0430】 Finally, the user sends feedback about their chosen actions and their results to the server via their device. The server uses this data to train its AI model, improving the accuracy of future suggestions. Furthermore, by considering changes in the user's behavior patterns and preferences, the entire system can be continuously improved. 【0431】 This allows the system to efficiently and effectively support users in making environmentally friendly choices naturally. 【0432】 The following describes the processing flow. 【0433】 Step 1: 【0434】 The server connects to various data sources to collect necessary information in real time, such as weather data, energy consumption data, and traffic information. For example, it retrieves weather conditions via an API and extracts the latest usage data from an energy consumption database. 【0435】 Step 2: 【0436】 The server cleanses the collected raw data and prepares it for analysis. Specifically, it fills in missing data and removes obviously abnormal values. This process is crucial for ensuring the reliability and consistency of the data. 【0437】 Step 3: 【0438】 The server applies a generative AI model based on the cleansed data to generate optimal action options for the user. During this process, it uses reinforcement learning algorithms to analyze past user behavior data and formulate proposals to minimize environmental impact. 【0439】 Step 4: 【0440】 The server sends the generated suggestions to the device. The device communicates these suggestions to the user using push notifications or in-app notifications. The user can then see specific action options on the device screen. 【0441】 Step 5: 【0442】 Users review the suggestions they receive from their devices and decide which options to adopt in their daily activities. For example, they might choose whether or not to use the suggested mode of transportation for their commute. 【0443】 Step 6: 【0444】 Users send feedback about their chosen actions and their results to the server via their device. This records the user's actual behavior. 【0445】 Step 7: 【0446】 The server incorporates user feedback into its AI model, using it as training data for future suggestions. This allows the system to improve the accuracy of its suggestions over time, enabling it to present users with more appropriate options. 【0447】 (Example 1) 【0448】 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." 【0449】 In recent years, with growing awareness of environmental protection, individuals are expected to make choices that reduce their environmental impact in their daily lives. However, for users to make environmentally conscious choices, they need to collect and analyze a large amount of information, which is a significant burden. Furthermore, because users have limited opportunities to receive personalized recommendations in their decision-making, there are challenges in promoting concrete actions to reduce environmental impact. 【0450】 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. 【0451】 In this invention, the server includes information acquisition means for acquiring environmental information, information processing means for cleansing and processing the acquired information, and option generation means for proposing options to the user that reduce environmental impact. As a result, users can easily obtain appropriate options for environmentally conscious daily activities without having to perform complex information gathering and analysis. 【0452】 "Environmental information" refers to data such as weather conditions and energy usage in the natural world, and is information directly related to resource consumption and environmental impact. 【0453】 "Information acquisition means" refers to devices or methods that have the function of collecting necessary information from various data sources. 【0454】 "Information processing means" refers to a method that has the function of analyzing acquired information and processing data into an optimal format. 【0455】 A "choice generation means" is a method that has the function of creating the option that best suits specific conditions using available information and algorithms. 【0456】 "Communication means" refers to technologies and devices used to exchange information between a server and a user's terminal. 【0457】 "Update methods" refer to processes and methods for training artificial intelligence models based on feedback data to improve the accuracy and suitability of suggestions. 【0458】 One specific embodiment of the present invention is a system in which a server, a terminal, and a user cooperate to provide environmentally friendly options. 【0459】 First, the server receives environmental information from various data sources through information acquisition means. For example, APIs for weather information services and energy consumption monitoring systems are used. Subsequently, the server performs data cleansing using information processing means. This involves using data science languages ​​such as Python and R, and their libraries, to perform processes such as imputing missing values ​​and removing outliers. 【0460】 The server then uses a generative AI model to generate the best options for the user. Reinforcement learning algorithms are used in this process. Specifically, past behavioral data and current environmental conditions are input to the model as prompts. For example, "Consider the user's past commute choices, current weather information, and energy consumption data, and suggest the best commute option." 【0461】 Next, the device receives suggestions from the server via communication and presents them to the user using push notifications. Based on this, the user chooses specific actions to reduce their environmental impact. A concrete example in daily life might be that, based on weather information provided by the server, the user is recommended to commute by bicycle. 【0462】 Finally, the user sends feedback from their device to the server regarding the choices they made and the results. This feedback is used to retrain the AI ​​model through an update mechanism, which improves the accuracy of the suggestions and provides choices that better suit the user's preferences in the future. 【0463】 Thus, the present invention is a system that facilitates environmental considerations and supports users in naturally taking actions that reduce their environmental impact in their daily lives. 【0464】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0465】 Step 1: 【0466】 The server acquires environmental information. It uses weather information and energy consumption data obtained from specialized organizations' APIs and databases as input. Specifically, the server calls the API every hour to collect the latest data. The output is raw data collected in its unprocessed state. 【0467】 Step 2: 【0468】 The server cleanses the collected environmental information. It uses the raw data obtained in step 1 as input. The server imputes missing values ​​and removes statistical outliers. Specifically, it uses the Python Pandas library to impute missing values ​​from the dataframe with the mean. The output is a consistent dataset suitable for analysis. 【0469】 Step 3: 【0470】 The server generates optimal choices using a generative AI model. It uses consistent data processed in step 2 and the user's past behavioral history as input. The server inputs this information into the model as prompts and utilizes a reinforcement learning algorithm. Specifically, it integrates past data with current environmental data and generates a prompt for the AI ​​model: "Considering the user's past commuting choices, current weather information, and energy consumption data, please suggest the optimal commuting method." The output is an optimized action suggestion for each user. 【0471】 Step 4: 【0472】 The device receives suggestions from the server and notifies the user. The input is the suggestions generated in step 3. The device uses push notifications to present the user with options. Specifically, it displays a notification on the user's smartphone such as, "Cycling is recommended for your commute today." The output is a visual notification to the user. 【0473】 Step 5: 【0474】 The user receives a notification from their device and chooses their action. The input is the information presented by the device. Specifically, the user chooses to commute by bicycle based on the notification. The output is the selected action itself. 【0475】 Step 6: 【0476】 Users send feedback about the results of their actions to the server via their device. As input, they enter their actual experience and evaluation into a form. Specifically, users evaluate their cycling commute experience within the app in the format of "satisfaction with cycling commute." The output is stored on the server as feedback data. 【0477】 Step 7: 【0478】 The server collects feedback data and retrains the AI ​​model. The feedback data obtained in step 6 is used as input. Based on this, the server updates the algorithm parameters and improves the accuracy of the suggestions. Specifically, the feedback data is fed into a reinforcement learning algorithm, and a new model is trained during the server's nighttime downtime. The output is the newly tuned AI model. 【0479】 (Application Example 1) 【0480】 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." 【0481】 As environmental problems worsen, there is a growing need to promote actions that reduce the impact on the natural environment in daily life. However, it is difficult for individuals to judge and act on appropriate choices in their daily activities, and an effective support system is needed for this purpose. 【0482】 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. 【0483】 In this invention, the server includes information gathering means for collecting environmental information, information processing means for cleaning and analyzing the collected information, and choice generation means for providing options that reduce the impact on the natural environment in daily activities. This makes it possible for users to easily choose environmentally conscious actions. 【0484】 "Information gathering means for collecting environmental information" refers to methods and devices for acquiring environmental data such as weather conditions and energy consumption. 【0485】 "Information processing means for cleansing and analyzing collected information" refers to methods or devices that perform data acquisition, such as imputing missing values ​​and removing outliers, and then converting the data into an analyzable format. 【0486】 "Means for generating options that reduce the impact on the natural environment in daily life activities" refers to methods or devices that, based on collected and processed data, create options for users to choose environmentally conscious actions in their daily lives. 【0487】 "Notification means for mobile devices" refers to methods or devices for conveying generated environmental consideration options to communication devices such as smartphones. 【0488】 "Methods for collecting responses and updating predictive models" refer to methods or devices that collect user-selected actions and their outcomes, and use that information to improve predictive models in order to enhance the accuracy of suggestions. 【0489】 The system in this invention helps reduce the environmental impact in daily life by providing users with environmentally friendly options. The system mainly consists of three elements: a server, a terminal, and a user. 【0490】 The server first collects environmental information, such as weather conditions and energy consumption, via APIs and other means. This uses data collection methods that run on a cloud platform. Next, the collected data is cleansed using Python and formatted into an appropriate format for analysis. Specifically, this includes imputing missing data and removing inappropriate data. 【0491】 Subsequently, a generative AI model is used to analyze the collected and cleansed data, and reinforcement learning techniques are used to generate the optimal choices for each user. Using libraries such as TensorFlow enables efficient data processing and AI model execution. 【0492】 The generated options are sent to the device as push notifications or in-app messages. The device is a mobile communication device such as a smartphone or tablet, and this notification allows the user to receive suggestions in real time. 【0493】 Users can incorporate the options they receive into their daily lives, for example, by choosing to commute by bicycle on certain days, thus taking environmentally conscious actions. These choices and responses regarding their results are sent back to the server via the terminal, and the server uses this information to train an AI model, building a system that improves the accuracy of future suggestions. 【0494】 As a concrete example, by inputting a prompt message into the AI ​​model—"Based on today's weather and traffic information, suggest the best commuting method for the user"—the system will derive a commuting method that suits the user. In this way, the system helps users make choices that are mindful of nature and the environment. 【0495】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0496】 Step 1: 【0497】 The server retrieves weather information and energy consumption data from APIs of various specialized organizations. Inputs include API keys and query parameters, while output is environmental information in raw data format. This step involves the specific operation of collecting data using HTTP requests. 【0498】 Step 2: 【0499】 The server cleanses the acquired data and formats it into a format suitable for analysis. The input is raw data, and the output is formatted data. Data missing values ​​are imputed and outliers are removed; specifically, data processing is performed using Python scripts. 【0500】 Step 3: 【0501】 The server inputs the formatted data into an AI model for analysis. Specifically, it uses a reinforcement learning algorithm to generate the optimal choices for the user. The input is formatted data, and the output is data representing environmentally conscious choices. The TensorFlow library is used for model execution and data processing. 【0502】 Step 4: 【0503】 The server notifies the device of the generated choices. The input for this step is the data of the generated choices, and the output is the information displayed on the device. Specifically, this involves sending data using a push notification API. 【0504】 Step 5: 【0505】 The user reviews the options provided through the terminal and makes a selection. The input is the presented options, and the output is the user's selection information. This step includes user interface operations as concrete actions. 【0506】 Step 6: 【0507】 The terminal sends user selection information to the server. The input is the user's selection information, and the output is response data stored on the server. Specifically, data transmission is performed using a communication protocol. 【0508】 Step 7: 【0509】 The server trains a generative AI model based on collected user selection information, improving the accuracy of future suggestions. The input is user selection information, and the output is the improved AI model. Specific operations include model training. 【0510】 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. 【0511】 This invention is a system that utilizes generative AI and an emotion engine to provide users with environmentally conscious choices, and can make suggestions that take into account the user's emotional state. 【0512】 In implementing this system, the server first collects environmental data. Specifically, it obtains weather information, energy consumption information, traffic information, etc., from various data sources and updates them in real time. This makes it possible to process data based on the latest environmental conditions. 【0513】 Next, the server cleanses the collected data and converts it into a format suitable for analysis. This process includes imputing missing values ​​and removing outliers to ensure data reliability. Then, a generative AI model is used to generate appropriate behavioral options for the environment. Reinforcement learning algorithms analyze the user's past behavioral data to provide optimal suggestions. 【0514】 Next, the device analyzes data such as voice and facial expressions using an emotion engine to recognize the user's emotions. Based on this data, the emotion engine has the ability to determine the user's emotional state in real time. For example, if it determines that the user is in a happy mood, suggestions will be made that align with that emotion. 【0515】 The suggestion generation mechanism takes the output of the emotion engine and generates options optimized for the user's emotional state. The device then communicates these generated suggestions to the user as notifications. The user can view the specific suggestions on the device screen and choose which action to take. For example, when feeling happy, the user might be suggested to take a short trip to a nature observation spot within walking distance. 【0516】 Subsequently, the user's actions and feedback are sent to the server via the device. The server uses this feedback data to continuously train the AI ​​model, improving the quality of future suggestions. This continuous learning enables flexible suggestions that respond to changes in the user's emotions and behavior. 【0517】 This system aims to reduce environmental impact while providing users with optimal choices that take their emotional state into consideration, thereby helping them live a more comfortable and environmentally friendly life. 【0518】 The following describes the processing flow. 【0519】 Step 1: 【0520】 The server aggregates environmental information such as weather data, energy consumption data, and traffic data from various data sources. This includes a process of obtaining real-time data using API connections to reflect the latest conditions. 【0521】 Step 2: 【0522】 The server cleanses the collected data, imputing outliers and missing values. It also uses deep learning algorithms and other techniques to preprocess the data and prepare it for analysis. 【0523】 Step 3: 【0524】 The device uses the user's voice input and images captured by the camera to analyze the user's emotions using an emotion engine. This analysis is then used to understand the user's mood and state in real time. 【0525】 Step 4: 【0526】 The server uses a generative AI model to generate optimal action options for the user, based on cleansed data and the results of the emotion engine's analysis. In this process, reinforcement learning algorithms are used to consider past behavioral data and the current environmental conditions. 【0527】 Step 5: 【0528】 The server sends the generated suggestions to the terminal. The terminal provides visual and audio feedback to the user, clearly explaining the advantages and reasons for each option. 【0529】 Step 6: 【0530】 The user reviews the options presented on the device and selects an action that suits their current situation. Based on the selected action, they then carry out actual travel or consumption activities. 【0531】 Step 7: 【0532】 Users send feedback about their chosen actions to the server via their device. This includes their impressions of the experience and their evaluation of the choices they made. 【0533】 Step 8: 【0534】 The server uses the feedback data to retrain the generating AI model, updating it to improve the accuracy of future suggestions. This feedback loop improves the overall accuracy and usability of the system. 【0535】 (Example 2) 【0536】 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." 【0537】 Reducing the environmental impact of individual lifestyles is crucial as a measure against environmental problems. However, conventional systems have been unable to provide flexible suggestions that take into account the user's emotional state, making it difficult to encourage sustainable behavioral change. Furthermore, there is a need to efficiently analyze the vast amount of environmental data and user information collected and provide individually optimized suggestions. 【0538】 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. 【0539】 In this invention, the server includes data collection means for collecting environmental data, data processing means for cleansing and analyzing the collected data, and emotion recognition means for recognizing the user's emotional state. This makes it possible to provide the user with choices that minimize environmental impact and are adapted to their emotional state. 【0540】 A "data collection means" is a mechanism for acquiring environmental data, enabling the real-time collection of weather information, energy consumption information, and other data. 【0541】 A "data processing means" is a mechanism for properly cleansing collected data and preparing it for analysis. It performs tasks such as imputing missing values ​​and removing outliers to ensure data reliability. 【0542】 An "emotion recognition system" is a mechanism that analyzes the user's voice and facial expression data to determine the user's emotional state in real time. 【0543】 A "proposal generation mechanism" is a system that uses a generation AI model to provide users with optimal action options that reduce environmental impact and are adapted to their emotional state. 【0544】 A "notification means" is a mechanism for communicating the options generated by the suggestion generation means to the user, and displays the notification on the user's terminal. 【0545】 A "learning tool" is a mechanism for collecting user feedback and continuously updating the learning model based on that feedback, thereby improving the accuracy of individually optimized suggestions. 【0546】 The embodiments for carrying out the present invention are shown below. 【0547】 This system combines a generative AI model with an emotion recognition engine to provide users with environmentally conscious choices. The server acquires diverse environmental data, such as weather information, energy consumption information, and traffic information, through data collection methods. In this process, it uses the data provider's API to record the information in a database in real time. 【0548】 Next, the server uses data processing tools to cleanse the collected environmental data and convert it into a parseable format. The Python programming language and its related libraries (e.g., Pandas, NumPy) are used to format the data and impute missing values. 【0549】 Subsequently, a generative AI model is used to prepare environmentally conscious action options. The server uses the TensorFlow library to implement reinforcement learning algorithms and predicts the optimal action based on past user behavior data. Examples of prompts used in this process include "Suggest activities that can be done at home while minimizing energy consumption when the user is emotionally calm." 【0550】 Meanwhile, the device evaluates the user's emotions using emotion recognition mechanisms. It analyzes emotions in real time from voice and facial expressions using input data from the device's built-in microphone and camera. This analysis utilizes image processing libraries such as OpenCV, as well as voice analysis tools. 【0551】 The suggestion generation method uses a generative AI model and emotion recognition results to create optimized options that are appropriate to the user's emotions. For example, if the user is in a happy mood, it suggests visiting a nearby park and notifies the user on their device. The user can then confirm and select this suggestion through the notification displayed on the screen. 【0552】 The system collects feedback from the device regarding the user's actions and sends it to the server. This feedback is used to update the AI ​​model through a learning mechanism, resulting in more personalized suggestions for the next time. 【0553】 This process in the system allows users to receive emotionally sensitive support while reducing their environmental impact, which is expected to improve their quality of life. 【0554】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0555】 Step 1: 【0556】 The server collects environmental data. It obtains data from APIs of weather, energy consumption, and traffic information providers as its information sources. API request parameters are used as input. The output is stored as raw data in the server's database. Specifically, it periodically retrieves data from each provider using HTTP requests and stores it in the database in JSON or XML format. 【0557】 Step 2: 【0558】 The server cleanses the collected data. It receives raw data as input and processes missing values ​​and outliers. For data processing, it manipulates dataframes using the Pandas library, imputes missing values ​​using statistical methods, and removes outliers. As output, the clean data is passed on to the next analysis step. Specifically, it applies methods such as mean imputation and outlier removal. 【0559】 Step 3: 【0560】 The server generates action options using a generative AI model based on clean data. It receives past user behavior history and current environmental data as input, which is then analyzed using a reinforcement learning algorithm. The output is an optimized list of action options. Specifically, TensorFlow is used, with a neural network evaluating actions, and the generated options are then evaluated and selected by the AI ​​model. 【0561】 Step 4: 【0562】 The device recognizes the user's emotional state. It receives audio data and camera footage in real time as input. For data processing, it uses video analysis and audio emotion recognition tools based on OpenCV to determine the emotional state. The output is stored on the device as user emotional state information. Specific operations include analyzing emotional tone from audio and identifying emotions from facial expressions. 【0563】 Step 5: 【0564】 Based on the generated action options and the user's sentiment information, the device generates suggestions and notifies the user. It receives options and sentiment recognition results from an AI model as input and optimizes the suggestions. The output is a notification message to the user, which includes specific action suggestions. Specific actions may include displaying suggestions on the user screen using a notification-centric UI design. 【0565】 Step 6: 【0566】 The user performs the suggested action and inputs feedback into the device. This input includes the user's actions and impressions. This input data is sent to the server and used to train the AI ​​model. The output is the feedback data necessary for training. Specifically, forms or simple questionnaires are provided on the device, through which the user provides feedback. 【0567】 Step 7: 【0568】 The server updates the generated AI model using the collected feedback data to improve the accuracy of the proposals. It continues reinforcement learning using the feedback data and the current model parameters as input. The output is the updated AI model, which will be used for the next proposal. Specifically, the model parameters are adjusted and retrained to improve accuracy. 【0569】 (Application Example 2) 【0570】 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." 【0571】 In modern urban environments, residents seek choices that allow them to live comfortably while reducing their environmental impact. However, general information systems do not consider the user's emotional state in their individual suggestions, making it difficult to propose environmentally friendly behavioral choices optimized for the individual. This invention realizes a system that enables residents to raise their environmental awareness in their daily choices by utilizing real-time emotional data of users and providing suggestions that integrate with environmental data. 【0572】 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. 【0573】 In this invention, the server includes information gathering means for comprehensively acquiring environmental data, information processing means for purifying and analyzing the acquired information, and suggestion generation means for generating suggestions based on the user's emotional state using an emotion analysis function. This allows for the provision of optimal action suggestions in real time by combining environmental data and the user's emotional state, making it easy for residents to make environmentally conscious choices in their daily lives. 【0574】 "Information gathering means" refers to a system for comprehensively acquiring environmental data related to climate and energy consumption. 【0575】 "Information processing means" refers to a function that cleanses and analyzes acquired information to enable accurate suggestions to the user. 【0576】 The "proposal generation method" is a system that uses emotion analysis functionality to generate optimal action suggestions based on the user's emotional state. 【0577】 "Communication means" refers to technology for transmitting generated proposals to user devices and providing real-time notifications. 【0578】 "Update methods" refer to the process of collecting user reactions and feedback and adaptively updating the learning model. 【0579】 A "machine learning algorithm" is a type of artificial intelligence technique used to generate appropriate suggestions for users. 【0580】 This invention is an information system for raising environmental awareness among residents in smart cities, and is implemented based on the respective roles of the server, terminal, and user. 【0581】 The server is equipped with an information gathering mechanism for comprehensively acquiring environmental data, and periodically collects climate-related and energy consumption-related information from external data sources. The collected data is purified by an information processing mechanism, which fills in missing data and removes outliers. This enables highly accurate data analysis in real time. 【0582】 Furthermore, the server uses a generative AI model to combine user behavior data and environmental data to create environmentally conscious behavioral suggestions. The suggestion generation method utilizes sentiment analysis functionality to reflect the user's emotional state and generate optimal suggestions. In this process, reinforcement learning algorithms are used to improve the model so that it can provide suggestions that are more suitable for the user. 【0583】 The device uses its camera and microphone to analyze the user's emotions in real time. It performs on-device emotion recognition using technologies such as the Emotion API and TensorFlow Lite. The analyzed emotion data is combined with environment suggestions received from the server to provide content tailored to the user's psychological state. 【0584】 Users receive notifications from their devices and choose actions based on the suggested actions. User feedback is sent to the server via the device, which continuously improves the accuracy of the suggestions. By providing personalized suggestions, residents can naturally choose actions in their daily lives that raise environmental awareness. 【0585】 For example, on a sunny day, a suggestion might be presented such as, "Today is a perfect day for going outside, so we recommend a walk to a nature observation spot." Another example of a prompt might be, "Based on today's weather and your mood, what are some recommended places to visit while being mindful of the environment?" Through such specific suggestions, residents can consciously choose environmentally friendly actions. 【0586】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0587】 Step 1: 【0588】 The server periodically collects environmental data from external data sources such as climate and energy consumption. Input is raw data from multiple APIs, and output is an integrated environmental dataset. Once data is acquired, it is stored in a database on the server. 【0589】 Step 2: 【0590】 The server cleanses and analyzes the collected environmental data. The input is the raw data obtained in step 1, and the output is a clean dataset. Specifically, it uses Pandas to fill in missing values, remove outliers, and convert the data into an analyzable format. 【0591】 Step 3: 【0592】 The server generates environmentally conscious suggestions using a generative AI model. The input consists of a clean dataset and data on the user's past behavior, while the output is specific action suggestions. The generative AI model uses a reinforcement learning algorithm to generate suggestions that lead to the user's optimal action. 【0593】 Step 4: 【0594】 The device uses its camera and microphone to acquire and analyze user emotion data. Input is real-time audio and image data, and output is the user's emotional state. Emotion analysis is performed on-device using the Emotion API and TensorFlow Lite. 【0595】 Step 5: 【0596】 The server optimizes suggestions based on the user's emotional state. The input is the suggestions generated in step 3 and the emotional state obtained in step 4, and the output is an action suggestion optimized for the emotional state. The suggestion generation means considers the emotional analysis data and customizes the suggestions. 【0597】 Step 6: 【0598】 The device communicates the final action suggestion to the user as a notification. The input is an optimized suggestion, and the output is a notification presented to the user visually or audibly. Information is conveyed to the user through the device screen or audio output. 【0599】 Step 7: 【0600】 The user receives suggestions and chooses an action. The input is a notification from the device, and the output is the user's action choice. The user selects what they believe to be the best option from the presented choices and puts it into action. 【0601】 Step 8: 【0602】 The device acquires user feedback and sends it to the server. Input is user feedback on their actions and satisfaction levels, while output is training data sent to the server. Feedback is collected through an in-app interface. 【0603】 Step 9: 【0604】 The server updates the AI ​​model using user feedback. The input is the feedback data obtained in step 8, and the output is the updated AI model. The server continuously updates the model based on reinforcement learning to improve the accuracy of future suggestions. 【0605】 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. 【0606】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0607】 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. 【0608】 [Fourth Embodiment] 【0609】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0610】 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. 【0611】 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). 【0612】 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. 【0613】 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. 【0614】 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). 【0615】 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. 【0616】 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. 【0617】 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. 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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". 【0622】 This invention is a system that proposes environmentally friendly options to users using generational AI, and operates in cooperation with a server, terminal, and user. 【0623】 In implementing this system, the server first acquires environmental data from various data sources. Specifically, it collects weather information and energy consumption data from APIs and systems of various specialized organizations. This allows the server to maintain the necessary data in real time. 【0624】 Next, the server cleanses the acquired data and converts it into a format suitable for analysis. This involves processes such as imputing missing values ​​and removing outliers. This prepares high-quality data for the generative AI model to function effectively. 【0625】 Next, the server uses a generative AI model to perform analysis and generate the optimal choices for the user. The generative AI uses a reinforcement learning algorithm to construct suggestions based on each user's past actions and current environmental conditions. 【0626】 Subsequently, this suggestion is sent to the device and presented to the user via push notifications, etc. The user reviews the multiple options displayed on the device screen and decides which option to adopt in their daily life. For example, in the case of suggestions for commuting transportation, the server notifies the user of transportation options and estimated arrival times based on traffic information and weather conditions collected by the server. In this way, users can take actions that ensure convenience while minimizing environmental impact. 【0627】 Finally, the user sends feedback about their chosen actions and their results to the server via their device. The server uses this data to train its AI model, improving the accuracy of future suggestions. Furthermore, by considering changes in the user's behavior patterns and preferences, the entire system can be continuously improved. 【0628】 This allows the system to efficiently and effectively support users in making environmentally friendly choices naturally. 【0629】 The following describes the processing flow. 【0630】 Step 1: 【0631】 The server connects to various data sources to collect necessary information in real time, such as weather data, energy consumption data, and traffic information. For example, it retrieves weather conditions via an API and extracts the latest usage data from an energy consumption database. 【0632】 Step 2: 【0633】 The server cleanses the collected raw data and prepares it for analysis. Specifically, it fills in missing data and removes obviously abnormal values. This process is crucial for ensuring the reliability and consistency of the data. 【0634】 Step 3: 【0635】 The server applies a generative AI model based on the cleansed data to generate optimal action options for the user. During this process, it uses reinforcement learning algorithms to analyze past user behavior data and formulate proposals to minimize environmental impact. 【0636】 Step 4: 【0637】 The server sends the generated suggestions to the device. The device communicates these suggestions to the user using push notifications or in-app notifications. The user can then see specific action options on the device screen. 【0638】 Step 5: 【0639】 Users review the suggestions they receive from their devices and decide which options to adopt in their daily activities. For example, they might choose whether or not to use the suggested mode of transportation for their commute. 【0640】 Step 6: 【0641】 Users send feedback about their chosen actions and their results to the server via their device. This records the user's actual behavior. 【0642】 Step 7: 【0643】 The server incorporates user feedback into its AI model, using it as training data for future suggestions. This allows the system to improve the accuracy of its suggestions over time, enabling it to present users with more appropriate options. 【0644】 (Example 1) 【0645】 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". 【0646】 In recent years, with growing awareness of environmental protection, individuals are expected to make choices that reduce their environmental impact in their daily lives. However, for users to make environmentally conscious choices, they need to collect and analyze a large amount of information, which is a significant burden. Furthermore, because users have limited opportunities to receive personalized recommendations in their decision-making, there are challenges in promoting concrete actions to reduce environmental impact. 【0647】 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. 【0648】 In this invention, the server includes information acquisition means for acquiring environmental information, information processing means for cleansing and processing the acquired information, and option generation means for proposing options to the user that reduce environmental impact. As a result, users can easily obtain appropriate options for environmentally conscious daily activities without having to perform complex information gathering and analysis. 【0649】 "Environmental information" refers to data such as weather conditions and energy usage in the natural world, and is information directly related to resource consumption and environmental impact. 【0650】 "Information acquisition means" refers to devices or methods that have the function of collecting necessary information from various data sources. 【0651】 "Information processing means" refers to a method that has the function of analyzing acquired information and processing data into an optimal format. 【0652】 A "choice generation means" is a method that has the function of creating the option that best suits specific conditions using available information and algorithms. 【0653】 "Communication means" refers to technologies and devices used to exchange information between a server and a user's terminal. 【0654】 "Update methods" refer to processes and methods for training artificial intelligence models based on feedback data to improve the accuracy and suitability of suggestions. 【0655】 One specific embodiment of the present invention is a system in which a server, a terminal, and a user cooperate to provide environmentally friendly options. 【0656】 First, the server receives environmental information from various data sources through information acquisition means. For example, APIs for weather information services and energy consumption monitoring systems are used. Subsequently, the server performs data cleansing using information processing means. This involves using data science languages ​​such as Python and R, and their libraries, to perform processes such as imputing missing values ​​and removing outliers. 【0657】 The server then uses a generative AI model to generate the best options for the user. Reinforcement learning algorithms are used in this process. Specifically, past behavioral data and current environmental conditions are input to the model as prompts. For example, "Consider the user's past commute choices, current weather information, and energy consumption data, and suggest the best commute option." 【0658】 Next, the device receives suggestions from the server via communication and presents them to the user using push notifications. Based on this, the user chooses specific actions to reduce their environmental impact. A concrete example in daily life might be that, based on weather information provided by the server, the user is recommended to commute by bicycle. 【0659】 Finally, the user sends feedback from their device to the server regarding the choices they made and the results. This feedback is used to retrain the AI ​​model through an update mechanism, which improves the accuracy of the suggestions and provides choices that better suit the user's preferences in the future. 【0660】 Thus, the present invention is a system that facilitates environmental considerations and supports users in naturally taking actions that reduce their environmental impact in their daily lives. 【0661】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0662】 Step 1: 【0663】 The server acquires environmental information. It uses weather information and energy consumption data obtained from specialized organizations' APIs and databases as input. Specifically, the server calls the API every hour to collect the latest data. The output is raw data collected in its unprocessed state. 【0664】 Step 2: 【0665】 The server cleanses the collected environmental information. It uses the raw data obtained in step 1 as input. The server imputes missing values ​​and removes statistical outliers. Specifically, it uses the Python Pandas library to impute missing values ​​from the dataframe with the mean. The output is a consistent dataset suitable for analysis. 【0666】 Step 3: 【0667】 The server generates optimal choices using a generative AI model. It uses consistent data processed in step 2 and the user's past behavioral history as input. The server inputs this information into the model as prompts and utilizes a reinforcement learning algorithm. Specifically, it integrates past data with current environmental data and generates a prompt for the AI ​​model: "Considering the user's past commuting choices, current weather information, and energy consumption data, please suggest the optimal commuting method." The output is an optimized action suggestion for each user. 【0668】 Step 4: 【0669】 The device receives suggestions from the server and notifies the user. The input is the suggestions generated in step 3. The device uses push notifications to present the user with options. Specifically, it displays a notification on the user's smartphone such as, "Cycling is recommended for your commute today." The output is a visual notification to the user. 【0670】 Step 5: 【0671】 The user receives a notification from their device and chooses their action. The input is the information presented by the device. Specifically, the user chooses to commute by bicycle based on the notification. The output is the selected action itself. 【0672】 Step 6: 【0673】 Users send feedback about the results of their actions to the server via their device. As input, they enter their actual experience and evaluation into a form. Specifically, users evaluate their cycling commute experience within the app in the format of "satisfaction with cycling commute." The output is stored on the server as feedback data. 【0674】 Step 7: 【0675】 The server collects feedback data and retrains the AI ​​model. The feedback data obtained in step 6 is used as input. Based on this, the server updates the algorithm parameters and improves the accuracy of the suggestions. Specifically, the feedback data is fed into a reinforcement learning algorithm, and a new model is trained during the server's nighttime downtime. The output is the newly tuned AI model. 【0676】 (Application Example 1) 【0677】 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". 【0678】 As environmental problems worsen, there is a growing need to promote actions that reduce the impact on the natural environment in daily life. However, it is difficult for individuals to judge and act on appropriate choices in their daily activities, and an effective support system is needed for this purpose. 【0679】 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. 【0680】 In this invention, the server includes information gathering means for collecting environmental information, information processing means for cleaning and analyzing the collected information, and choice generation means for providing options that reduce the impact on the natural environment in daily activities. This makes it possible for users to easily choose environmentally conscious actions. 【0681】 "Information gathering means for collecting environmental information" refers to methods and devices for acquiring environmental data such as weather conditions and energy consumption. 【0682】 "Information processing means for cleansing and analyzing collected information" refers to methods or devices that perform data acquisition, such as imputing missing values ​​and removing outliers, and then converting the data into an analyzable format. 【0683】 "Means for generating options that reduce the impact on the natural environment in daily life activities" refers to methods or devices that, based on collected and processed data, create options for users to choose environmentally conscious actions in their daily lives. 【0684】 "Notification means for mobile devices" refers to methods or devices for conveying generated environmental consideration options to communication devices such as smartphones. 【0685】 "Methods for collecting responses and updating predictive models" refer to methods or devices that collect user-selected actions and their outcomes, and use that information to improve predictive models in order to enhance the accuracy of suggestions. 【0686】 The system in this invention helps reduce the environmental impact in daily life by providing users with environmentally friendly options. The system mainly consists of three elements: a server, a terminal, and a user. 【0687】 The server first collects environmental information, such as weather conditions and energy consumption, via APIs and other means. This uses data collection methods that run on a cloud platform. Next, the collected data is cleansed using Python and formatted into an appropriate format for analysis. Specifically, this includes imputing missing data and removing inappropriate data. 【0688】 Subsequently, a generative AI model is used to analyze the collected and cleansed data, and reinforcement learning techniques are used to generate the optimal choices for each user. Using libraries such as TensorFlow enables efficient data processing and AI model execution. 【0689】 The generated options are sent to the device as push notifications or in-app messages. The device is a mobile communication device such as a smartphone or tablet, and this notification allows the user to receive suggestions in real time. 【0690】 Users can incorporate the options they receive into their daily lives, for example, by choosing to commute by bicycle on certain days, thus taking environmentally conscious actions. These choices and responses regarding their results are sent back to the server via the terminal, and the server uses this information to train an AI model, building a system that improves the accuracy of future suggestions. 【0691】 As a concrete example, by inputting a prompt message into the AI ​​model—"Based on today's weather and traffic information, suggest the best commuting method for the user"—the system will derive a commuting method that suits the user. In this way, the system helps users make choices that are mindful of nature and the environment. 【0692】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0693】 Step 1: 【0694】 The server retrieves weather information and energy consumption data from APIs of various specialized organizations. Inputs include API keys and query parameters, while output is environmental information in raw data format. This step involves the specific operation of collecting data using HTTP requests. 【0695】 Step 2: 【0696】 The server cleanses the acquired data and formats it into a format suitable for analysis. The input is raw data, and the output is formatted data. Data missing values ​​are imputed and outliers are removed; specifically, data processing is performed using Python scripts. 【0697】 Step 3: 【0698】 The server inputs the formatted data into an AI model for analysis. Specifically, it uses a reinforcement learning algorithm to generate the optimal choices for the user. The input is formatted data, and the output is data representing environmentally conscious choices. The TensorFlow library is used for model execution and data processing. 【0699】 Step 4: 【0700】 The server notifies the device of the generated choices. The input for this step is the data of the generated choices, and the output is the information displayed on the device. Specifically, this involves sending data using a push notification API. 【0701】 Step 5: 【0702】 The user reviews the options provided through the terminal and makes a selection. The input is the presented options, and the output is the user's selection information. This step includes user interface operations as concrete actions. 【0703】 Step 6: 【0704】 The terminal sends user selection information to the server. The input is the user's selection information, and the output is response data stored on the server. Specifically, data transmission is performed using a communication protocol. 【0705】 Step 7: 【0706】 The server trains a generative AI model based on collected user selection information, improving the accuracy of future suggestions. The input is user selection information, and the output is the improved AI model. Specific operations include model training. 【0707】 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. 【0708】 This invention is a system that utilizes generative AI and an emotion engine to provide users with environmentally conscious choices, and can make suggestions that take into account the user's emotional state. 【0709】 In implementing this system, the server first collects environmental data. Specifically, it obtains weather information, energy consumption information, traffic information, etc., from various data sources and updates them in real time. This makes it possible to process data based on the latest environmental conditions. 【0710】 Next, the server cleanses the collected data and converts it into a format suitable for analysis. This process includes imputing missing values ​​and removing outliers to ensure data reliability. Then, a generative AI model is used to generate appropriate behavioral options for the environment. Reinforcement learning algorithms analyze the user's past behavioral data to provide optimal suggestions. 【0711】 Next, the device analyzes data such as voice and facial expressions using an emotion engine to recognize the user's emotions. Based on this data, the emotion engine has the ability to determine the user's emotional state in real time. For example, if it determines that the user is in a happy mood, suggestions will be made that align with that emotion. 【0712】 The suggestion generation mechanism takes the output of the emotion engine and generates options optimized for the user's emotional state. The device then communicates these generated suggestions to the user as notifications. The user can view the specific suggestions on the device screen and choose which action to take. For example, when feeling happy, the user might be suggested to take a short trip to a nature observation spot within walking distance. 【0713】 Subsequently, the user's actions and feedback are sent to the server via the device. The server uses this feedback data to continuously train the AI ​​model, improving the quality of future suggestions. This continuous learning enables flexible suggestions that respond to changes in the user's emotions and behavior. 【0714】 This system aims to reduce environmental impact while providing users with optimal choices that take their emotional state into consideration, thereby helping them live a more comfortable and environmentally friendly life. 【0715】 The following describes the processing flow. 【0716】 Step 1: 【0717】 The server aggregates environmental information such as weather data, energy consumption data, and traffic data from various data sources. This includes a process of obtaining real-time data using API connections to reflect the latest conditions. 【0718】 Step 2: 【0719】 The server cleanses the collected data, imputing outliers and missing values. It also uses deep learning algorithms and other techniques to preprocess the data and prepare it for analysis. 【0720】 Step 3: 【0721】 The device uses the user's voice input and images captured by the camera to analyze the user's emotions using an emotion engine. This analysis is then used to understand the user's mood and state in real time. 【0722】 Step 4: 【0723】 The server uses a generative AI model to generate optimal action options for the user, based on cleansed data and the results of the emotion engine's analysis. In this process, reinforcement learning algorithms are used to consider past behavioral data and the current environmental conditions. 【0724】 Step 5: 【0725】 The server sends the generated suggestions to the terminal. The terminal provides visual and audio feedback to the user, clearly explaining the advantages and reasons for each option. 【0726】 Step 6: 【0727】 The user reviews the options presented on the device and selects an action that suits their current situation. Based on the selected action, they then carry out actual travel or consumption activities. 【0728】 Step 7: 【0729】 Users send feedback about their chosen actions to the server via their device. This includes their impressions of the experience and their evaluation of the choices they made. 【0730】 Step 8: 【0731】 The server uses the feedback data to retrain the generating AI model, updating it to improve the accuracy of future suggestions. This feedback loop improves the overall accuracy and usability of the system. 【0732】 (Example 2) 【0733】 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". 【0734】 Reducing the environmental impact of individual lifestyles is crucial as a measure against environmental problems. However, conventional systems have been unable to provide flexible suggestions that take into account the user's emotional state, making it difficult to encourage sustainable behavioral change. Furthermore, there is a need to efficiently analyze the vast amount of environmental data and user information collected and provide individually optimized suggestions. 【0735】 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. 【0736】 In this invention, the server includes data collection means for collecting environmental data, data processing means for cleansing and analyzing the collected data, and emotion recognition means for recognizing the user's emotional state. This makes it possible to provide the user with choices that minimize environmental impact and are adapted to their emotional state. 【0737】 A "data collection means" is a mechanism for acquiring environmental data, enabling the real-time collection of weather information, energy consumption information, and other data. 【0738】 A "data processing means" is a mechanism for properly cleansing collected data and preparing it for analysis. It performs tasks such as imputing missing values ​​and removing outliers to ensure data reliability. 【0739】 An "emotion recognition system" is a mechanism that analyzes the user's voice and facial expression data to determine the user's emotional state in real time. 【0740】 A "proposal generation mechanism" is a system that uses a generation AI model to provide users with optimal action options that reduce environmental impact and are adapted to their emotional state. 【0741】 A "notification means" is a mechanism for communicating the options generated by the suggestion generation means to the user, and displays the notification on the user's terminal. 【0742】 A "learning tool" is a mechanism for collecting user feedback and continuously updating the learning model based on that feedback, thereby improving the accuracy of individually optimized suggestions. 【0743】 The embodiments for carrying out the present invention are shown below. 【0744】 This system combines a generative AI model with an emotion recognition engine to provide users with environmentally conscious choices. The server acquires diverse environmental data, such as weather information, energy consumption information, and traffic information, through data collection methods. In this process, it uses the data provider's API to record the information in a database in real time. 【0745】 Next, the server uses data processing tools to cleanse the collected environmental data and convert it into a parseable format. The Python programming language and its related libraries (e.g., Pandas, NumPy) are used to format the data and impute missing values. 【0746】 Subsequently, a generative AI model is used to prepare environmentally conscious action options. The server uses the TensorFlow library to implement reinforcement learning algorithms and predicts the optimal action based on past user behavior data. Examples of prompts used in this process include "Suggest activities that can be done at home while minimizing energy consumption when the user is emotionally calm." 【0747】 Meanwhile, the device evaluates the user's emotions using emotion recognition mechanisms. It analyzes emotions in real time from voice and facial expressions using input data from the device's built-in microphone and camera. This analysis utilizes image processing libraries such as OpenCV, as well as voice analysis tools. 【0748】 The suggestion generation method uses a generative AI model and emotion recognition results to create optimized options that are appropriate to the user's emotions. For example, if the user is in a happy mood, it suggests visiting a nearby park and notifies the user on their device. The user can then confirm and select this suggestion through the notification displayed on the screen. 【0749】 The system collects feedback from the device regarding the user's actions and sends it to the server. This feedback is used to update the AI ​​model through a learning mechanism, resulting in more personalized suggestions for the next time. 【0750】 This process in the system allows users to receive emotionally sensitive support while reducing their environmental impact, which is expected to improve their quality of life. 【0751】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0752】 Step 1: 【0753】 The server collects environmental data. It obtains data from APIs of weather, energy consumption, and traffic information providers as its information sources. API request parameters are used as input. The output is stored as raw data in the server's database. Specifically, it periodically retrieves data from each provider using HTTP requests and stores it in the database in JSON or XML format. 【0754】 Step 2: 【0755】 The server cleanses the collected data. It receives raw data as input and processes missing values ​​and outliers. For data processing, it manipulates dataframes using the Pandas library, imputes missing values ​​using statistical methods, and removes outliers. As output, the clean data is passed on to the next analysis step. Specifically, it applies methods such as mean imputation and outlier removal. 【0756】 Step 3: 【0757】 The server generates action options using a generative AI model based on clean data. It receives past user behavior history and current environmental data as input, which is then analyzed using a reinforcement learning algorithm. The output is an optimized list of action options. Specifically, TensorFlow is used, with a neural network evaluating actions, and the generated options are then evaluated and selected by the AI ​​model. 【0758】 Step 4: 【0759】 The device recognizes the user's emotional state. It receives audio data and camera footage in real time as input. For data processing, it uses video analysis and audio emotion recognition tools based on OpenCV to determine the emotional state. The output is stored on the device as user emotional state information. Specific operations include analyzing emotional tone from audio and identifying emotions from facial expressions. 【0760】 Step 5: 【0761】 Based on the generated action options and the user's sentiment information, the device generates suggestions and notifies the user. It receives options and sentiment recognition results from an AI model as input and optimizes the suggestions. The output is a notification message to the user, which includes specific action suggestions. Specific actions may include displaying suggestions on the user screen using a notification-centric UI design. 【0762】 Step 6: 【0763】 The user performs the suggested action and inputs feedback into the device. This input includes the user's actions and impressions. This input data is sent to the server and used to train the AI ​​model. The output is the feedback data necessary for training. Specifically, forms or simple questionnaires are provided on the device, through which the user provides feedback. 【0764】 Step 7: 【0765】 The server updates the generated AI model using the collected feedback data to improve the accuracy of the proposals. It continues reinforcement learning using the feedback data and the current model parameters as input. The output is the updated AI model, which will be used for the next proposal. Specifically, the model parameters are adjusted and retrained to improve accuracy. 【0766】 (Application Example 2) 【0767】 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". 【0768】 In modern urban environments, residents seek choices that allow them to live comfortably while reducing their environmental impact. However, general information systems do not consider the user's emotional state in their individual suggestions, making it difficult to propose environmentally friendly behavioral choices optimized for the individual. This invention realizes a system that enables residents to raise their environmental awareness in their daily choices by utilizing real-time emotional data of users and providing suggestions that integrate with environmental data. 【0769】 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. 【0770】 In this invention, the server includes information gathering means for comprehensively acquiring environmental data, information processing means for purifying and analyzing the acquired information, and suggestion generation means for generating suggestions based on the user's emotional state using an emotion analysis function. This allows for the provision of optimal action suggestions in real time by combining environmental data and the user's emotional state, making it easy for residents to make environmentally conscious choices in their daily lives. 【0771】 "Information gathering means" refers to a system for comprehensively acquiring environmental data related to climate and energy consumption. 【0772】 "Information processing means" refers to a function that cleanses and analyzes acquired information to enable accurate suggestions to the user. 【0773】 The "proposal generation method" is a system that uses emotion analysis functionality to generate optimal action suggestions based on the user's emotional state. 【0774】 "Communication means" refers to technology for transmitting generated proposals to user devices and providing real-time notifications. 【0775】 "Update methods" refer to the process of collecting user reactions and feedback and adaptively updating the learning model. 【0776】 A "machine learning algorithm" is a type of artificial intelligence technique used to generate appropriate suggestions for users. 【0777】 This invention is an information system for raising environmental awareness among residents in smart cities, and is implemented based on the respective roles of the server, terminal, and user. 【0778】 The server is equipped with an information gathering mechanism for comprehensively acquiring environmental data, and periodically collects climate-related and energy consumption-related information from external data sources. The collected data is purified by an information processing mechanism, which fills in missing data and removes outliers. This enables highly accurate data analysis in real time. 【0779】 Furthermore, the server uses a generative AI model to combine user behavior data and environmental data to create environmentally conscious behavioral suggestions. The suggestion generation method utilizes sentiment analysis functionality to reflect the user's emotional state and generate optimal suggestions. In this process, reinforcement learning algorithms are used to improve the model so that it can provide suggestions that are more suitable for the user. 【0780】 The device uses its camera and microphone to analyze the user's emotions in real time. It performs on-device emotion recognition using technologies such as the Emotion API and TensorFlow Lite. The analyzed emotion data is combined with environment suggestions received from the server to provide content tailored to the user's psychological state. 【0781】 Users receive notifications from their devices and choose actions based on the suggested actions. User feedback is sent to the server via the device, which continuously improves the accuracy of the suggestions. By providing personalized suggestions, residents can naturally choose actions in their daily lives that raise environmental awareness. 【0782】 For example, on a sunny day, a suggestion might be presented such as, "Today is a perfect day for going outside, so we recommend a walk to a nature observation spot." Another example of a prompt might be, "Based on today's weather and your mood, what are some recommended places to visit while being mindful of the environment?" Through such specific suggestions, residents can consciously choose environmentally friendly actions. 【0783】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0784】 Step 1: 【0785】 The server periodically collects environmental data from external data sources such as climate and energy consumption. Input is raw data from multiple APIs, and output is an integrated environmental dataset. Once data is acquired, it is stored in a database on the server. 【0786】 Step 2: 【0787】 The server cleanses and analyzes the collected environmental data. The input is the raw data obtained in step 1, and the output is a clean dataset. Specifically, it uses Pandas to fill in missing values, remove outliers, and convert the data into an analyzable format. 【0788】 Step 3: 【0789】 The server generates environmentally conscious suggestions using a generative AI model. The input consists of a clean dataset and data on the user's past behavior, while the output is specific action suggestions. The generative AI model uses a reinforcement learning algorithm to generate suggestions that lead to the user's optimal action. 【0790】 Step 4: 【0791】 The device uses its camera and microphone to acquire and analyze user emotion data. Input is real-time audio and image data, and output is the user's emotional state. Emotion analysis is performed on-device using the Emotion API and TensorFlow Lite. 【0792】 Step 5: 【0793】 The server optimizes suggestions based on the user's emotional state. The input is the suggestions generated in step 3 and the emotional state obtained in step 4, and the output is an action suggestion optimized for the emotional state. The suggestion generation means considers the emotional analysis data and customizes the suggestions. 【0794】 Step 6: 【0795】 The device communicates the final action suggestion to the user as a notification. The input is an optimized suggestion, and the output is a notification presented to the user visually or audibly. Information is conveyed to the user through the device screen or audio output. 【0796】 Step 7: 【0797】 The user receives suggestions and chooses an action. The input is a notification from the device, and the output is the user's action choice. The user selects what they believe to be the best option from the presented choices and puts it into action. 【0798】 Step 8: 【0799】 The device acquires user feedback and sends it to the server. Input is user feedback on their actions and satisfaction levels, while output is training data sent to the server. Feedback is collected through an in-app interface. 【0800】 Step 9: 【0801】 The server updates the AI ​​model using user feedback. The input is the feedback data obtained in step 8, and the output is the updated AI model. The server continuously updates the model based on reinforcement learning to improve the accuracy of future suggestions. 【0802】 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. 【0803】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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. 【0810】 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." 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 The following is further disclosed regarding the embodiments described above. 【0824】 (Claim 1) 【0825】 Data collection means for collecting environmental data, 【0826】 Data processing means for cleansing and analyzing collected data, 【0827】 A proposal generation method that provides users with options to minimize environmental impact, 【0828】 A notification method for notifying the user terminal of the proposal, 【0829】 A means of collecting user feedback and updating the model, 【0830】 A system that includes this. 【0831】 (Claim 2) 【0832】 The system according to claim 1, wherein the data collection means includes means for acquiring weather information and energy consumption information. 【0833】 (Claim 3) 【0834】 The system according to claim 1, wherein the proposal generation means includes means for generating the optimal choice for the user using a reinforcement learning algorithm. 【0835】 "Example 1" 【0836】 (Claim 1) 【0837】 Means for acquiring environmental information, 【0838】 Information processing means for cleansing and processing acquired information, 【0839】 A means for generating options to propose to users options that reduce environmental impact, 【0840】 A communication means for notifying the user terminal of the generated proposal, 【0841】 A means of collecting user feedback and updating the artificial intelligence model for retraining, 【0842】 A system that includes this. 【0843】 (Claim 2) 【0844】 The system according to claim 1, wherein the information acquisition means includes a function for acquiring weather data and energy usage data. 【0845】 (Claim 3) 【0846】 The system according to claim 1, wherein the option generation means includes a function that uses a reinforcement learning algorithm to propose the optimal option to the user. 【0847】 "Application Example 1" 【0848】 (Claim 1) 【0849】 Information gathering methods for collecting environmental information, 【0850】 Information processing means for cleansing and analyzing collected information, 【0851】 A means for generating options that provide choices that reduce the impact on the natural environment in daily life activities, 【0852】 A notification method that notifies a mobile device of the generated options, 【0853】 A means for collecting responses obtained through mobile devices and learning to update a predictive model, 【0854】 A system that includes this. 【0855】 (Claim 2) 【0856】 The system according to claim 1, wherein the information gathering means includes means for acquiring weather conditions and energy consumption conditions. 【0857】 (Claim 3) 【0858】 The system according to claim 1, wherein the option generation means includes means for generating the optimal option for the user using a reinforcement learning method. 【0859】 "Example 2 of combining an emotion engine" 【0860】 (Claim 1) 【0861】 Data collection means for collecting environmental data, 【0862】 A data processing means for cleansing and analyzing the collected data, 【0863】 A means of recognizing the emotional state of a user, 【0864】 A proposal generation method that uses a generative AI model to provide users with choices that minimize environmental impact and are adapted to their emotional state, 【0865】 A notification means for notifying user information devices of the proposal, 【0866】 A learning method for collecting user feedback and updating the learning model, 【0867】 A system that includes this. 【0868】 (Claim 2) 【0869】 The system according to claim 1, wherein the data collection means includes means for acquiring weather information and energy consumption information. 【0870】 (Claim 3) 【0871】 The system according to claim 1, wherein the proposal generation means includes means for generating the optimal choice for the user using a reinforcement learning algorithm. 【0872】 "Application example 2 when combining with an emotional engine" 【0873】 (Claim 1) 【0874】 Information gathering means for comprehensively acquiring environmental data, 【0875】 Information processing means for purifying and analyzing acquired information, 【0876】 A suggestion generation means that generates suggestions based on the user's emotional state using an emotion analysis function, 【0877】 A communication means for transmitting the generated proposal to the user's device, 【0878】 A means of updating the learning model by collecting user feedback and adaptively updating it, 【0879】 Information systems including 【0880】 (Claim 2) 【0881】 The information system according to claim 1, wherein the information gathering means includes means for receiving climate-related information and energy consumption-related information. 【0882】 (Claim 3) 【0883】 The information system according to claim 1, wherein the suggestion generation means includes means for generating appropriate suggestions for the user using a machine learning algorithm. [Explanation of symbols] 【0884】 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

[Claim 1] Data collection means for collecting environmental data, Data processing means for cleansing and analyzing collected data, A proposal generation method that provides users with options to minimize environmental impact, A notification method for notifying the user terminal of the proposal, A means of collecting user feedback and updating the model, A system that includes this. [Claim 2] The system according to claim 1, wherein the data collection means includes means for acquiring weather information and energy consumption information. [Claim 3] The system according to claim 1, wherein the proposal generation means includes means for generating the optimal choice for the user using a reinforcement learning algorithm.

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