System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining real-time weather data; means for calculating a global warming risk index by comparing with historical weather data; means for displaying the calculated global warming risk index on a user device; means for collecting daily behavior data of a user; means for analyzing behavior of the user based on the collected data and generating feedback; means for notifying the user device of the generated feedback; and means for providing a reward for eco-friendly behavior of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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 Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, the effects of global warming are becoming more serious, and countermeasures are urgently needed. However, many people have few opportunities to experience the effects of global warming in their daily lives, and lack the motivation to promote specific eco-friendly behavior. Furthermore, the lack of concrete feedback or rewards for engaging in eco-friendly behavior makes it difficult to achieve sustainable behavioral change. The present invention is provided to solve these problems. [Means for solving the problem]

[0005] This invention provides a means for acquiring weather data in real time and calculating a global warming risk index by comparing it with past weather data. Then, by using a means for displaying this index on a user's device, the user can feel the impact of global warming in real time. It also includes a means for collecting data on the user's daily behavior, analyzes the user's behavior based on the collected data, and generates feedback. This allows the user to understand the specific impact of their eco-friendly behavior. Furthermore, by providing a means for rewarding users for their eco-friendly behavior, a system is constructed that promotes sustainable behavioral change.

[0006] "Real-time" means processing and providing information in real time.

[0007] "Weather data" refers to information about atmospheric conditions and weather, specifically including temperature, humidity, precipitation, wind speed, and the like.

[0008] "Historical weather data" refers to historical information of previously recorded weather data, including weather observation records over an extended period of time.

[0009] The Global Warming Risk Index is a quantitative indicator of the progress and impact of global warming, and is usually calculated by comparing current weather data with past average weather data.

[0010] A "user device" refers to an electronic device used by a user, such as a computer or smartphone, that is capable of acquiring and displaying information.

[0011] "User's daily behavior data" refers to data relating to the behavior of the user in daily life, including, for example, means of transportation, energy consumption, and purchase history.

[0012] "Means of collection" refers to the technology and methods used to obtain and store specific data or information.

[0013] "Analysis" refers to the process of evaluating and analyzing collected data and extracting useful information from it.

[0014] "Feedback" refers to information such as ratings and advice provided based on collected and analyzed data, which is used to improve or encourage user behavior.

[0015] The term "means for providing rewards" refers to a method or system for providing some kind of benefit or benefit to a user as an incentive.

[0016] "Eco-friendly" refers to actions and products that are environmentally friendly and are kind to the natural environment. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a system that uses real-time weather data to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[0039] Obtaining and displaying the Global Warming Risk Index

[0040] server:

[0041] The server retrieves real-time weather data from weather information services via the Internet.

[0042] The acquired data will be stored in a database for comparison with weather data from the past 30 years.

[0043] The server compares past average temperature data with current data and calculates the Global Warming Risk Index, which is a numerical representation of the progress and impact of global warming.

[0044] The calculation results are stored in a database that is updated continuously.

[0045] Device:

[0046] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[0047] The device will then display the obtained index to the user, for example displaying a specific message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0048] User Data Collection and Feedback

[0049] Device:

[0050] Users record their daily eco-friendly actions in the app, for example, by entering "I cycled to work today."

[0051] In addition, user behavior data is automatically collected in conjunction with smart devices (e.g., smartwatches and energy consumption meters).

[0052] server:

[0053] The server receives and analyzes the collected data on the user's daily activities.

[0054] Based on the collected data, the eco-friendly actions taken by the user are recorded and the data is compiled for one week.

[0055] Based on the results, the system evaluates the user's eco-friendly behavior and generates a feedback message, such as "This week you reduced your carbon footprint by 35% more than usual."

[0056] Device:

[0057] The server sends feedback messages to the user, allowing the user to understand the specific effects of their actions in real time.

[0058] Implementing a reward system

[0059] server:

[0060] We will implement a system that awards and accumulates points for users' eco-friendly actions.

[0061] The server sets rewards for users when they accumulate a certain number of points, for example, 100 points, and offers discount coupons for eco-friendly products.

[0062] Device:

[0063] Sending a notification to the user that they have earned a reward, for example, "You have earned 100 points! New rewards are available!"

[0064] Users can check the reward details and select and use them through the app.

[0065] Specific examples

[0066] 1. Obtaining and displaying the Global Warming Risk Index:

[0067] Every day, the server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[0068] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[0069] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0070] 2. User Data Collection and Feedback:

[0071] The user records in the app, "I went on an eco-walk today."

[0072] The device sends the data to the server, which analyzes one week's worth of data.

[0073] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[0074] A feedback message is sent to the user's terminal.

[0075] 3. Implementing a reward system:

[0076] The server accumulates points for users' eco-friendly behavior.

[0077] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[0078] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[0079] Users select and redeem rewards through the app.

[0080] The above is an embodiment of the present invention. This system allows users to feel the effects of global warming in real time and incorporate eco-friendly behavior into their daily lives.

[0081] The processing flow will be explained below.

[0082] Step 1: Obtaining weather data

[0083] server:

[0084] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[0085] Save the retrieved data in the database.

[0086] Step 2: Compare with historical data

[0087] server:

[0088] The server retrieves the latest stored weather data and weather data from the past 30 years from the database.

[0089] Calculate the average temperature over the past 30 years.

[0090] Step 3: Calculate the Global Warming Risk Index

[0091] server:

[0092] The latest weather data is compared with historical average temperature data to calculate the Global Warming Risk Index, which indicates the percentage of current temperatures above the historical average.

[0093] The calculation results are saved in the database again.

[0094] Step 4: Displaying the Crisis Index

[0095] Device:

[0096] The user launches the app.

[0097] The app sends a request to the server to get the latest Global Warming Risk Index.

[0098] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[0099] Step 5: Collect user data

[0100] User:

[0101] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[0102] The smart device and app work together to automatically collect daily behavior data (location information, number of steps, energy consumption, etc.).

[0103] Step 6: Sending data

[0104] Device:

[0105] The collected user data is sent from the device to a server.

[0106] Step 7: Data analysis

[0107] server:

[0108] The server analyzes the collected data and evaluates the user's eco-friendly behavior.

[0109] The data for one week is compiled and the effect of eco-friendly behavior is calculated (e.g., reducing carbon footprint by 35% more than usual).

[0110] Step 8: Feedback Generation

[0111] server:

[0112] The server generates user feedback based on the analysis (e.g., "You reduced your carbon footprint by 35% this week").

[0113] Step 9: Notification of feedback

[0114] Device:

[0115] The feedback message sent from the server is displayed on the terminal.

[0116] The user can review the feedback.

[0117] Step 10: Awarding reward points

[0118] server:

[0119] The server awards points to users for their eco-friendly behavior.

[0120] The accumulated points are stored in a database.

[0121] Step 11: Set up and notify rewards

[0122] server:

[0123] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[0124] Device:

[0125] The device notifies the user that a reward is available (e.g., "You've collected 100 points! New rewards are available.").

[0126] Step 12: Use your rewards

[0127] User:

[0128] Users can check the reward details and select and use them through the app.

[0129] Example 1

[0130] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0131] In modern society, there is a lack of concrete mechanisms to grasp the impact of global warming in real time and to encourage people to be eco-friendly. There is also a need for a system that allows users to instantly recognize the extent to which their daily actions contribute to the environment and receive feedback and appropriate rewards for their actions.

[0132] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0133] In this invention, the server includes means for acquiring environmental information in real time, means for calculating a global warming impact index by comparing it with past environmental information, means for displaying the calculated global warming impact index on a user interface, means for collecting end-user daily behavior data, means for analyzing end-user behavior based on the collected data and generating feedback, means for notifying the user interface of the generated feedback, and means for providing rewards to end-users for their environmental protection behavior, thereby enabling users to recognize the impact of global warming in real time, immediately understand the effects of their own eco-friendly behavior, and receive appropriate feedback and rewards.

[0134] "Real-time" refers to data and information being processed and displayed immediately at the moment it is acquired or updated.

[0135] "Environmental information" refers to data related to the natural environment, such as temperature, humidity, precipitation, and wind speed.

[0136] The "global warming impact index" refers to an indicator that numerically shows the progress and impact of global warming based on past and present environmental data.

[0137] "User interface" refers to the display screen and operating means used to exchange information and instructions between the user and the system.

[0138] "End User" refers to the final user who directly uses a system or application.

[0139] "Daily Behavioral Data" refers to data relating to the End User's daily activities and habits, such as commuting method and energy consumption.

[0140] "Feedback" refers to information provided by a system that evaluates and comments on a user's actions, allowing the user to understand the results and impact of those actions.

[0141] "Rewards" refers to rewards or incentives provided to users for specific actions or achievements, including, for example, points or discount coupons.

[0142] This invention is a system that uses real-time environmental information to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[0143] Obtaining and displaying the global warming impact index

[0144] server:

[0145] The server obtains real-time environmental information from weather information providers, such as online services like WeatherAPI and OpenWeatherMap.

[0146] The server stores the acquired environmental information in a database, typically a relational database such as MySQL or PostgreSQL.

[0147] The server compares data from the past 30 years with current data and calculates a global warming impact index using Python's NumPy library.

[0148] The calculated global warming impact index is stored in a database and updated as needed.

[0149] Device:

[0150] When a user launches the app, the device sends a request to the server to obtain the latest global warming impact index, using a RESTful API.

[0151] The device displays the obtained index to the user. For example, a notification such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years" is displayed. React Native is used for the display.

[0152] User Data Collection and Feedback

[0153] Device:

[0154] Users record their daily eco-friendly actions within the app, such as "I cycled to work today."

[0155] The device connects to smart devices (e.g., smartwatches and energy consumption meters) and automatically collects user behavior data. This connection uses the Google Fit API and HealthKit.

[0156] server:

[0157] The server receives user behavior data sent from the device and stores it in a database. The received data is analyzed and aggregated for one week. This aggregation is performed using Python libraries such as Pandas and Scikit-learn.

[0158] The server generates a feedback message based on the analysis results, such as "This week you reduced your carbon footprint by 35% more than usual."

[0159] Device:

[0160] Feedback messages sent from the server are sent to the device. Firebase Cloud Messaging is used for notifications, allowing users to understand the effects of their actions in real time.

[0161] Implementing a reward system

[0162] server:

[0163] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management.

[0164] The server sets rewards when a user accumulates a certain number of points, and provides discount coupons for eco-friendly products when the user accumulates 100 points, for example.

[0165] Device:

[0166] The device will then notify the user that they have earned a reward. They will receive a notification saying, "You have earned 100 points. New rewards are available."

[0167] Users can check reward details and use the rewards they select through the app. React Native is also used to display and operate reward information.

[0168] Specific examples

[0169] 1. Obtaining and displaying the Global Warming Impact Index:

[0170] Every day, the server obtains environmental information for Tokyo from a weather information service and stores the new data in a database.

[0171] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[0172] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0173] 2. User Data Collection and Feedback:

[0174] The user records in the app, "I went on an eco-walk today."

[0175] The device sends the data to the server, which analyzes one week's worth of data.

[0176] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[0177] A feedback message is sent to the user's terminal.

[0178] 3. Implementing a reward system:

[0179] The server accumulates points for users' eco-friendly behavior.

[0180] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[0181] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[0182] Users select and redeem rewards through the app.

[0183] Prompt Sentence Examples

[0184] "Write a Python program that compares weather data from the past 30 years with current data and calculates the progress of global warming."

[0185] By inputting this prompt into a generative AI model, the corresponding Python code is generated.

[0186] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0187] Step 1:

[0188] server:

[0189] Every day, the server retrieves the latest environmental information from the environmental information service via the network. Specifically, it uses WeatherAPI or OpenWeatherMap to send an HTTP request. The retrieved data is in JSON format and includes information such as temperature, humidity, precipitation, and wind speed. The input is geographic location information and an API key, and the output is the latest environmental information data. This data is parsed and stored in a database.

[0190] Step 2:

[0191] server:

[0192] The server retrieves stored environmental information and environmental data for the past 30 years. The server uses MySQL or PostgreSQL to execute SQL queries to retrieve historical temperature data. The input is an SQL query for temperature data for the past 30 years, and the output is the retrieved historical temperature data. This data is processed using the NumPy library to calculate the average historical temperature.

[0193] Step 3:

[0194] server:

[0195] The acquired current environmental information is compared with historical average temperature data to calculate a global warming impact index. This calculation uses the NumPy library and shows the difference between the historical average temperature and the current temperature as a percentage. The input is the current temperature data and the historical average temperature data, and the output is the calculated global warming impact index. This index is saved in a database.

[0196] Step 4:

[0197] Device:

[0198] When a user launches the app, the device sends an HTTP GET request to the server to retrieve the latest global warming impact index. The input is the user request, and the output is the latest global warming impact index. The device parses this and displays it in an easy-to-understand format for the user. Specifically, it uses React Native to display a message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0199] Step 5:

[0200] User:

[0201] Users record their daily eco-friendly actions within the app. For example, they can enter, "I cycled to work today." If a smartwatch or energy consumption meter is connected, the device automatically collects data using the Google Fit API or HealthKit. The input is the user's behavioral data or data from the smart device, and the output is the data recorded within the app.

[0202] Step 6:

[0203] Device:

[0204] The device sends the collected user behavior data to the server. The input is the collected behavior data, and the output is an HTTP POST request to the server. The server receives this and stores it in a database.

[0205] Step 7:

[0206] server:

[0207] The server analyzes the received behavioral data and aggregates the data for one week. The data is aggregated using Python's Pandas library. The input is the user's behavioral data for one week, and the output is the aggregated results. Based on this result, the server evaluates the user's eco-friendly behavior and generates a feedback message.

[0208] Step 8:

[0209] Device:

[0210] The generated feedback message is sent to the user's device using Firebase Cloud Messaging. The input is the feedback message sent from the server, and the output is the notification sent to the user's device. The user can understand the effect of their actions in real time.

[0211] Step 9:

[0212] server:

[0213] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management. The input is user behavior data and point calculation logic, and the output is the accumulated points. When a certain number of points is reached, a reward is set, such as a discount coupon for an eco-friendly product.

[0214] Step 10:

[0215] Device:

[0216] The device will send a notification to the user that they have earned a reward. The notification will say, "You have earned 100 points. A new reward is available." The input is the reward notification sent from the server, and the output is the notification to the device. The user can check the reward details through the app and use the selected reward.

[0217] (Application example 1)

[0218] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0219] Currently, there are systems that detect the effects of global warming in real time and promote eco-friendly behavior based on that information, but there are not enough ways for users to specifically incorporate these systems into their lives or to immediately feel the effects. As a result, there is a problem that users' environmental awareness and actions are not aligned. There is also a lack of ways to clearly show the extent to which purchasing behavior in a virtual environment contributes to the environment.

[0220] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0221] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting users' daily behavior data, means for analyzing users' behavior based on the collected data and generating feedback, means for notifying the user device of the generated feedback, means for providing rewards for users' environmental protection behavior, and means for encouraging users to purchase products in the virtual environment using the global warming risk index. This allows users to grasp the effects of global warming in real time, making it easier for them to immediately understand the impact of their actions on the environment, and also encouraging them to purchase products in the virtual environment.

[0222] The "means for obtaining weather data in real time" refers to a means for obtaining the latest weather data from a weather information service via the Internet.

[0223] The "means of calculating the global warming risk index by comparing it with past weather data" is a means of comparing the latest weather data with weather data from the past 30 years, and as a result, expressing the degree of progress of global warming in numerical terms.

[0224] The "means for displaying the calculated global warming risk index on a user device" refers to a means for visually displaying the calculated index on a device used by a user.

[0225] The "means for collecting data on the user's daily activities" refers to a means for recording or automatically acquiring the eco-friendly activities that the user performs on a daily basis.

[0226] "Means for analyzing user behavior based on collected data and generating feedback" refers to means for analyzing collected user data, evaluating user behavior based on the analysis results, and generating specific feedback.

[0227] The "means for notifying the user device of the generated feedback" refers to a means for notifying the user device of the content of the generated feedback.

[0228] The "means for providing rewards for users' environmental protection actions" refers to a means for giving rewards such as points or discount coupons to users for eco-friendly actions they take.

[0229] "Means for promoting product purchasing behavior of users within a virtual environment using the global warming risk index" refers to means for motivating users to purchase eco-friendly products within a virtual environment using the calculated global warming risk index.

[0230] This invention is a system that detects the effects of global warming in real time and encourages users to behave eco-friendly. It mainly consists of three elements: a server, a terminal, and a user. The specific operation and technical details of this system are as follows:

[0231] Obtaining and displaying the Global Warming Risk Index

[0232] server:

[0233] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it accesses a weather information API to obtain current temperature data. The obtained data is stored in a database and compared with weather data from the past 30 years. The results of this comparison are used to calculate a global warming risk index. This index quantifies the difference between past average temperature data and current data, and quantitatively indicates the progress of global warming. The calculated index is updated in the database as needed.

[0234] Device:

[0235] When a user launches the application, the device sends a request to the server to retrieve the latest global warming risk index. The device then displays the index and provides a specific message to the user, such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years."

[0236] User Data Collection and Feedback

[0237] Device:

[0238] Users record their daily eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." It is also possible to link the app with smart devices such as smartwatches and energy consumption meters to automatically collect user behavior data.

[0239] server:

[0240] The server receives and analyzes user behavior data sent from the device. Based on the collected data, it evaluates the user's behavior over the course of a week and generates a feedback message. For example, it could evaluate the user's behavior by saying, "This week, you reduced your carbon footprint by 35% more than usual."

[0241] Device:

[0242] The server notifies the user of the feedback messages sent from the server, allowing the user to grasp the specific effects of their actions in real time.

[0243] Implementing a reward system

[0244] server:

[0245] The server will also implement a points system that rewards users for their eco-friendly behavior. When a certain number of points are accumulated, specific rewards can be set, such as discount coupons for eco-friendly products.

[0246] Device:

[0247] The device will notify the user when they have accumulated points and provide information to enable them to view, select, and use reward details. For example, a notification such as "You have accumulated 100 points. New rewards are available."

[0248] Application in virtual stores

[0249] server:

[0250] The server uses the calculated global warming risk index to encourage users to purchase eco-friendly products in the virtual environment. Based on this index, the server specifically indicates how much the product selected by the user contributes to preventing global warming.

[0251] Device:

[0252] When users purchase eco-friendly products displayed in the virtual store, the device compares the impact with real-time weather data. For example, it provides a message such as, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years, but by choosing this water bottle, you can reduce CO2 emissions by approximately 20 kg over the course of a year."

[0253] Example prompts for generative AI models

[0254] An example prompt is:

[0255] Obtain real-time weather data for Tokyo and compare it with the past 30 years to calculate a global warming risk index. Based on the obtained index, provide a message that informs users about the impact of purchasing eco-friendly products. For example, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years. By choosing a reusable water bottle, you can reduce CO2 emissions by approximately 20 kg per year."

[0256] Overall, the system allows users to feel the effects of global warming in real time and proactively incorporate eco-friendly behaviors into their lives. Product selection within the virtual environment will also be further encouraged by clearly showing the tangible contribution to environmental conservation.

[0257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0258] Step 1:

[0259] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it sends an API request to obtain current temperature data. At this time, the server receives the response data from the weather information service and parses it in a format such as JSON. The input is the response data from the weather information API, and the output is the parsed current temperature data.

[0260] Step 2:

[0261] The server retrieves weather data from the past 30 years and the latest weather data from the database, compares them, and calculates the Global Warming Crisis Index. Specifically, it calculates the temperature increase rate using the latest temperature data and historical average temperature data. In this case, the server receives current temperature data and data from the past 30 years as arguments, and outputs the Global Warming Crisis Index as the calculation result. The input is the latest temperature data and historical temperature data, and the output is the Global Warming Crisis Index.

[0262] Step 3:

[0263] The server stores the calculated Global Warming Crisis Index in a database and updates it as needed. Specifically, it inserts or updates the Global Warming Crisis Index into the database as a new record. The input is the calculation result of the Global Warming Crisis Index, and the output is the updated state of the database.

[0264] Step 4:

[0265] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index. Specifically, it sends an HTTP request to obtain the latest Global Warming Risk Index from the server. The input is the HTTP request, and the output is the obtained Global Warming Risk Index.

[0266] Step 5:

[0267] The device displays the acquired Global Warming Risk Index to the user. Specifically, it updates the UI component that visually displays the acquired value. For example, it displays a message on the screen saying, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years." The input is the acquired Global Warming Risk Index, and the output is the displayed message.

[0268] Step 6:

[0269] Users record their daily eco-friendly actions in the app. Specifically, users enter their actions into a form within the app, and the app saves the data. The input is the action data entered by the user, and the output is the saved action data.

[0270] Step 7:

[0271] The terminal works in conjunction with smart devices to automatically collect user behavioral data. Specifically, it acquires data from devices such as smartwatches and energy consumption meters and sends it to the app. The input is behavioral data from the smart device, and the output is behavioral data recorded in the app.

[0272] Step 8:

[0273] The server receives and analyzes the collected user behavior data. Specifically, it retrieves the behavior data from the database and performs statistical analysis to evaluate the user's eco-friendly behavior. The input is the behavior data, and the output is the evaluation result.

[0274] Step 9:

[0275] The server generates a feedback message based on the analysis results. Specifically, it uses a generative AI model to generate a feedback message for the user's actions. The input is the analysis results, and the output is the feedback message.

[0276] Step 10:

[0277] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the feedback message, and the output is the message sent to the user's terminal.

[0278] Step 11:

[0279] The terminal notifies the user of the feedback message by displaying the message to the user using a notification function. The input is the received feedback message, and the output is the displayed notification.

[0280] Step 12:

[0281] The server implements a system that awards points to users for their eco-friendly behavior. Specifically, it calculates points based on user behavior data and stores them in a database. The input is the user behavior data, and the output is the awarded points.

[0282] Step 13:

[0283] The server sets a reward for users who have accumulated 100 points and registers it in the database. Specifically, it sets a reward such as a discount coupon for eco-friendly products for users who have reached a certain number of points. The input is the user's accumulated points, and the output is the set reward.

[0284] Step 14:

[0285] The terminal sends a notification to the user that a reward has been earned. Specifically, it notifies the user with a message that a reward is available. The input is the set reward, and the output is the sent notification.

[0286] Step 15:

[0287] Within the virtual store system, the terminal presents the user with the impact of purchasing a product based on the Global Warming Risk Index. Specifically, when a user purchases a reusable product, the terminal displays in real time the extent to which that purchase will contribute to preventing global warming. The input is the Global Warming Risk Index and product information, and the output is the impact information presented to the user.

[0288] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0289] This system calculates the impact of global warming based on real-time and historical weather data and displays the results on a user's device. It also collects and analyzes the user's daily behavior and emotional state, generating feedback and rewarding the user for their eco-friendly behavior. The specific operation of each component and their interaction are described below.

[0290] Obtaining and displaying the Global Warming Risk Index

[0291] server:

[0292] The server periodically obtains real-time weather data from the weather information service API via the Internet.

[0293] The acquired data is stored in a database.

[0294] The server compares weather data from the past 30 years with the latest data to calculate the global warming risk index.

[0295] The calculated results are stored in a database.

[0296] Device:

[0297] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[0298] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[0299] User Data Collection and Feedback

[0300] Device:

[0301] Users record eco-friendly actions in the app, for example, by typing, "I cycled to work today."

[0302] By linking the smart device with the app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[0303] Additionally, an emotion engine is used to collect the user's emotional state (e.g., happy, sad, excited, etc.), which includes voice analysis, text input analysis, and camera-based facial expression analysis.

[0304] server:

[0305] The collected user data and emotion data are sent to the server.

[0306] The server analyzes the received data and evaluates the user's eco-friendly behavior, taking into account emotional data, such as how the user felt about a particular behavior.

[0307] Generate feedback messages based on the analysis, including personalized feedback based on emotional data (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive mood you experience while cycling to work.").

[0308] A feedback message is sent to the user's terminal.

[0309] Device:

[0310] Users can check the feedback messages in the app.

[0311] Implementing a reward system

[0312] server:

[0313] The server implements a system that gives and accumulates points for users' eco-friendly actions.

[0314] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[0315] Device:

[0316] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[0317] Users can check the reward details and select and use them through the app.

[0318] Specific examples

[0319] 1. Obtaining and displaying the Global Warming Risk Index

[0320] The server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[0321] The server compares temperature data from the past 30 years with the latest data and calculates that this week's average temperature is 5% higher.

[0322] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0323] 2. User Data Collection and Feedback

[0324] The user records in the app, "I went on an eco-walk today," and the device sends that data to the server.

[0325] Furthermore, the emotion engine analyzes the user's facial expression to detect the emotion of "joy" and transmits this to the server.

[0326] The server analyzes a week's worth of data and generates feedback such as, "This week you reduced your carbon footprint by 35% and felt more positive while cycling to work."

[0327] The feedback is displayed on the terminal for the user to review.

[0328] 3. Implementing a reward system

[0329] The server accumulates points for users' eco-friendly behavior.

[0330] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[0331] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[0332] Users can select and redeem rewards through the app.

[0333] The above is a concrete example of how the present invention can be implemented. This system allows users to feel the effects of global warming in real time, and by utilizing emotion data, users can more effectively incorporate eco-friendly behaviors into their daily lives.

[0334] The processing flow will be explained below.

[0335] Step 1: Obtaining weather data

[0336] server:

[0337] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[0338] The acquired data includes items such as temperature, humidity, and precipitation, and is stored in a database.

[0339] Step 2: Obtain and compare historical data

[0340] server:

[0341] The server retrieves weather data from the database for the past 30 years.

[0342] The latest weather data obtained is compared with past average temperature data to calculate the global warming risk index.

[0343] The calculated global warming risk index is stored in a database.

[0344] Step 3: Displaying the Crisis Index

[0345] Device:

[0346] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index.

[0347] The obtained global warming risk index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[0348] Step 4: Collect user data

[0349] User:

[0350] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[0351] By connecting the smart device and app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[0352] Step 5: Collecting sentiment data

[0353] Device:

[0354] The app's built-in emotion engine recognizes the user's emotional state through user input, the camera, and the microphone. For example, it can determine whether the user is having fun through voice analysis, or recognize emotions by analyzing facial expressions via the camera.

[0355] The collected emotion data is sent to the server.

[0356] Step 6: Sending data

[0357] Device:

[0358] The collected daily behavior data and emotion data are sent to a server.

[0359] Step 7: Data analysis and feedback generation

[0360] server:

[0361] The server analyzes the received user data and evaluates the impact of eco-friendly actions, while also considering emotional data to analyze how the user felt about each action.

[0362] Generate feedback messages based on the analysis results (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive feelings you get from cycling to work.").

[0363] A feedback message is sent to the user's terminal.

[0364] Step 8: Notification of feedback

[0365] Device:

[0366] The terminal notifies the user of the feedback message sent from the server.

[0367] Users can view feedback in the app.

[0368] Step 9: Reward Points Awarded

[0369] server:

[0370] The server awards points to users for their eco-friendly behavior.

[0371] When points reach a certain threshold, a reward (e.g., a discount coupon for an eco-friendly product) is set.

[0372] Step 10: Notification and Redemption of Rewards

[0373] Device:

[0374] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[0375] User:

[0376] Users can check the reward details and select and use them through the app.

[0377] The above is the processing flow in the embodiment of the invention. This flow allows users to feel the effects of global warming in real time and continuously take eco-friendly actions, including their emotions.

[0378] Example 2

[0379] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0380] Global warming has become a serious problem in modern society, and people need to feel its effects on a daily basis. Furthermore, there is a need for a method to visualize the environmental impact of individual actions and promote sustainable behavior. However, there is no comprehensive system available that collects real-time weather data, compares it with past data, collects and analyzes users' daily behavior data, and understands their emotional state. This makes the process of users accurately evaluating their eco-friendly behavior and receiving rewards complicated, making effective efforts difficult.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0382] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting data on the user's daily behavior, means for analyzing the user's behavior based on the collected data and emotional state data and generating feedback, means for notifying the user device of the generated feedback, and means for providing rewards for the user's eco-friendly behavior, thereby enabling the user to grasp the effects of global warming in real time, have their eco-friendly behavior accurately evaluated, and be encouraged to be more environmentally friendly through rewards.

[0383] "Weather data" is a general term for information related to weather conditions, such as temperature, humidity, precipitation, and wind speed.

[0384] "Real-time" refers to conditions and data that are immediately available at the present time.

[0385] "Historical weather data" includes records of information relating to observed weather conditions over a particular period of time.

[0386] The Global Warming Risk Index is an index that evaluates the impact of global warming by comparing past weather data with the latest weather data.

[0387] "User device" is a general term for electronic devices used by users to receive and display information, such as smartphones, tablets, and PCs.

[0388] The "daily behavior data" includes information related to the user's daily activities, such as data on transportation methods and lifestyle habits.

[0389] "Emotional state data" is data that indicates the user's emotional state, and includes the results of analyzing emotions such as joy, sadness, and excitement.

[0390] "Analysis" is the act of evaluating the characteristics and trends of collected data.

[0391] "Feedback" refers to advice and evaluation given to users based on collected and analyzed data.

[0392] "Eco-friendly behavior" is a general term for actions that are considered environmentally friendly, including recycling, reusing, saving electricity, and saving energy.

[0393] "Rewards" refers to incentives or benefits provided to users for specific actions or achievements.

[0394] "Via the Internet" refers to a method of sending and receiving data using the Internet.

[0395] "Weather information provision service" refers to a service or system that provides weather-related data.

[0396] "Smart devices" are terminal devices that can connect to the Internet, such as smartphones, smartwatches, and fitness trackers.

[0397] "Emotion engine" is a general term for algorithms and systems that analyze emotions from a user's voice, facial expressions, text, etc.

[0398] This system acquires weather data in real time, compares it with past weather data to calculate a global warming risk index, and collects and analyzes the user's daily behavior data and emotional state to generate feedback and provide rewards for eco-friendly behavior. The specific operation of each component and how they work together are explained below.

[0399] Obtaining and displaying the Global Warming Risk Index

[0400] Server behavior:

[0401] The server periodically obtains real-time weather data via the internet using the API of a weather information service. For example, a timer can be set to send a request to the API at midnight every day. The obtained weather data is stored in a database. The server then retrieves weather data from the database for the past 30 years, compares it with the latest data, and runs an algorithm to calculate the global warming risk index. The calculated index is then stored back in the database.

[0402] Terminal behavior:

[0403] When a user launches the app on their device, the device sends a request to the server to obtain the latest global warming risk index. The obtained index is displayed to the user. For example, the display might say, "This week's average temperature in Tokyo is 5% higher than the average over the past 30 years."

[0404] Collecting user data and generating feedback

[0405] Terminal behavior:

[0406] Users can record their eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." By linking their smart device with the app, daily activity data (location information, number of steps, energy consumption, etc.) is automatically collected. Furthermore, an emotion engine uses voice and camera data to analyze the user's emotional state (e.g., joy, sadness, excitement, etc.). This includes voice analysis, text input analysis, and facial expression analysis using a camera.

[0407] Server behavior:

[0408] The server receives the collected data and emotional state data sent from the device and analyzes them. A specific algorithm is used for the analysis to evaluate the user's eco-friendly behavior, taking their emotional state into account in the process. For example, the server may evaluate the user as having a positive mood while commuting by bicycle. Based on the analysis results, a personalized feedback message is generated. For example, a feedback message such as "You have reduced your carbon footprint by 35% this week. In addition, your positive mood while commuting by bicycle has been confirmed" is generated and sent to the user's device.

[0409] Terminal behavior:

[0410] Users can check feedback messages through the app.

[0411] Implementing a reward system

[0412] Server behavior:

[0413] The server implements a system that awards and accumulates points for users' eco-friendly behavior. For example, "10 points for commuting by bicycle." When a certain number of points are accumulated, for example, 100 points, the user is offered a discount coupon for an eco-friendly product.

[0414] Terminal behavior:

[0415] The device will send a notification to the user informing them that a reward is available. For example, a notification saying "You have accumulated 100 points. New rewards are available." The user can check the reward details through the app and select and use the desired reward.

[0416] The above is a specific embodiment for carrying out the present invention. This system allows users to understand the impact of global warming in real time, have their eco-friendly behavior evaluated, and encourage further environmentally friendly behavior through rewards.

[0417] Prompt Sentence Examples

[0418] "I went on an eco-walk today."

[0419] "This month's carbon footprint is lower than last month's."

[0420] "I'd like to use a discount coupon for a new eco-friendly product."

[0421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0422] Obtaining and displaying the Global Warming Risk Index

[0423] Step 1: Regularly acquire weather data

[0424] Server operation: The server periodically sets a timer and sends a request to the weather information service API. For example, the server calls the API every day at midnight.

[0425] Input: Timer event, API request

[0426] Output: Real-time weather data (e.g. temperature, humidity, precipitation)

[0427] What happens: The server authenticates using the API key and retrieves weather data for the specified region.

[0428] Step 2: Save your data

[0429] Server operation: Save the acquired weather data in a database.

[0430] Input: Weather data (e.g., JSON format)

[0431] Output: Weather data stored in a database

[0432] What it does: Adds a new record to the database, storing the date and time of acquisition and regional details.

[0433] Step 3: Calculate the Global Warming Risk Index

[0434] Server operation: Compares weather data from the past 30 years with the latest data and calculates the Global Warming Risk Index.

[0435] Input: Weather data from the past 30 years and the latest data obtained from the database

[0436] Output: Calculated Global Warming Risk Index

[0437] What it does: It runs an algorithm to calculate the rate of change in average temperature, for example, comparing the average temperature of the most recent year with the average temperature of the past 30 years and calculating the difference as a percentage.

[0438] Step 4: User retrieves and displays the index

[0439] What happens on the device: When a user launches the app, it sends a request to the server to get the latest Global Warming Risk Index.

[0440] Input: User action (launching the app)

[0441] Output: Obtained Global Warming Risk Index

[0442] Specific behavior: Parse the data sent as a response from the server and display it in the app interface. For example, it might say, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years."

[0443] Collecting user data and generating feedback

[0444] Step 1: Recording user behavior data

[0445] Device Action: The user types "I biked to work today" in the app.

[0446] Input: User action input (text format)

[0447] Output: Behavioral data stored in the app

[0448] Specific operation: Saves the entered text data in an internal database.

[0449] Step 2: Collecting emotion data

[0450] Device operation: The emotion engine analyzes audio data and camera footage to detect the user's emotional state.

[0451] Input: Audio data, camera footage

[0452] Output: Detected emotion data (e.g., happy, sad)

[0453] What it does: Implements voice and facial expression analysis algorithms to classify and detect emotional states, such as detecting joy from the user's voice tone.

[0454] Step 3: Send data to the server

[0455] Device operation: The collected user behavior data and emotion data are sent to the server as an HTTP request.

[0456] Input: User behavior data, emotion data

[0457] Output: Data sent to the server

[0458] What it does: Formats an HTTP request and sends it to the server with the required data as a payload.

[0459] Step 4: Data analysis and feedback generation

[0460] Server operation: The server analyzes the received data and evaluates the user's eco-friendly behavior.

[0461] Input: Submitted user behavior and emotion data

[0462] Output: Analysis results and feedback messages

[0463] What it does: Runs analytical algorithms and evaluates user behavior based on specific metrics. For example, calculates the carbon footprint reduction percentage over the week. Generates and customizes feedback messages. For example, "You've reduced your carbon footprint by 35% this week. What's more, cycling to work has confirmed your positive mood."

[0464] Step 5: View your feedback

[0465] Device Action: Display a feedback message in the user's app.

[0466] Input: Feedback message received from the server

[0467] Output: Feedback message displayed in the app

[0468] Specific behavior: Parse the feedback message and display it in the user interface. For example, display something like "This week's evaluation results are..."

[0469] Implementing a reward system

[0470] Step 1: Earn points for eco-friendly actions

[0471] Server operation: Executes the logic to award points to users for their daily eco-friendly actions.

[0472] Input: User behavior data and analysis results

[0473] Output: Points awarded

[0474] Specific operation: Points are calculated based on user behavior and stored in a database. For example, "10 points for each bicycle commute."

[0475] Step 2: Reward Settings and Notifications

[0476] Server operation: Monitors the point accumulation status and sets rewards for users who have accumulated a certain number of points.

[0477] Input: User's accumulated points

[0478] Output: The configured reward and notification message.

[0479] Specific behavior: When the user's points reach 100, provide a discount coupon for eco-friendly products. Generate and send a notification message to the user. Example: "You've accumulated 100 points. New rewards are available."

[0480] Step 3: Earn and use your rewards

[0481] Device operation: The user checks the reward details through the app and selects and uses the reward they want.

[0482] Input: User action (reward selection)

[0483] Output: Rewards used

[0484] Specific behavior: Display the reward details and select the reward you want. For example, use the reward by pressing the "Use eco product discount coupon" button.

[0485] (Application example 2)

[0486] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0487] While factories are required to optimize energy consumption and reduce their environmental impact, conventional systems have difficulty analyzing environmental impacts using real-time and historical weather data and suggesting specific eco-friendly actions to factory managers. Furthermore, there is no reward system for eco-friendly actions, which means that factory managers are not sufficiently motivated.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0489] In this invention, the server includes a means for acquiring weather data in real time, a means for calculating a global warming risk index by comparing it with past weather data, and a means for collecting and analyzing energy consumption data in the factory, thereby enabling optimization of energy consumption in the factory and reduction of environmental load.

[0490] "Means for obtaining weather data in real time" refers to a function for obtaining the latest weather data from a weather information service via the Internet.

[0491] "Means for calculating the global warming risk index by comparing with past weather data" is a function that quantifies the impact of global warming by comparing past weather data with current weather data.

[0492] The "means for displaying the calculated global warming crisis index on the user device" is a function for visually conveying the calculated global warming crisis index on the user's device.

[0493] The "means for collecting data on daily user behavior" is a function for collecting information on daily user behavior.

[0494] The "means for analyzing user behavior based on collected data and generating feedback" is a function that analyzes the user's daily behavior data and creates feedback based on the results.

[0495] The "means for notifying the user device of the generated feedback" is a function for notifying the user device of the generated feedback.

[0496] The "means for collecting and analyzing energy consumption data within the factory" is a function for collecting and analyzing data on energy use within the factory.

[0497] "Means of proposing eco-friendly actions to factory managers based on analysis results" is a function that suggests environmentally friendly actions to factory managers based on the analysis results of collected data.

[0498] The "means for providing rewards for eco-friendly behavior of factory managers" is a function that provides rewards when factory managers behave in an environmentally friendly manner.

[0499] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Each element functions using specific hardware and software. Below, we will explain how each element works together to realize the invention.

[0500] Server Features

[0501] The server implements the following methods:

[0502] 1. Means of obtaining weather data in real time: The server periodically obtains the latest weather data from weather information services via the Internet, thereby ensuring that the latest weather information is always available.

[0503] 2. Means for calculating the Global Warming Risk Index by comparing with past weather data: The server compares weather data from the past 30 years with current weather data and calculates the Global Warming Risk Index. This calculation uses a high-performance database and analytical algorithms.

[0504] 3. Collecting and analyzing energy consumption data within the factory: Energy consumption data is collected from sensors and smart devices installed within the factory and analyzed using machine learning models.

[0505] Device Features

[0506] The terminal acts as a user device and implements the following:

[0507] 1. A means for displaying the calculated Global Warming Risk Index on the user device: The Global Warming Risk Index obtained from the server is visually displayed to the user. Specifically, a message such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years" is displayed on the screen of a smartphone or tablet.

[0508] 2. Means of collecting user's daily behavior data: Automatically collect user's daily behavior data from smart devices, including location information, number of steps, energy consumption, etc.

[0509] 3. Means of notifying the user device of the generated feedback: The feedback generated by the server is notified to the user device, for example, by displaying a message such as "You reduced your carbon footprint by 35% this week. In addition, we have confirmed the positive feelings you get from commuting by bicycle."

[0510] User Involvement

[0511] Users provide their daily behavior data to the system. For example, by carrying a smartphone, location information and step counts are automatically collected. Furthermore, by practicing eco-friendly behavior, users can receive rewards from the system.

[0512] Specific examples

[0513] As a specific example of operation, we will explain the case where a factory manager uses an application called "Eco Manager for Factory Robots." Robots in the factory collect energy consumption data in real time and send it to a server. The server compares the acquired weather data with past data and calculates an environmental load index. Based on this, it provides feedback to the factory manager, such as "We recommend the following operations to reduce energy consumption by 10%."

[0514] Example prompt for a generative AI model:

[0515] Calculate the factory's environmental impact index based on weather and energy consumption data and suggest eco-friendly improvements.

[0516] Through these procedures, the system of the present invention can optimize energy consumption in factories and reduce environmental impact.

[0517] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0518] Step 1:

[0519] The server obtains real-time weather data from the weather information service's API via the Internet. It sends an API request as input and receives the latest weather data as output. This data is stored in the server's database. Specifically, it sends an HTTP request using an API key and receives weather data in JSON format as a response.

[0520] Step 2:

[0521] The server retrieves weather data from the past 30 years and compares it with real-time data to calculate the Global Warming Risk Index. It uses past and current weather data as input and obtains the Global Warming Risk Index as output. The server stores the calculation results in a database. Specifically, it sends a query to the historical weather database, compares it with current data to calculate the temperature difference, and then indexes the result.

[0522] Step 3:

[0523] The terminal periodically collects data on the user's daily activities from the smart device to automatically collect it. As input, it takes in sensor information and GPS data from the smart device. As output, it sends the collected daily activity data to the server. Specifically, the terminal periodically acquires data from the wearable device or smartphone and uploads it to the server.

[0524] Step 4:

[0525] The server analyzes the collected daily behavior data and evaluates the user's eco-friendly behavior. It uses the user's daily behavior data as input and generates behavior evaluation results and feedback messages as output. Specifically, it analyzes the data using a machine learning algorithm and scores eco-friendly behavior.

[0526] Step 5:

[0527] The server generates a feedback message based on the analysis results and notifies the user device. It uses the analysis results and a feedback template as input and generates a customized feedback message as output. Specific operations include generating a message such as "You reduced your carbon footprint by 35% this week" and sending a push notification to the user's smartphone.

[0528] Step 6:

[0529] The server collects energy consumption data within the factory and, based on the analysis results, suggests eco-friendly actions to factory managers. Energy consumption data and real-time weather data are used as input, and specific improvement suggestions are generated as output. For example, the server analyzes energy consumption data and generates a suggestion message such as, "To reduce energy consumption by 10%, we recommend the following actions."

[0530] Step 7:

[0531] The server implements a system that rewards factory managers for their eco-friendly behavior. It uses behavioral data and reward conditions as input and generates reward information as output. Specifically, it sets up a points system, sets rewards (e.g., discount coupons for eco-friendly products) when a certain number of points are accumulated, and sends a notification.

[0532] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0533] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0534] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0535] [Second embodiment]

[0536] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0537] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0538] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0539] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0540] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0541] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0542] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0543] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0544] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0545] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0546] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0547] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0548] This invention is a system that uses real-time weather data to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[0549] Obtaining and displaying the Global Warming Risk Index

[0550] server:

[0551] The server retrieves real-time weather data from weather information services via the Internet.

[0552] The acquired data will be stored in a database for comparison with weather data from the past 30 years.

[0553] The server compares past average temperature data with current data and calculates the Global Warming Risk Index, which is a numerical representation of the progress and impact of global warming.

[0554] The calculation results are stored in a database that is updated continuously.

[0555] Device:

[0556] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[0557] The device will then display the obtained index to the user, for example displaying a specific message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0558] User Data Collection and Feedback

[0559] Device:

[0560] Users record their daily eco-friendly actions in the app, for example, by entering "I cycled to work today."

[0561] In addition, user behavior data is automatically collected in conjunction with smart devices (e.g., smartwatches and energy consumption meters).

[0562] server:

[0563] The server receives and analyzes the collected data on the user's daily activities.

[0564] Based on the collected data, the eco-friendly actions taken by the user are recorded and the data is compiled for one week.

[0565] Based on the results, the system evaluates the user's eco-friendly behavior and generates a feedback message, such as "This week you reduced your carbon footprint by 35% more than usual."

[0566] Device:

[0567] The server sends feedback messages to the user, allowing the user to understand the specific effects of their actions in real time.

[0568] Implementing a reward system

[0569] server:

[0570] We will implement a system that awards and accumulates points for users' eco-friendly actions.

[0571] The server sets rewards for users when they accumulate a certain number of points, for example, 100 points, and offers discount coupons for eco-friendly products.

[0572] Device:

[0573] Sending a notification to the user that they have earned a reward, for example, "You have earned 100 points! New rewards are available!"

[0574] Users can check the reward details and select and use them through the app.

[0575] Specific examples

[0576] 1. Obtaining and displaying the Global Warming Risk Index:

[0577] Every day, the server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[0578] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[0579] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0580] 2. User Data Collection and Feedback:

[0581] The user records in the app, "I went on an eco-walk today."

[0582] The device sends the data to the server, which analyzes one week's worth of data.

[0583] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[0584] A feedback message is sent to the user's terminal.

[0585] 3. Implementing a reward system:

[0586] The server accumulates points for users' eco-friendly behavior.

[0587] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[0588] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[0589] Users select and redeem rewards through the app.

[0590] The above is an embodiment of the present invention. This system allows users to feel the effects of global warming in real time and incorporate eco-friendly behavior into their daily lives.

[0591] The processing flow will be explained below.

[0592] Step 1: Obtaining weather data

[0593] server:

[0594] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[0595] Save the retrieved data in the database.

[0596] Step 2: Compare with historical data

[0597] server:

[0598] The server retrieves the latest stored weather data and weather data from the past 30 years from the database.

[0599] Calculate the average temperature over the past 30 years.

[0600] Step 3: Calculate the Global Warming Risk Index

[0601] server:

[0602] The latest weather data is compared with historical average temperature data to calculate the Global Warming Risk Index, which indicates the percentage of current temperatures above the historical average.

[0603] The calculation results are saved in the database again.

[0604] Step 4: Displaying the Crisis Index

[0605] Device:

[0606] The user launches the app.

[0607] The app sends a request to the server to get the latest Global Warming Risk Index.

[0608] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[0609] Step 5: Collect user data

[0610] User:

[0611] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[0612] The smart device and app work together to automatically collect daily behavior data (location information, number of steps, energy consumption, etc.).

[0613] Step 6: Sending data

[0614] Device:

[0615] The collected user data is sent from the device to a server.

[0616] Step 7: Data analysis

[0617] server:

[0618] The server analyzes the collected data and evaluates the user's eco-friendly behavior.

[0619] The data for one week is compiled and the effect of eco-friendly behavior is calculated (e.g., reducing carbon footprint by 35% more than usual).

[0620] Step 8: Feedback Generation

[0621] server:

[0622] The server generates user feedback based on the analysis (e.g., "You reduced your carbon footprint by 35% this week").

[0623] Step 9: Notification of feedback

[0624] Device:

[0625] The feedback message sent from the server is displayed on the terminal.

[0626] The user can review the feedback.

[0627] Step 10: Awarding reward points

[0628] server:

[0629] The server awards points to users for their eco-friendly behavior.

[0630] The accumulated points are stored in a database.

[0631] Step 11: Set up and notify rewards

[0632] server:

[0633] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[0634] Device:

[0635] The device notifies the user that a reward is available (e.g., "You've collected 100 points! New rewards are available.").

[0636] Step 12: Use your rewards

[0637] User:

[0638] Users can check the reward details and select and use them through the app.

[0639] Example 1

[0640] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0641] In modern society, there is a lack of concrete mechanisms to grasp the impact of global warming in real time and to encourage people to be eco-friendly. There is also a need for a system that allows users to instantly recognize the extent to which their daily actions contribute to the environment and receive feedback and appropriate rewards for their actions.

[0642] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0643] In this invention, the server includes means for acquiring environmental information in real time, means for calculating a global warming impact index by comparing it with past environmental information, means for displaying the calculated global warming impact index on a user interface, means for collecting end-user daily behavior data, means for analyzing end-user behavior based on the collected data and generating feedback, means for notifying the user interface of the generated feedback, and means for providing rewards to end-users for their environmental protection behavior, thereby enabling users to recognize the impact of global warming in real time, immediately understand the effects of their own eco-friendly behavior, and receive appropriate feedback and rewards.

[0644] "Real-time" refers to data and information being processed and displayed immediately at the moment it is acquired or updated.

[0645] "Environmental information" refers to data related to the natural environment, such as temperature, humidity, precipitation, and wind speed.

[0646] The "global warming impact index" refers to an indicator that numerically shows the progress and impact of global warming based on past and present environmental data.

[0647] "User interface" refers to the display screen and operating means used to exchange information and instructions between the user and the system.

[0648] "End User" refers to the final user who directly uses a system or application.

[0649] "Daily Behavioral Data" refers to data relating to the End User's daily activities and habits, such as commuting method and energy consumption.

[0650] "Feedback" refers to information provided by a system that evaluates and comments on a user's actions, allowing the user to understand the results and impact of those actions.

[0651] "Rewards" refers to rewards or incentives provided to users for specific actions or achievements, including, for example, points or discount coupons.

[0652] This invention is a system that uses real-time environmental information to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[0653] Obtaining and displaying the global warming impact index

[0654] server:

[0655] The server obtains real-time environmental information from weather information providers, such as online services like WeatherAPI and OpenWeatherMap.

[0656] The server stores the acquired environmental information in a database, typically a relational database such as MySQL or PostgreSQL.

[0657] The server compares data from the past 30 years with current data and calculates a global warming impact index using Python's NumPy library.

[0658] The calculated global warming impact index is stored in a database and updated as needed.

[0659] Device:

[0660] When a user launches the app, the device sends a request to the server to obtain the latest global warming impact index, using a RESTful API.

[0661] The device displays the obtained index to the user. For example, a notification such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years" is displayed. React Native is used for the display.

[0662] User Data Collection and Feedback

[0663] Device:

[0664] Users record their daily eco-friendly actions within the app, such as "I cycled to work today."

[0665] The device connects to smart devices (e.g., smartwatches and energy consumption meters) and automatically collects user behavior data. This connection uses the Google Fit API and HealthKit.

[0666] server:

[0667] The server receives user behavior data sent from the device and stores it in a database. The received data is analyzed and aggregated for one week. This aggregation is performed using Python libraries such as Pandas and Scikit-learn.

[0668] The server generates a feedback message based on the analysis results, such as "This week you reduced your carbon footprint by 35% more than usual."

[0669] Device:

[0670] Feedback messages sent from the server are sent to the device. Firebase Cloud Messaging is used for notifications, allowing users to understand the effects of their actions in real time.

[0671] Implementing a reward system

[0672] server:

[0673] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management.

[0674] The server sets rewards when a user accumulates a certain number of points, and provides discount coupons for eco-friendly products when the user accumulates 100 points, for example.

[0675] Device:

[0676] The device will then notify the user that they have earned a reward. They will receive a notification saying, "You have earned 100 points. New rewards are available."

[0677] Users can check reward details and use the rewards they select through the app. React Native is also used to display and operate reward information.

[0678] Specific examples

[0679] 1. Obtaining and displaying the Global Warming Impact Index:

[0680] Every day, the server obtains environmental information for Tokyo from a weather information service and stores the new data in a database.

[0681] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[0682] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0683] 2. User Data Collection and Feedback:

[0684] The user records in the app, "I went on an eco-walk today."

[0685] The device sends the data to the server, which analyzes one week's worth of data.

[0686] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[0687] A feedback message is sent to the user's terminal.

[0688] 3. Implementing a reward system:

[0689] The server accumulates points for users' eco-friendly behavior.

[0690] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[0691] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[0692] Users select and redeem rewards through the app.

[0693] Prompt Sentence Examples

[0694] "Write a Python program that compares weather data from the past 30 years with current data and calculates the progress of global warming."

[0695] By inputting this prompt into a generative AI model, the corresponding Python code is generated.

[0696] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0697] Step 1:

[0698] server:

[0699] Every day, the server retrieves the latest environmental information from the environmental information service via the network. Specifically, it uses WeatherAPI or OpenWeatherMap to send an HTTP request. The retrieved data is in JSON format and includes information such as temperature, humidity, precipitation, and wind speed. The input is geographic location information and an API key, and the output is the latest environmental information data. This data is parsed and stored in a database.

[0700] Step 2:

[0701] server:

[0702] The server retrieves stored environmental information and environmental data for the past 30 years. The server uses MySQL or PostgreSQL to execute SQL queries to retrieve historical temperature data. The input is an SQL query for temperature data for the past 30 years, and the output is the retrieved historical temperature data. This data is processed using the NumPy library to calculate the average historical temperature.

[0703] Step 3:

[0704] server:

[0705] The acquired current environmental information is compared with historical average temperature data to calculate a global warming impact index. This calculation uses the NumPy library and shows the difference between the historical average temperature and the current temperature as a percentage. The input is the current temperature data and the historical average temperature data, and the output is the calculated global warming impact index. This index is saved in a database.

[0706] Step 4:

[0707] Device:

[0708] When a user launches the app, the device sends an HTTP GET request to the server to retrieve the latest global warming impact index. The input is the user request, and the output is the latest global warming impact index. The device parses this and displays it in an easy-to-understand format for the user. Specifically, it uses React Native to display a message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0709] Step 5:

[0710] User:

[0711] Users record their daily eco-friendly actions within the app. For example, they can enter, "I cycled to work today." If a smartwatch or energy consumption meter is connected, the device automatically collects data using the Google Fit API or HealthKit. The input is the user's behavioral data or data from the smart device, and the output is the data recorded within the app.

[0712] Step 6:

[0713] Device:

[0714] The device sends the collected user behavior data to the server. The input is the collected behavior data, and the output is an HTTP POST request to the server. The server receives this and stores it in a database.

[0715] Step 7:

[0716] server:

[0717] The server analyzes the received behavioral data and aggregates the data for one week. The data is aggregated using Python's Pandas library. The input is the user's behavioral data for one week, and the output is the aggregated results. Based on this result, the server evaluates the user's eco-friendly behavior and generates a feedback message.

[0718] Step 8:

[0719] Device:

[0720] The generated feedback message is sent to the user's device using Firebase Cloud Messaging. The input is the feedback message sent from the server, and the output is the notification sent to the user's device. The user can understand the effect of their actions in real time.

[0721] Step 9:

[0722] server:

[0723] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management. The input is user behavior data and point calculation logic, and the output is the accumulated points. When a certain number of points is reached, a reward is set, such as a discount coupon for an eco-friendly product.

[0724] Step 10:

[0725] Device:

[0726] The device will send a notification to the user that they have earned a reward. The notification will say, "You have earned 100 points. A new reward is available." The input is the reward notification sent from the server, and the output is the notification to the device. The user can check the reward details through the app and use the selected reward.

[0727] (Application example 1)

[0728] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0729] Currently, there are systems that detect the effects of global warming in real time and promote eco-friendly behavior based on that information, but there are not enough ways for users to specifically incorporate these systems into their lives or to immediately feel the effects. As a result, there is a problem that users' environmental awareness and actions are not aligned. There is also a lack of ways to clearly show the extent to which purchasing behavior in a virtual environment contributes to the environment.

[0730] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0731] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting users' daily behavior data, means for analyzing users' behavior based on the collected data and generating feedback, means for notifying the user device of the generated feedback, means for providing rewards for users' environmental protection behavior, and means for encouraging users to purchase products in the virtual environment using the global warming risk index. This allows users to grasp the effects of global warming in real time, making it easier for them to immediately understand the impact of their actions on the environment, and also encouraging them to purchase products in the virtual environment.

[0732] The "means for obtaining weather data in real time" refers to a means for obtaining the latest weather data from a weather information service via the Internet.

[0733] The "means of calculating the global warming risk index by comparing it with past weather data" is a means of comparing the latest weather data with weather data from the past 30 years, and as a result, expressing the degree of progress of global warming in numerical terms.

[0734] The "means for displaying the calculated global warming risk index on a user device" refers to a means for visually displaying the calculated index on a device used by a user.

[0735] The "means for collecting data on the user's daily activities" refers to a means for recording or automatically acquiring the eco-friendly activities that the user performs on a daily basis.

[0736] "Means for analyzing user behavior based on collected data and generating feedback" refers to means for analyzing collected user data, evaluating user behavior based on the analysis results, and generating specific feedback.

[0737] The "means for notifying the user device of the generated feedback" refers to a means for notifying the user device of the content of the generated feedback.

[0738] The "means for providing rewards for users' environmental protection actions" refers to a means for giving rewards such as points or discount coupons to users for eco-friendly actions they take.

[0739] "Means for promoting product purchasing behavior of users within a virtual environment using the global warming risk index" refers to means for motivating users to purchase eco-friendly products within a virtual environment using the calculated global warming risk index.

[0740] This invention is a system that detects the effects of global warming in real time and encourages users to behave eco-friendly. It mainly consists of three elements: a server, a terminal, and a user. The specific operation and technical details of this system are as follows:

[0741] Obtaining and displaying the Global Warming Risk Index

[0742] server:

[0743] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it accesses a weather information API to obtain current temperature data. The obtained data is stored in a database and compared with weather data from the past 30 years. The results of this comparison are used to calculate a global warming risk index. This index quantifies the difference between past average temperature data and current data, and quantitatively indicates the progress of global warming. The calculated index is updated in the database as needed.

[0744] Device:

[0745] When a user launches the application, the device sends a request to the server to retrieve the latest global warming risk index. The device then displays the index and provides a specific message to the user, such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years."

[0746] User Data Collection and Feedback

[0747] Device:

[0748] Users record their daily eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." It is also possible to link the app with smart devices such as smartwatches and energy consumption meters to automatically collect user behavior data.

[0749] server:

[0750] The server receives and analyzes user behavior data sent from the device. Based on the collected data, it evaluates the user's behavior over the course of a week and generates a feedback message. For example, it could evaluate the user's behavior by saying, "This week, you reduced your carbon footprint by 35% more than usual."

[0751] Device:

[0752] The server notifies the user of the feedback messages sent from the server, allowing the user to grasp the specific effects of their actions in real time.

[0753] Implementing a reward system

[0754] server:

[0755] The server will also implement a points system that rewards users for their eco-friendly behavior. When a certain number of points are accumulated, specific rewards can be set, such as discount coupons for eco-friendly products.

[0756] Device:

[0757] The device will notify the user when they have accumulated points and provide information to enable them to view, select, and use reward details. For example, a notification such as "You have accumulated 100 points. New rewards are available."

[0758] Application in virtual stores

[0759] server:

[0760] The server uses the calculated global warming risk index to encourage users to purchase eco-friendly products in the virtual environment. Based on this index, the server specifically indicates how much the product selected by the user contributes to preventing global warming.

[0761] Device:

[0762] When users purchase eco-friendly products displayed in the virtual store, the device compares the impact with real-time weather data. For example, it provides a message such as, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years, but by choosing this water bottle, you can reduce CO2 emissions by approximately 20 kg over the course of a year."

[0763] Example prompts for generative AI models

[0764] An example prompt is:

[0765] Obtain real-time weather data for Tokyo and compare it with the past 30 years to calculate a global warming risk index. Based on the obtained index, provide a message that informs users about the impact of purchasing eco-friendly products. For example, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years. By choosing a reusable water bottle, you can reduce CO2 emissions by approximately 20 kg per year."

[0766] Overall, the system allows users to feel the effects of global warming in real time and proactively incorporate eco-friendly behaviors into their lives. Product selection within the virtual environment will also be further encouraged by clearly showing the tangible contribution to environmental conservation.

[0767] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0768] Step 1:

[0769] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it sends an API request to obtain current temperature data. At this time, the server receives the response data from the weather information service and parses it in a format such as JSON. The input is the response data from the weather information API, and the output is the parsed current temperature data.

[0770] Step 2:

[0771] The server retrieves weather data from the past 30 years and the latest weather data from the database, compares them, and calculates the Global Warming Crisis Index. Specifically, it calculates the temperature increase rate using the latest temperature data and historical average temperature data. In this case, the server receives current temperature data and data from the past 30 years as arguments, and outputs the Global Warming Crisis Index as the calculation result. The input is the latest temperature data and historical temperature data, and the output is the Global Warming Crisis Index.

[0772] Step 3:

[0773] The server stores the calculated Global Warming Crisis Index in a database and updates it as needed. Specifically, it inserts or updates the Global Warming Crisis Index into the database as a new record. The input is the calculation result of the Global Warming Crisis Index, and the output is the updated state of the database.

[0774] Step 4:

[0775] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index. Specifically, it sends an HTTP request to obtain the latest Global Warming Risk Index from the server. The input is the HTTP request, and the output is the obtained Global Warming Risk Index.

[0776] Step 5:

[0777] The device displays the acquired Global Warming Risk Index to the user. Specifically, it updates the UI component that visually displays the acquired value. For example, it displays a message on the screen saying, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years." The input is the acquired Global Warming Risk Index, and the output is the displayed message.

[0778] Step 6:

[0779] Users record their daily eco-friendly actions in the app. Specifically, users enter their actions into a form within the app, and the app saves the data. The input is the action data entered by the user, and the output is the saved action data.

[0780] Step 7:

[0781] The terminal works in conjunction with smart devices to automatically collect user behavioral data. Specifically, it acquires data from devices such as smartwatches and energy consumption meters and sends it to the app. The input is behavioral data from the smart device, and the output is behavioral data recorded in the app.

[0782] Step 8:

[0783] The server receives and analyzes the collected user behavior data. Specifically, it retrieves the behavior data from the database and performs statistical analysis to evaluate the user's eco-friendly behavior. The input is the behavior data, and the output is the evaluation result.

[0784] Step 9:

[0785] The server generates a feedback message based on the analysis results. Specifically, it uses a generative AI model to generate a feedback message for the user's actions. The input is the analysis results, and the output is the feedback message.

[0786] Step 10:

[0787] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the feedback message, and the output is the message sent to the user's terminal.

[0788] Step 11:

[0789] The terminal notifies the user of the feedback message by displaying the message to the user using a notification function. The input is the received feedback message, and the output is the displayed notification.

[0790] Step 12:

[0791] The server implements a system that awards points to users for their eco-friendly behavior. Specifically, it calculates points based on user behavior data and stores them in a database. The input is the user behavior data, and the output is the awarded points.

[0792] Step 13:

[0793] The server sets a reward for users who have accumulated 100 points and registers it in the database. Specifically, it sets a reward such as a discount coupon for eco-friendly products for users who have reached a certain number of points. The input is the user's accumulated points, and the output is the set reward.

[0794] Step 14:

[0795] The terminal sends a notification to the user that a reward has been earned. Specifically, it notifies the user with a message that a reward is available. The input is the set reward, and the output is the sent notification.

[0796] Step 15:

[0797] Within the virtual store system, the terminal presents the user with the impact of purchasing a product based on the Global Warming Risk Index. Specifically, when a user purchases a reusable product, the terminal displays in real time the extent to which that purchase will contribute to preventing global warming. The input is the Global Warming Risk Index and product information, and the output is the impact information presented to the user.

[0798] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0799] This system calculates the impact of global warming based on real-time and historical weather data and displays the results on a user's device. It also collects and analyzes the user's daily behavior and emotional state, generating feedback and rewarding the user for their eco-friendly behavior. The specific operation of each component and their interaction are described below.

[0800] Obtaining and displaying the Global Warming Risk Index

[0801] server:

[0802] The server periodically obtains real-time weather data from the weather information service API via the Internet.

[0803] The acquired data is stored in a database.

[0804] The server compares weather data from the past 30 years with the latest data to calculate the global warming risk index.

[0805] The calculated results are stored in a database.

[0806] Device:

[0807] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[0808] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[0809] User Data Collection and Feedback

[0810] Device:

[0811] Users record eco-friendly actions in the app, for example, by typing, "I cycled to work today."

[0812] By linking the smart device with the app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[0813] Additionally, an emotion engine is used to collect the user's emotional state (e.g., happy, sad, excited, etc.), which includes voice analysis, text input analysis, and camera-based facial expression analysis.

[0814] server:

[0815] The collected user data and emotion data are sent to the server.

[0816] The server analyzes the received data and evaluates the user's eco-friendly behavior, taking into account emotional data, such as how the user felt about a particular behavior.

[0817] Generate feedback messages based on the analysis, including personalized feedback based on emotional data (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive mood you experience while cycling to work.").

[0818] A feedback message is sent to the user's terminal.

[0819] Device:

[0820] Users can check the feedback messages in the app.

[0821] Implementing a reward system

[0822] server:

[0823] The server implements a system that gives and accumulates points for users' eco-friendly actions.

[0824] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[0825] Device:

[0826] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[0827] Users can check the reward details and select and use them through the app.

[0828] Specific examples

[0829] 1. Obtaining and displaying the Global Warming Risk Index

[0830] The server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[0831] The server compares temperature data from the past 30 years with the latest data and calculates that this week's average temperature is 5% higher.

[0832] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[0833] 2. User Data Collection and Feedback

[0834] The user records in the app, "I went on an eco-walk today," and the device sends that data to the server.

[0835] Furthermore, the emotion engine analyzes the user's facial expression to detect the emotion of "joy" and transmits this to the server.

[0836] The server analyzes a week's worth of data and generates feedback such as, "This week you reduced your carbon footprint by 35% and felt more positive while cycling to work."

[0837] The feedback is displayed on the terminal for the user to review.

[0838] 3. Implementing a reward system

[0839] The server accumulates points for users' eco-friendly behavior.

[0840] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[0841] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[0842] Users can select and redeem rewards through the app.

[0843] The above is a concrete example of how the present invention can be implemented. This system allows users to feel the effects of global warming in real time, and by utilizing emotion data, users can more effectively incorporate eco-friendly behaviors into their daily lives.

[0844] The processing flow will be explained below.

[0845] Step 1: Obtaining weather data

[0846] server:

[0847] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[0848] The acquired data includes items such as temperature, humidity, and precipitation, and is stored in a database.

[0849] Step 2: Obtain and compare historical data

[0850] server:

[0851] The server retrieves weather data from the database for the past 30 years.

[0852] The latest weather data obtained is compared with past average temperature data to calculate the global warming risk index.

[0853] The calculated global warming risk index is stored in a database.

[0854] Step 3: Displaying the Crisis Index

[0855] Device:

[0856] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index.

[0857] The obtained global warming risk index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[0858] Step 4: Collect user data

[0859] User:

[0860] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[0861] By connecting the smart device and app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[0862] Step 5: Collecting sentiment data

[0863] Device:

[0864] The app's built-in emotion engine recognizes the user's emotional state through user input, the camera, and the microphone. For example, it can determine whether the user is having fun through voice analysis, or recognize emotions by analyzing facial expressions via the camera.

[0865] The collected emotion data is sent to the server.

[0866] Step 6: Sending data

[0867] Device:

[0868] The collected daily behavior data and emotion data are sent to a server.

[0869] Step 7: Data analysis and feedback generation

[0870] server:

[0871] The server analyzes the received user data and evaluates the impact of eco-friendly actions, while also considering emotional data to analyze how the user felt about each action.

[0872] Generate feedback messages based on the analysis results (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive feelings you get from cycling to work.").

[0873] A feedback message is sent to the user's terminal.

[0874] Step 8: Notification of feedback

[0875] Device:

[0876] The terminal notifies the user of the feedback message sent from the server.

[0877] Users can view feedback in the app.

[0878] Step 9: Reward Points Awarded

[0879] server:

[0880] The server awards points to users for their eco-friendly behavior.

[0881] When points reach a certain threshold, a reward (e.g., a discount coupon for an eco-friendly product) is set.

[0882] Step 10: Notification and Redemption of Rewards

[0883] Device:

[0884] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[0885] User:

[0886] Users can check the reward details and select and use them through the app.

[0887] The above is the processing flow in the embodiment of the invention. This flow allows users to feel the effects of global warming in real time and continuously take eco-friendly actions, including their emotions.

[0888] Example 2

[0889] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0890] Global warming has become a serious problem in modern society, and people need to feel its effects on a daily basis. Furthermore, there is a need for a method to visualize the environmental impact of individual actions and promote sustainable behavior. However, there is no comprehensive system available that collects real-time weather data, compares it with past data, collects and analyzes users' daily behavior data, and understands their emotional state. This makes the process of users accurately evaluating their eco-friendly behavior and receiving rewards complicated, making effective efforts difficult.

[0891] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0892] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting data on the user's daily behavior, means for analyzing the user's behavior based on the collected data and emotional state data and generating feedback, means for notifying the user device of the generated feedback, and means for providing rewards for the user's eco-friendly behavior, thereby enabling the user to grasp the effects of global warming in real time, have their eco-friendly behavior accurately evaluated, and be encouraged to be more environmentally friendly through rewards.

[0893] "Weather data" is a general term for information related to weather conditions, such as temperature, humidity, precipitation, and wind speed.

[0894] "Real-time" refers to conditions and data that are immediately available at the present time.

[0895] "Historical weather data" includes records of information relating to observed weather conditions over a particular period of time.

[0896] The Global Warming Risk Index is an index that evaluates the impact of global warming by comparing past weather data with the latest weather data.

[0897] "User device" is a general term for electronic devices used by users to receive and display information, such as smartphones, tablets, and PCs.

[0898] The "daily behavior data" includes information related to the user's daily activities, such as data on transportation methods and lifestyle habits.

[0899] "Emotional state data" is data that indicates the user's emotional state, and includes the results of analyzing emotions such as joy, sadness, and excitement.

[0900] "Analysis" is the act of evaluating the characteristics and trends of collected data.

[0901] "Feedback" refers to advice and evaluation given to users based on collected and analyzed data.

[0902] "Eco-friendly behavior" is a general term for actions that are considered environmentally friendly, including recycling, reusing, saving electricity, and saving energy.

[0903] "Rewards" refers to incentives or benefits provided to users for specific actions or achievements.

[0904] "Via the Internet" refers to a method of sending and receiving data using the Internet.

[0905] "Weather information provision service" refers to a service or system that provides weather-related data.

[0906] "Smart devices" are terminal devices that can connect to the Internet, such as smartphones, smartwatches, and fitness trackers.

[0907] "Emotion engine" is a general term for algorithms and systems that analyze emotions from a user's voice, facial expressions, text, etc.

[0908] This system acquires weather data in real time, compares it with past weather data to calculate a global warming risk index, and collects and analyzes the user's daily behavior data and emotional state to generate feedback and provide rewards for eco-friendly behavior. The specific operation of each component and how they work together are explained below.

[0909] Obtaining and displaying the Global Warming Risk Index

[0910] Server behavior:

[0911] The server periodically obtains real-time weather data via the internet using the API of a weather information service. For example, a timer can be set to send a request to the API at midnight every day. The obtained weather data is stored in a database. The server then retrieves weather data from the database for the past 30 years, compares it with the latest data, and runs an algorithm to calculate the global warming risk index. The calculated index is then stored back in the database.

[0912] Terminal behavior:

[0913] When a user launches the app on their device, the device sends a request to the server to obtain the latest global warming risk index. The obtained index is displayed to the user. For example, the display might say, "This week's average temperature in Tokyo is 5% higher than the average over the past 30 years."

[0914] Collecting user data and generating feedback

[0915] Terminal behavior:

[0916] Users can record their eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." By linking their smart device with the app, daily activity data (location information, number of steps, energy consumption, etc.) is automatically collected. Furthermore, an emotion engine uses voice and camera data to analyze the user's emotional state (e.g., joy, sadness, excitement, etc.). This includes voice analysis, text input analysis, and facial expression analysis using a camera.

[0917] Server behavior:

[0918] The server receives the collected data and emotional state data sent from the device and analyzes them. A specific algorithm is used for the analysis to evaluate the user's eco-friendly behavior, taking their emotional state into account in the process. For example, the server may evaluate the user as having a positive mood while commuting by bicycle. Based on the analysis results, a personalized feedback message is generated. For example, a feedback message such as "You have reduced your carbon footprint by 35% this week. In addition, your positive mood while commuting by bicycle has been confirmed" is generated and sent to the user's device.

[0919] Terminal behavior:

[0920] Users can check feedback messages through the app.

[0921] Implementing a reward system

[0922] Server behavior:

[0923] The server implements a system that awards and accumulates points for users' eco-friendly behavior. For example, "10 points for commuting by bicycle." When a certain number of points are accumulated, for example, 100 points, the user is offered a discount coupon for an eco-friendly product.

[0924] Terminal behavior:

[0925] The device will send a notification to the user informing them that a reward is available. For example, a notification saying "You have accumulated 100 points. New rewards are available." The user can check the reward details through the app and select and use the desired reward.

[0926] The above is a specific embodiment for carrying out the present invention. This system allows users to understand the impact of global warming in real time, have their eco-friendly behavior evaluated, and encourage further environmentally friendly behavior through rewards.

[0927] Prompt Sentence Examples

[0928] "I went on an eco-walk today."

[0929] "This month's carbon footprint is lower than last month's."

[0930] "I'd like to use a discount coupon for a new eco-friendly product."

[0931] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0932] Obtaining and displaying the Global Warming Risk Index

[0933] Step 1: Regularly acquire weather data

[0934] Server operation: The server periodically sets a timer and sends a request to the weather information service API. For example, the server calls the API every day at midnight.

[0935] Input: Timer event, API request

[0936] Output: Real-time weather data (e.g. temperature, humidity, precipitation)

[0937] What happens: The server authenticates using the API key and retrieves weather data for the specified region.

[0938] Step 2: Save your data

[0939] Server operation: Save the acquired weather data in a database.

[0940] Input: Weather data (e.g., JSON format)

[0941] Output: Weather data stored in a database

[0942] What it does: Adds a new record to the database, storing the date and time of acquisition and regional details.

[0943] Step 3: Calculate the Global Warming Risk Index

[0944] Server operation: Compares weather data from the past 30 years with the latest data and calculates the Global Warming Risk Index.

[0945] Input: Weather data from the past 30 years and the latest data obtained from the database

[0946] Output: Calculated Global Warming Risk Index

[0947] What it does: It runs an algorithm to calculate the rate of change in average temperature, for example, comparing the average temperature of the most recent year with the average temperature of the past 30 years and calculating the difference as a percentage.

[0948] Step 4: User retrieves and displays the index

[0949] What happens on the device: When a user launches the app, it sends a request to the server to get the latest Global Warming Risk Index.

[0950] Input: User action (launching the app)

[0951] Output: Obtained Global Warming Risk Index

[0952] Specific behavior: Parse the data sent as a response from the server and display it in the app interface. For example, it might say, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years."

[0953] Collecting user data and generating feedback

[0954] Step 1: Recording user behavior data

[0955] Device Action: The user types "I biked to work today" in the app.

[0956] Input: User action input (text format)

[0957] Output: Behavioral data stored in the app

[0958] Specific operation: Saves the entered text data in an internal database.

[0959] Step 2: Collecting emotion data

[0960] Device operation: The emotion engine analyzes audio data and camera footage to detect the user's emotional state.

[0961] Input: Audio data, camera footage

[0962] Output: Detected emotion data (e.g., happy, sad)

[0963] What it does: Implements voice and facial expression analysis algorithms to classify and detect emotional states, such as detecting joy from the user's voice tone.

[0964] Step 3: Send data to the server

[0965] Device operation: The collected user behavior data and emotion data are sent to the server as an HTTP request.

[0966] Input: User behavior data, emotion data

[0967] Output: Data sent to the server

[0968] What it does: Formats an HTTP request and sends it to the server with the required data as a payload.

[0969] Step 4: Data analysis and feedback generation

[0970] Server operation: The server analyzes the received data and evaluates the user's eco-friendly behavior.

[0971] Input: Submitted user behavior and emotion data

[0972] Output: Analysis results and feedback messages

[0973] What it does: Runs analytical algorithms and evaluates user behavior based on specific metrics. For example, calculates the carbon footprint reduction percentage over the week. Generates and customizes feedback messages. For example, "You've reduced your carbon footprint by 35% this week. What's more, cycling to work has confirmed your positive mood."

[0974] Step 5: View your feedback

[0975] Device Action: Display a feedback message in the user's app.

[0976] Input: Feedback message received from the server

[0977] Output: Feedback message displayed in the app

[0978] Specific behavior: Parse the feedback message and display it in the user interface. For example, display something like "This week's evaluation results are..."

[0979] Implementing a reward system

[0980] Step 1: Earn points for eco-friendly actions

[0981] Server operation: Executes the logic to award points to users for their daily eco-friendly actions.

[0982] Input: User behavior data and analysis results

[0983] Output: Points awarded

[0984] Specific operation: Points are calculated based on user behavior and stored in a database. For example, "10 points for each bicycle commute."

[0985] Step 2: Reward Settings and Notifications

[0986] Server operation: Monitors the point accumulation status and sets rewards for users who have accumulated a certain number of points.

[0987] Input: User's accumulated points

[0988] Output: The configured reward and notification message.

[0989] Specific behavior: When the user's points reach 100, provide a discount coupon for eco-friendly products. Generate and send a notification message to the user. Example: "You've accumulated 100 points. New rewards are available."

[0990] Step 3: Earn and use your rewards

[0991] Device operation: The user checks the reward details through the app and selects and uses the reward they want.

[0992] Input: User action (reward selection)

[0993] Output: Rewards used

[0994] Specific behavior: Display the reward details and select the reward you want. For example, use the reward by pressing the "Use eco product discount coupon" button.

[0995] (Application example 2)

[0996] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0997] While factories are required to optimize energy consumption and reduce their environmental impact, conventional systems have difficulty analyzing environmental impacts using real-time and historical weather data and suggesting specific eco-friendly actions to factory managers. Furthermore, there is no reward system for eco-friendly actions, which means that factory managers are not sufficiently motivated.

[0998] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0999] In this invention, the server includes a means for acquiring weather data in real time, a means for calculating a global warming risk index by comparing it with past weather data, and a means for collecting and analyzing energy consumption data in the factory, thereby enabling optimization of energy consumption in the factory and reduction of environmental load.

[1000] "Means for obtaining weather data in real time" refers to a function for obtaining the latest weather data from a weather information service via the Internet.

[1001] "Means for calculating the global warming risk index by comparing with past weather data" is a function that quantifies the impact of global warming by comparing past weather data with current weather data.

[1002] The "means for displaying the calculated global warming crisis index on the user device" is a function for visually conveying the calculated global warming crisis index on the user's device.

[1003] The "means for collecting data on daily user behavior" is a function for collecting information on daily user behavior.

[1004] The "means for analyzing user behavior based on collected data and generating feedback" is a function that analyzes the user's daily behavior data and creates feedback based on the results.

[1005] The "means for notifying the user device of the generated feedback" is a function for notifying the user device of the generated feedback.

[1006] The "means for collecting and analyzing energy consumption data within the factory" is a function for collecting and analyzing data on energy use within the factory.

[1007] "Means of proposing eco-friendly actions to factory managers based on analysis results" is a function that suggests environmentally friendly actions to factory managers based on the analysis results of collected data.

[1008] The "means for providing rewards for eco-friendly behavior of factory managers" is a function that provides rewards when factory managers behave in an environmentally friendly manner.

[1009] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Each element functions using specific hardware and software. Below, we will explain how each element works together to realize the invention.

[1010] Server Features

[1011] The server implements the following methods:

[1012] 1. Means of obtaining weather data in real time: The server periodically obtains the latest weather data from weather information services via the Internet, thereby ensuring that the latest weather information is always available.

[1013] 2. Means for calculating the Global Warming Risk Index by comparing with past weather data: The server compares weather data from the past 30 years with current weather data and calculates the Global Warming Risk Index. This calculation uses a high-performance database and analytical algorithms.

[1014] 3. Collecting and analyzing energy consumption data within the factory: Energy consumption data is collected from sensors and smart devices installed within the factory and analyzed using machine learning models.

[1015] Device Features

[1016] The terminal acts as a user device and implements the following:

[1017] 1. A means for displaying the calculated Global Warming Risk Index on the user device: The Global Warming Risk Index obtained from the server is visually displayed to the user. Specifically, a message such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years" is displayed on the screen of a smartphone or tablet.

[1018] 2. Means of collecting user's daily behavior data: Automatically collect user's daily behavior data from smart devices, including location information, number of steps, energy consumption, etc.

[1019] 3. Means of notifying the user device of the generated feedback: The feedback generated by the server is notified to the user device, for example, by displaying a message such as "You reduced your carbon footprint by 35% this week. In addition, we have confirmed the positive feelings you get from commuting by bicycle."

[1020] User Involvement

[1021] Users provide their daily behavior data to the system. For example, by carrying a smartphone, location information and step counts are automatically collected. Furthermore, by practicing eco-friendly behavior, users can receive rewards from the system.

[1022] Specific examples

[1023] As a specific example of operation, we will explain the case where a factory manager uses an application called "Eco Manager for Factory Robots." Robots in the factory collect energy consumption data in real time and send it to a server. The server compares the acquired weather data with past data and calculates an environmental load index. Based on this, it provides feedback to the factory manager, such as "We recommend the following operations to reduce energy consumption by 10%."

[1024] Example prompt for a generative AI model:

[1025] Calculate the factory's environmental impact index based on weather and energy consumption data and suggest eco-friendly improvements.

[1026] Through these procedures, the system of the present invention can optimize energy consumption in factories and reduce environmental impact.

[1027] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1028] Step 1:

[1029] The server obtains real-time weather data from the weather information service's API via the Internet. It sends an API request as input and receives the latest weather data as output. This data is stored in the server's database. Specifically, it sends an HTTP request using an API key and receives weather data in JSON format as a response.

[1030] Step 2:

[1031] The server retrieves weather data from the past 30 years and compares it with real-time data to calculate the Global Warming Risk Index. It uses past and current weather data as input and obtains the Global Warming Risk Index as output. The server stores the calculation results in a database. Specifically, it sends a query to the historical weather database, compares it with current data to calculate the temperature difference, and then indexes the result.

[1032] Step 3:

[1033] The terminal periodically collects data on the user's daily activities from the smart device to automatically collect it. As input, it takes in sensor information and GPS data from the smart device. As output, it sends the collected daily activity data to the server. Specifically, the terminal periodically acquires data from the wearable device or smartphone and uploads it to the server.

[1034] Step 4:

[1035] The server analyzes the collected daily behavior data and evaluates the user's eco-friendly behavior. It uses the user's daily behavior data as input and generates behavior evaluation results and feedback messages as output. Specifically, it analyzes the data using a machine learning algorithm and scores eco-friendly behavior.

[1036] Step 5:

[1037] The server generates a feedback message based on the analysis results and notifies the user device. It uses the analysis results and a feedback template as input and generates a customized feedback message as output. Specific operations include generating a message such as "You reduced your carbon footprint by 35% this week" and sending a push notification to the user's smartphone.

[1038] Step 6:

[1039] The server collects energy consumption data within the factory and, based on the analysis results, suggests eco-friendly actions to factory managers. Energy consumption data and real-time weather data are used as input, and specific improvement suggestions are generated as output. For example, the server analyzes energy consumption data and generates a suggestion message such as, "To reduce energy consumption by 10%, we recommend the following actions."

[1040] Step 7:

[1041] The server implements a system that rewards factory managers for their eco-friendly behavior. It uses behavioral data and reward conditions as input and generates reward information as output. Specifically, it sets up a points system, sets rewards (e.g., discount coupons for eco-friendly products) when a certain number of points are accumulated, and sends a notification.

[1042] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1043] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1044] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1045] [Third embodiment]

[1046] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1047] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1048] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1049] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1050] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1051] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1052] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1053] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1054] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1055] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1056] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1057] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1058] This invention is a system that uses real-time weather data to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[1059] Obtaining and displaying the Global Warming Risk Index

[1060] server:

[1061] The server retrieves real-time weather data from weather information services via the Internet.

[1062] The acquired data will be stored in a database for comparison with weather data from the past 30 years.

[1063] The server compares past average temperature data with current data and calculates the Global Warming Risk Index, which is a numerical representation of the progress and impact of global warming.

[1064] The calculation results are stored in a database that is updated continuously.

[1065] Device:

[1066] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[1067] The device will then display the obtained index to the user, for example displaying a specific message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1068] User Data Collection and Feedback

[1069] Device:

[1070] Users record their daily eco-friendly actions in the app, for example, by entering "I cycled to work today."

[1071] In addition, user behavior data is automatically collected in conjunction with smart devices (e.g., smartwatches and energy consumption meters).

[1072] server:

[1073] The server receives and analyzes the collected data on the user's daily activities.

[1074] Based on the collected data, the eco-friendly actions taken by the user are recorded and the data is compiled for one week.

[1075] Based on the results, the system evaluates the user's eco-friendly behavior and generates a feedback message, such as "This week you reduced your carbon footprint by 35% more than usual."

[1076] Device:

[1077] The server sends feedback messages to the user, allowing the user to understand the specific effects of their actions in real time.

[1078] Implementing a reward system

[1079] server:

[1080] We will implement a system that awards and accumulates points for users' eco-friendly actions.

[1081] The server sets rewards for users when they accumulate a certain number of points, for example, 100 points, and offers discount coupons for eco-friendly products.

[1082] Device:

[1083] Sending a notification to the user that they have earned a reward, for example, "You have earned 100 points! New rewards are available!"

[1084] Users can check the reward details and select and use them through the app.

[1085] Specific examples

[1086] 1. Obtaining and displaying the Global Warming Risk Index:

[1087] Every day, the server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[1088] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[1089] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1090] 2. User Data Collection and Feedback:

[1091] The user records in the app, "I went on an eco-walk today."

[1092] The device sends the data to the server, which analyzes one week's worth of data.

[1093] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[1094] A feedback message is sent to the user's terminal.

[1095] 3. Implementing a reward system:

[1096] The server accumulates points for users' eco-friendly behavior.

[1097] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[1098] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[1099] Users select and redeem rewards through the app.

[1100] The above is an embodiment of the present invention. This system allows users to feel the effects of global warming in real time and incorporate eco-friendly behavior into their daily lives.

[1101] The processing flow will be explained below.

[1102] Step 1: Obtaining weather data

[1103] server:

[1104] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[1105] Save the retrieved data in the database.

[1106] Step 2: Compare with historical data

[1107] server:

[1108] The server retrieves the latest stored weather data and weather data from the past 30 years from the database.

[1109] Calculate the average temperature over the past 30 years.

[1110] Step 3: Calculate the Global Warming Risk Index

[1111] server:

[1112] The latest weather data is compared with historical average temperature data to calculate the Global Warming Risk Index, which indicates the percentage of current temperatures above the historical average.

[1113] The calculation results are saved in the database again.

[1114] Step 4: Displaying the Crisis Index

[1115] Device:

[1116] The user launches the app.

[1117] The app sends a request to the server to get the latest Global Warming Risk Index.

[1118] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[1119] Step 5: Collect user data

[1120] User:

[1121] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[1122] The smart device and app work together to automatically collect daily behavior data (location information, number of steps, energy consumption, etc.).

[1123] Step 6: Sending data

[1124] Device:

[1125] The collected user data is sent from the device to a server.

[1126] Step 7: Data analysis

[1127] server:

[1128] The server analyzes the collected data and evaluates the user's eco-friendly behavior.

[1129] The data for one week is compiled and the effect of eco-friendly behavior is calculated (e.g., reducing carbon footprint by 35% more than usual).

[1130] Step 8: Feedback Generation

[1131] server:

[1132] The server generates user feedback based on the analysis (e.g., "You reduced your carbon footprint by 35% this week").

[1133] Step 9: Notification of feedback

[1134] Device:

[1135] The feedback message sent from the server is displayed on the terminal.

[1136] The user can review the feedback.

[1137] Step 10: Awarding reward points

[1138] server:

[1139] The server awards points to users for their eco-friendly behavior.

[1140] The accumulated points are stored in a database.

[1141] Step 11: Set up and notify rewards

[1142] server:

[1143] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[1144] Device:

[1145] The device notifies the user that a reward is available (e.g., "You've collected 100 points! New rewards are available.").

[1146] Step 12: Use your rewards

[1147] User:

[1148] Users can check the reward details and select and use them through the app.

[1149] Example 1

[1150] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1151] In modern society, there is a lack of concrete mechanisms to grasp the impact of global warming in real time and to encourage people to be eco-friendly. There is also a need for a system that allows users to instantly recognize the extent to which their daily actions contribute to the environment and receive feedback and appropriate rewards for their actions.

[1152] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1153] In this invention, the server includes means for acquiring environmental information in real time, means for calculating a global warming impact index by comparing it with past environmental information, means for displaying the calculated global warming impact index on a user interface, means for collecting end-user daily behavior data, means for analyzing end-user behavior based on the collected data and generating feedback, means for notifying the user interface of the generated feedback, and means for providing rewards to end-users for their environmental protection behavior, thereby enabling users to recognize the impact of global warming in real time, immediately understand the effects of their own eco-friendly behavior, and receive appropriate feedback and rewards.

[1154] "Real-time" refers to data and information being processed and displayed immediately at the moment it is acquired or updated.

[1155] "Environmental information" refers to data related to the natural environment, such as temperature, humidity, precipitation, and wind speed.

[1156] The "global warming impact index" refers to an indicator that numerically shows the progress and impact of global warming based on past and present environmental data.

[1157] "User interface" refers to the display screen and operating means used to exchange information and instructions between the user and the system.

[1158] "End User" refers to the final user who directly uses a system or application.

[1159] "Daily Behavioral Data" refers to data relating to the End User's daily activities and habits, such as commuting method and energy consumption.

[1160] "Feedback" refers to information provided by a system that evaluates and comments on a user's actions, allowing the user to understand the results and impact of those actions.

[1161] "Rewards" refers to rewards or incentives provided to users for specific actions or achievements, including, for example, points or discount coupons.

[1162] This invention is a system that uses real-time environmental information to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[1163] Obtaining and displaying the global warming impact index

[1164] server:

[1165] The server obtains real-time environmental information from weather information providers, such as online services like WeatherAPI and OpenWeatherMap.

[1166] The server stores the acquired environmental information in a database, typically a relational database such as MySQL or PostgreSQL.

[1167] The server compares data from the past 30 years with current data and calculates a global warming impact index using Python's NumPy library.

[1168] The calculated global warming impact index is stored in a database and updated as needed.

[1169] Device:

[1170] When a user launches the app, the device sends a request to the server to obtain the latest global warming impact index, using a RESTful API.

[1171] The device displays the obtained index to the user. For example, a notification such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years" is displayed. React Native is used for the display.

[1172] User Data Collection and Feedback

[1173] Device:

[1174] Users record their daily eco-friendly actions within the app, such as "I cycled to work today."

[1175] The device connects to smart devices (e.g., smartwatches and energy consumption meters) and automatically collects user behavior data. This connection uses the Google Fit API and HealthKit.

[1176] server:

[1177] The server receives user behavior data sent from the device and stores it in a database. The received data is analyzed and aggregated for one week. This aggregation is performed using Python libraries such as Pandas and Scikit-learn.

[1178] The server generates a feedback message based on the analysis results, such as "This week you reduced your carbon footprint by 35% more than usual."

[1179] Device:

[1180] Feedback messages sent from the server are sent to the device. Firebase Cloud Messaging is used for notifications, allowing users to understand the effects of their actions in real time.

[1181] Implementing a reward system

[1182] server:

[1183] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management.

[1184] The server sets rewards when a user accumulates a certain number of points, and provides discount coupons for eco-friendly products when the user accumulates 100 points, for example.

[1185] Device:

[1186] The device will then notify the user that they have earned a reward. They will receive a notification saying, "You have earned 100 points. New rewards are available."

[1187] Users can check reward details and use the rewards they select through the app. React Native is also used to display and operate reward information.

[1188] Specific examples

[1189] 1. Obtaining and displaying the Global Warming Impact Index:

[1190] Every day, the server obtains environmental information for Tokyo from a weather information service and stores the new data in a database.

[1191] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[1192] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1193] 2. User Data Collection and Feedback:

[1194] The user records in the app, "I went on an eco-walk today."

[1195] The device sends the data to the server, which analyzes one week's worth of data.

[1196] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[1197] A feedback message is sent to the user's terminal.

[1198] 3. Implementing a reward system:

[1199] The server accumulates points for users' eco-friendly behavior.

[1200] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[1201] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[1202] Users select and redeem rewards through the app.

[1203] Prompt Sentence Examples

[1204] "Write a Python program that compares weather data from the past 30 years with current data and calculates the progress of global warming."

[1205] By inputting this prompt into a generative AI model, the corresponding Python code is generated.

[1206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1207] Step 1:

[1208] server:

[1209] Every day, the server retrieves the latest environmental information from the environmental information service via the network. Specifically, it uses WeatherAPI or OpenWeatherMap to send an HTTP request. The retrieved data is in JSON format and includes information such as temperature, humidity, precipitation, and wind speed. The input is geographic location information and an API key, and the output is the latest environmental information data. This data is parsed and stored in a database.

[1210] Step 2:

[1211] server:

[1212] The server retrieves stored environmental information and environmental data for the past 30 years. The server uses MySQL or PostgreSQL to execute SQL queries to retrieve historical temperature data. The input is an SQL query for temperature data for the past 30 years, and the output is the retrieved historical temperature data. This data is processed using the NumPy library to calculate the average historical temperature.

[1213] Step 3:

[1214] server:

[1215] The acquired current environmental information is compared with historical average temperature data to calculate a global warming impact index. This calculation uses the NumPy library and shows the difference between the historical average temperature and the current temperature as a percentage. The input is the current temperature data and the historical average temperature data, and the output is the calculated global warming impact index. This index is saved in a database.

[1216] Step 4:

[1217] Device:

[1218] When a user launches the app, the device sends an HTTP GET request to the server to retrieve the latest global warming impact index. The input is the user request, and the output is the latest global warming impact index. The device parses this and displays it in an easy-to-understand format for the user. Specifically, it uses React Native to display a message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1219] Step 5:

[1220] User:

[1221] Users record their daily eco-friendly actions within the app. For example, they can enter, "I cycled to work today." If a smartwatch or energy consumption meter is connected, the device automatically collects data using the Google Fit API or HealthKit. The input is the user's behavioral data or data from the smart device, and the output is the data recorded within the app.

[1222] Step 6:

[1223] Device:

[1224] The device sends the collected user behavior data to the server. The input is the collected behavior data, and the output is an HTTP POST request to the server. The server receives this and stores it in a database.

[1225] Step 7:

[1226] server:

[1227] The server analyzes the received behavioral data and aggregates the data for one week. The data is aggregated using Python's Pandas library. The input is the user's behavioral data for one week, and the output is the aggregated results. Based on this result, the server evaluates the user's eco-friendly behavior and generates a feedback message.

[1228] Step 8:

[1229] Device:

[1230] The generated feedback message is sent to the user's device using Firebase Cloud Messaging. The input is the feedback message sent from the server, and the output is the notification sent to the user's device. The user can understand the effect of their actions in real time.

[1231] Step 9:

[1232] server:

[1233] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management. The input is user behavior data and point calculation logic, and the output is the accumulated points. When a certain number of points is reached, a reward is set, such as a discount coupon for an eco-friendly product.

[1234] Step 10:

[1235] Device:

[1236] The device will send a notification to the user that they have earned a reward. The notification will say, "You have earned 100 points. A new reward is available." The input is the reward notification sent from the server, and the output is the notification to the device. The user can check the reward details through the app and use the selected reward.

[1237] (Application example 1)

[1238] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1239] Currently, there are systems that detect the effects of global warming in real time and promote eco-friendly behavior based on that information, but there are not enough ways for users to specifically incorporate these systems into their lives or to immediately feel the effects. As a result, there is a problem that users' environmental awareness and actions are not aligned. There is also a lack of ways to clearly show the extent to which purchasing behavior in a virtual environment contributes to the environment.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1241] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting users' daily behavior data, means for analyzing users' behavior based on the collected data and generating feedback, means for notifying the user device of the generated feedback, means for providing rewards for users' environmental protection behavior, and means for encouraging users to purchase products in the virtual environment using the global warming risk index. This allows users to grasp the effects of global warming in real time, making it easier for them to immediately understand the impact of their actions on the environment, and also encouraging them to purchase products in the virtual environment.

[1242] The "means for obtaining weather data in real time" refers to a means for obtaining the latest weather data from a weather information service via the Internet.

[1243] The "means of calculating the global warming risk index by comparing it with past weather data" is a means of comparing the latest weather data with weather data from the past 30 years, and as a result, expressing the degree of progress of global warming in numerical terms.

[1244] The "means for displaying the calculated global warming risk index on a user device" refers to a means for visually displaying the calculated index on a device used by a user.

[1245] The "means for collecting data on the user's daily activities" refers to a means for recording or automatically acquiring the eco-friendly activities that the user performs on a daily basis.

[1246] "Means for analyzing user behavior based on collected data and generating feedback" refers to means for analyzing collected user data, evaluating user behavior based on the analysis results, and generating specific feedback.

[1247] The "means for notifying the user device of the generated feedback" refers to a means for notifying the user device of the content of the generated feedback.

[1248] The "means for providing rewards for users' environmental protection actions" refers to a means for giving rewards such as points or discount coupons to users for eco-friendly actions they take.

[1249] "Means for promoting product purchasing behavior of users within a virtual environment using the global warming risk index" refers to means for motivating users to purchase eco-friendly products within a virtual environment using the calculated global warming risk index.

[1250] This invention is a system that detects the effects of global warming in real time and encourages users to behave eco-friendly. It mainly consists of three elements: a server, a terminal, and a user. The specific operation and technical details of this system are as follows:

[1251] Obtaining and displaying the Global Warming Risk Index

[1252] server:

[1253] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it accesses a weather information API to obtain current temperature data. The obtained data is stored in a database and compared with weather data from the past 30 years. The results of this comparison are used to calculate a global warming risk index. This index quantifies the difference between past average temperature data and current data, and quantitatively indicates the progress of global warming. The calculated index is updated in the database as needed.

[1254] Device:

[1255] When a user launches the application, the device sends a request to the server to retrieve the latest global warming risk index. The device then displays the index and provides a specific message to the user, such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years."

[1256] User Data Collection and Feedback

[1257] Device:

[1258] Users record their daily eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." It is also possible to link the app with smart devices such as smartwatches and energy consumption meters to automatically collect user behavior data.

[1259] server:

[1260] The server receives and analyzes user behavior data sent from the device. Based on the collected data, it evaluates the user's behavior over the course of a week and generates a feedback message. For example, it could evaluate the user's behavior by saying, "This week, you reduced your carbon footprint by 35% more than usual."

[1261] Device:

[1262] The server notifies the user of the feedback messages sent from the server, allowing the user to grasp the specific effects of their actions in real time.

[1263] Implementing a reward system

[1264] server:

[1265] The server will also implement a points system that rewards users for their eco-friendly behavior. When a certain number of points are accumulated, specific rewards can be set, such as discount coupons for eco-friendly products.

[1266] Device:

[1267] The device will notify the user when they have accumulated points and provide information to enable them to view, select, and use reward details. For example, a notification such as "You have accumulated 100 points. New rewards are available."

[1268] Application in virtual stores

[1269] server:

[1270] The server uses the calculated global warming risk index to encourage users to purchase eco-friendly products in the virtual environment. Based on this index, the server specifically indicates how much the product selected by the user contributes to preventing global warming.

[1271] Device:

[1272] When users purchase eco-friendly products displayed in the virtual store, the device compares the impact with real-time weather data. For example, it provides a message such as, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years, but by choosing this water bottle, you can reduce CO2 emissions by approximately 20 kg over the course of a year."

[1273] Example prompts for generative AI models

[1274] An example prompt is:

[1275] Obtain real-time weather data for Tokyo and compare it with the past 30 years to calculate a global warming risk index. Based on the obtained index, provide a message that informs users about the impact of purchasing eco-friendly products. For example, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years. By choosing a reusable water bottle, you can reduce CO2 emissions by approximately 20 kg per year."

[1276] Overall, the system allows users to feel the effects of global warming in real time and proactively incorporate eco-friendly behaviors into their lives. Product selection within the virtual environment will also be further encouraged by clearly showing the tangible contribution to environmental conservation.

[1277] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1278] Step 1:

[1279] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it sends an API request to obtain current temperature data. At this time, the server receives the response data from the weather information service and parses it in a format such as JSON. The input is the response data from the weather information API, and the output is the parsed current temperature data.

[1280] Step 2:

[1281] The server retrieves weather data from the past 30 years and the latest weather data from the database, compares them, and calculates the Global Warming Crisis Index. Specifically, it calculates the temperature increase rate using the latest temperature data and historical average temperature data. In this case, the server receives current temperature data and data from the past 30 years as arguments, and outputs the Global Warming Crisis Index as the calculation result. The input is the latest temperature data and historical temperature data, and the output is the Global Warming Crisis Index.

[1282] Step 3:

[1283] The server stores the calculated Global Warming Crisis Index in a database and updates it as needed. Specifically, it inserts or updates the Global Warming Crisis Index into the database as a new record. The input is the calculation result of the Global Warming Crisis Index, and the output is the updated state of the database.

[1284] Step 4:

[1285] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index. Specifically, it sends an HTTP request to obtain the latest Global Warming Risk Index from the server. The input is the HTTP request, and the output is the obtained Global Warming Risk Index.

[1286] Step 5:

[1287] The device displays the acquired Global Warming Risk Index to the user. Specifically, it updates the UI component that visually displays the acquired value. For example, it displays a message on the screen saying, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years." The input is the acquired Global Warming Risk Index, and the output is the displayed message.

[1288] Step 6:

[1289] Users record their daily eco-friendly actions in the app. Specifically, users enter their actions into a form within the app, and the app saves the data. The input is the action data entered by the user, and the output is the saved action data.

[1290] Step 7:

[1291] The terminal works in conjunction with smart devices to automatically collect user behavioral data. Specifically, it acquires data from devices such as smartwatches and energy consumption meters and sends it to the app. The input is behavioral data from the smart device, and the output is behavioral data recorded in the app.

[1292] Step 8:

[1293] The server receives and analyzes the collected user behavior data. Specifically, it retrieves the behavior data from the database and performs statistical analysis to evaluate the user's eco-friendly behavior. The input is the behavior data, and the output is the evaluation result.

[1294] Step 9:

[1295] The server generates a feedback message based on the analysis results. Specifically, it uses a generative AI model to generate a feedback message for the user's actions. The input is the analysis results, and the output is the feedback message.

[1296] Step 10:

[1297] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the feedback message, and the output is the message sent to the user's terminal.

[1298] Step 11:

[1299] The terminal notifies the user of the feedback message by displaying the message to the user using a notification function. The input is the received feedback message, and the output is the displayed notification.

[1300] Step 12:

[1301] The server implements a system that awards points to users for their eco-friendly behavior. Specifically, it calculates points based on user behavior data and stores them in a database. The input is the user behavior data, and the output is the awarded points.

[1302] Step 13:

[1303] The server sets a reward for users who have accumulated 100 points and registers it in the database. Specifically, it sets a reward such as a discount coupon for eco-friendly products for users who have reached a certain number of points. The input is the user's accumulated points, and the output is the set reward.

[1304] Step 14:

[1305] The terminal sends a notification to the user that a reward has been earned. Specifically, it notifies the user with a message that a reward is available. The input is the set reward, and the output is the sent notification.

[1306] Step 15:

[1307] Within the virtual store system, the terminal presents the user with the impact of purchasing a product based on the Global Warming Risk Index. Specifically, when a user purchases a reusable product, the terminal displays in real time the extent to which that purchase will contribute to preventing global warming. The input is the Global Warming Risk Index and product information, and the output is the impact information presented to the user.

[1308] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1309] This system calculates the impact of global warming based on real-time and historical weather data and displays the results on a user's device. It also collects and analyzes the user's daily behavior and emotional state, generating feedback and rewarding the user for their eco-friendly behavior. The specific operation of each component and their interaction are described below.

[1310] Obtaining and displaying the Global Warming Risk Index

[1311] server:

[1312] The server periodically obtains real-time weather data from the weather information service API via the Internet.

[1313] The acquired data is stored in a database.

[1314] The server compares weather data from the past 30 years with the latest data to calculate the global warming risk index.

[1315] The calculated results are stored in a database.

[1316] Device:

[1317] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[1318] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[1319] User Data Collection and Feedback

[1320] Device:

[1321] Users record eco-friendly actions in the app, for example, by typing, "I cycled to work today."

[1322] By linking the smart device with the app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[1323] Additionally, an emotion engine is used to collect the user's emotional state (e.g., happy, sad, excited, etc.), which includes voice analysis, text input analysis, and camera-based facial expression analysis.

[1324] server:

[1325] The collected user data and emotion data are sent to the server.

[1326] The server analyzes the received data and evaluates the user's eco-friendly behavior, taking into account emotional data, such as how the user felt about a particular behavior.

[1327] Generate feedback messages based on the analysis, including personalized feedback based on emotional data (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive mood you experience while cycling to work.").

[1328] A feedback message is sent to the user's terminal.

[1329] Device:

[1330] Users can check the feedback messages in the app.

[1331] Implementing a reward system

[1332] server:

[1333] The server implements a system that gives and accumulates points for users' eco-friendly actions.

[1334] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[1335] Device:

[1336] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[1337] Users can check the reward details and select and use them through the app.

[1338] Specific examples

[1339] 1. Obtaining and displaying the Global Warming Risk Index

[1340] The server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[1341] The server compares temperature data from the past 30 years with the latest data and calculates that this week's average temperature is 5% higher.

[1342] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1343] 2. User Data Collection and Feedback

[1344] The user records in the app, "I went on an eco-walk today," and the device sends that data to the server.

[1345] Furthermore, the emotion engine analyzes the user's facial expression to detect the emotion of "joy" and transmits this to the server.

[1346] The server analyzes a week's worth of data and generates feedback such as, "This week you reduced your carbon footprint by 35% and felt more positive while cycling to work."

[1347] The feedback is displayed on the terminal for the user to review.

[1348] 3. Implementing a reward system

[1349] The server accumulates points for users' eco-friendly behavior.

[1350] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[1351] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[1352] Users can select and redeem rewards through the app.

[1353] The above is a concrete example of how the present invention can be implemented. This system allows users to feel the effects of global warming in real time, and by utilizing emotion data, users can more effectively incorporate eco-friendly behaviors into their daily lives.

[1354] The processing flow will be explained below.

[1355] Step 1: Obtaining weather data

[1356] server:

[1357] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[1358] The acquired data includes items such as temperature, humidity, and precipitation, and is stored in a database.

[1359] Step 2: Obtain and compare historical data

[1360] server:

[1361] The server retrieves weather data from the database for the past 30 years.

[1362] The latest weather data obtained is compared with past average temperature data to calculate the global warming risk index.

[1363] The calculated global warming risk index is stored in a database.

[1364] Step 3: Displaying the Crisis Index

[1365] Device:

[1366] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index.

[1367] The obtained global warming risk index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[1368] Step 4: Collect user data

[1369] User:

[1370] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[1371] By connecting the smart device and app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[1372] Step 5: Collecting sentiment data

[1373] Device:

[1374] The app's built-in emotion engine recognizes the user's emotional state through user input, the camera, and the microphone. For example, it can determine whether the user is having fun through voice analysis, or recognize emotions by analyzing facial expressions via the camera.

[1375] The collected emotion data is sent to the server.

[1376] Step 6: Sending data

[1377] Device:

[1378] The collected daily behavior data and emotion data are sent to a server.

[1379] Step 7: Data analysis and feedback generation

[1380] server:

[1381] The server analyzes the received user data and evaluates the impact of eco-friendly actions, while also considering emotional data to analyze how the user felt about each action.

[1382] Generate feedback messages based on the analysis results (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive feelings you get from cycling to work.").

[1383] A feedback message is sent to the user's terminal.

[1384] Step 8: Notification of feedback

[1385] Device:

[1386] The terminal notifies the user of the feedback message sent from the server.

[1387] Users can view feedback in the app.

[1388] Step 9: Reward Points Awarded

[1389] server:

[1390] The server awards points to users for their eco-friendly behavior.

[1391] When points reach a certain threshold, a reward (e.g., a discount coupon for an eco-friendly product) is set.

[1392] Step 10: Notification and Redemption of Rewards

[1393] Device:

[1394] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[1395] User:

[1396] Users can check the reward details and select and use them through the app.

[1397] The above is the processing flow in the embodiment of the invention. This flow allows users to feel the effects of global warming in real time and continuously take eco-friendly actions, including their emotions.

[1398] Example 2

[1399] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1400] Global warming has become a serious problem in modern society, and people need to feel its effects on a daily basis. Furthermore, there is a need for a method to visualize the environmental impact of individual actions and promote sustainable behavior. However, there is no comprehensive system available that collects real-time weather data, compares it with past data, collects and analyzes users' daily behavior data, and understands their emotional state. This makes the process of users accurately evaluating their eco-friendly behavior and receiving rewards complicated, making effective efforts difficult.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1402] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting data on the user's daily behavior, means for analyzing the user's behavior based on the collected data and emotional state data and generating feedback, means for notifying the user device of the generated feedback, and means for providing rewards for the user's eco-friendly behavior, thereby enabling the user to grasp the effects of global warming in real time, have their eco-friendly behavior accurately evaluated, and be encouraged to be more environmentally friendly through rewards.

[1403] "Weather data" is a general term for information related to weather conditions, such as temperature, humidity, precipitation, and wind speed.

[1404] "Real-time" refers to conditions and data that are immediately available at the present time.

[1405] "Historical weather data" includes records of information relating to observed weather conditions over a particular period of time.

[1406] The Global Warming Risk Index is an index that evaluates the impact of global warming by comparing past weather data with the latest weather data.

[1407] "User device" is a general term for electronic devices used by users to receive and display information, such as smartphones, tablets, and PCs.

[1408] The "daily behavior data" includes information related to the user's daily activities, such as data on transportation methods and lifestyle habits.

[1409] "Emotional state data" is data that indicates the user's emotional state, and includes the results of analyzing emotions such as joy, sadness, and excitement.

[1410] "Analysis" is the act of evaluating the characteristics and trends of collected data.

[1411] "Feedback" refers to advice and evaluation given to users based on collected and analyzed data.

[1412] "Eco-friendly behavior" is a general term for actions that are considered environmentally friendly, including recycling, reusing, saving electricity, and saving energy.

[1413] "Rewards" refers to incentives or benefits provided to users for specific actions or achievements.

[1414] "Via the Internet" refers to a method of sending and receiving data using the Internet.

[1415] "Weather information provision service" refers to a service or system that provides weather-related data.

[1416] "Smart devices" are terminal devices that can connect to the Internet, such as smartphones, smartwatches, and fitness trackers.

[1417] "Emotion engine" is a general term for algorithms and systems that analyze emotions from a user's voice, facial expressions, text, etc.

[1418] This system acquires weather data in real time, compares it with past weather data to calculate a global warming risk index, and collects and analyzes the user's daily behavior data and emotional state to generate feedback and provide rewards for eco-friendly behavior. The specific operation of each component and how they work together are explained below.

[1419] Obtaining and displaying the Global Warming Risk Index

[1420] Server behavior:

[1421] The server periodically obtains real-time weather data via the internet using the API of a weather information service. For example, a timer can be set to send a request to the API at midnight every day. The obtained weather data is stored in a database. The server then retrieves weather data from the database for the past 30 years, compares it with the latest data, and runs an algorithm to calculate the global warming risk index. The calculated index is then stored back in the database.

[1422] Terminal behavior:

[1423] When a user launches the app on their device, the device sends a request to the server to obtain the latest global warming risk index. The obtained index is displayed to the user. For example, the display might say, "This week's average temperature in Tokyo is 5% higher than the average over the past 30 years."

[1424] Collecting user data and generating feedback

[1425] Terminal behavior:

[1426] Users can record their eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." By linking their smart device with the app, daily activity data (location information, number of steps, energy consumption, etc.) is automatically collected. Furthermore, an emotion engine uses voice and camera data to analyze the user's emotional state (e.g., joy, sadness, excitement, etc.). This includes voice analysis, text input analysis, and facial expression analysis using a camera.

[1427] Server behavior:

[1428] The server receives the collected data and emotional state data sent from the device and analyzes them. A specific algorithm is used for the analysis to evaluate the user's eco-friendly behavior, taking their emotional state into account in the process. For example, the server may evaluate the user as having a positive mood while commuting by bicycle. Based on the analysis results, a personalized feedback message is generated. For example, a feedback message such as "You have reduced your carbon footprint by 35% this week. In addition, your positive mood while commuting by bicycle has been confirmed" is generated and sent to the user's device.

[1429] Terminal behavior:

[1430] Users can check feedback messages through the app.

[1431] Implementing a reward system

[1432] Server behavior:

[1433] The server implements a system that awards and accumulates points for users' eco-friendly behavior. For example, "10 points for commuting by bicycle." When a certain number of points are accumulated, for example, 100 points, the user is offered a discount coupon for an eco-friendly product.

[1434] Terminal behavior:

[1435] The device will send a notification to the user informing them that a reward is available. For example, a notification saying "You have accumulated 100 points. New rewards are available." The user can check the reward details through the app and select and use the desired reward.

[1436] The above is a specific embodiment for carrying out the present invention. This system allows users to understand the impact of global warming in real time, have their eco-friendly behavior evaluated, and encourage further environmentally friendly behavior through rewards.

[1437] Prompt Sentence Examples

[1438] "I went on an eco-walk today."

[1439] "This month's carbon footprint is lower than last month's."

[1440] "I'd like to use a discount coupon for a new eco-friendly product."

[1441] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1442] Obtaining and displaying the Global Warming Risk Index

[1443] Step 1: Regularly acquire weather data

[1444] Server operation: The server periodically sets a timer and sends a request to the weather information service API. For example, the server calls the API every day at midnight.

[1445] Input: Timer event, API request

[1446] Output: Real-time weather data (e.g. temperature, humidity, precipitation)

[1447] What happens: The server authenticates using the API key and retrieves weather data for the specified region.

[1448] Step 2: Save your data

[1449] Server operation: Save the acquired weather data in a database.

[1450] Input: Weather data (e.g., JSON format)

[1451] Output: Weather data stored in a database

[1452] What it does: Adds a new record to the database, storing the date and time of acquisition and regional details.

[1453] Step 3: Calculate the Global Warming Risk Index

[1454] Server operation: Compares weather data from the past 30 years with the latest data and calculates the Global Warming Risk Index.

[1455] Input: Weather data from the past 30 years and the latest data obtained from the database

[1456] Output: Calculated Global Warming Risk Index

[1457] What it does: It runs an algorithm to calculate the rate of change in average temperature, for example, comparing the average temperature of the most recent year with the average temperature of the past 30 years and calculating the difference as a percentage.

[1458] Step 4: User retrieves and displays the index

[1459] What happens on the device: When a user launches the app, it sends a request to the server to get the latest Global Warming Risk Index.

[1460] Input: User action (launching the app)

[1461] Output: Obtained Global Warming Risk Index

[1462] Specific behavior: Parse the data sent as a response from the server and display it in the app interface. For example, it might say, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years."

[1463] Collecting user data and generating feedback

[1464] Step 1: Recording user behavior data

[1465] Device Action: The user types "I biked to work today" in the app.

[1466] Input: User action input (text format)

[1467] Output: Behavioral data stored in the app

[1468] Specific operation: Saves the entered text data in an internal database.

[1469] Step 2: Collecting emotion data

[1470] Device operation: The emotion engine analyzes audio data and camera footage to detect the user's emotional state.

[1471] Input: Audio data, camera footage

[1472] Output: Detected emotion data (e.g., happy, sad)

[1473] What it does: Implements voice and facial expression analysis algorithms to classify and detect emotional states, such as detecting joy from the user's voice tone.

[1474] Step 3: Send data to the server

[1475] Device operation: The collected user behavior data and emotion data are sent to the server as an HTTP request.

[1476] Input: User behavior data, emotion data

[1477] Output: Data sent to the server

[1478] What it does: Formats an HTTP request and sends it to the server with the required data as a payload.

[1479] Step 4: Data analysis and feedback generation

[1480] Server operation: The server analyzes the received data and evaluates the user's eco-friendly behavior.

[1481] Input: Submitted user behavior and emotion data

[1482] Output: Analysis results and feedback messages

[1483] What it does: Runs analytical algorithms and evaluates user behavior based on specific metrics. For example, calculates the carbon footprint reduction percentage over the week. Generates and customizes feedback messages. For example, "You've reduced your carbon footprint by 35% this week. What's more, cycling to work has confirmed your positive mood."

[1484] Step 5: View your feedback

[1485] Device Action: Display a feedback message in the user's app.

[1486] Input: Feedback message received from the server

[1487] Output: Feedback message displayed in the app

[1488] Specific behavior: Parse the feedback message and display it in the user interface. For example, display something like "This week's evaluation results are..."

[1489] Implementing a reward system

[1490] Step 1: Earn points for eco-friendly actions

[1491] Server operation: Executes the logic to award points to users for their daily eco-friendly actions.

[1492] Input: User behavior data and analysis results

[1493] Output: Points awarded

[1494] Specific operation: Points are calculated based on user behavior and stored in a database. For example, "10 points for each bicycle commute."

[1495] Step 2: Reward Settings and Notifications

[1496] Server operation: Monitors the point accumulation status and sets rewards for users who have accumulated a certain number of points.

[1497] Input: User's accumulated points

[1498] Output: The configured reward and notification message.

[1499] Specific behavior: When the user's points reach 100, provide a discount coupon for eco-friendly products. Generate and send a notification message to the user. Example: "You've accumulated 100 points. New rewards are available."

[1500] Step 3: Earn and use your rewards

[1501] Device operation: The user checks the reward details through the app and selects and uses the reward they want.

[1502] Input: User action (reward selection)

[1503] Output: Rewards used

[1504] Specific behavior: Display the reward details and select the reward you want. For example, use the reward by pressing the "Use eco product discount coupon" button.

[1505] (Application example 2)

[1506] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1507] While factories are required to optimize energy consumption and reduce their environmental impact, conventional systems have difficulty analyzing environmental impacts using real-time and historical weather data and suggesting specific eco-friendly actions to factory managers. Furthermore, there is no reward system for eco-friendly actions, which means that factory managers are not sufficiently motivated.

[1508] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1509] In this invention, the server includes a means for acquiring weather data in real time, a means for calculating a global warming risk index by comparing it with past weather data, and a means for collecting and analyzing energy consumption data in the factory, thereby enabling optimization of energy consumption in the factory and reduction of environmental load.

[1510] "Means for obtaining weather data in real time" refers to a function for obtaining the latest weather data from a weather information service via the Internet.

[1511] "Means for calculating the global warming risk index by comparing with past weather data" is a function that quantifies the impact of global warming by comparing past weather data with current weather data.

[1512] The "means for displaying the calculated global warming crisis index on the user device" is a function for visually conveying the calculated global warming crisis index on the user's device.

[1513] The "means for collecting data on daily user behavior" is a function for collecting information on daily user behavior.

[1514] The "means for analyzing user behavior based on collected data and generating feedback" is a function that analyzes the user's daily behavior data and creates feedback based on the results.

[1515] The "means for notifying the user device of the generated feedback" is a function for notifying the user device of the generated feedback.

[1516] The "means for collecting and analyzing energy consumption data within the factory" is a function for collecting and analyzing data on energy use within the factory.

[1517] "Means of proposing eco-friendly actions to factory managers based on analysis results" is a function that suggests environmentally friendly actions to factory managers based on the analysis results of collected data.

[1518] The "means for providing rewards for eco-friendly behavior of factory managers" is a function that provides rewards when factory managers behave in an environmentally friendly manner.

[1519] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Each element functions using specific hardware and software. Below, we will explain how each element works together to realize the invention.

[1520] Server Features

[1521] The server implements the following methods:

[1522] 1. Means of obtaining weather data in real time: The server periodically obtains the latest weather data from weather information services via the Internet, thereby ensuring that the latest weather information is always available.

[1523] 2. Means for calculating the Global Warming Risk Index by comparing with past weather data: The server compares weather data from the past 30 years with current weather data and calculates the Global Warming Risk Index. This calculation uses a high-performance database and analytical algorithms.

[1524] 3. Collecting and analyzing energy consumption data within the factory: Energy consumption data is collected from sensors and smart devices installed within the factory and analyzed using machine learning models.

[1525] Device Features

[1526] The terminal acts as a user device and implements the following:

[1527] 1. A means for displaying the calculated Global Warming Risk Index on the user device: The Global Warming Risk Index obtained from the server is visually displayed to the user. Specifically, a message such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years" is displayed on the screen of a smartphone or tablet.

[1528] 2. Means of collecting user's daily behavior data: Automatically collect user's daily behavior data from smart devices, including location information, number of steps, energy consumption, etc.

[1529] 3. Means of notifying the user device of the generated feedback: The feedback generated by the server is notified to the user device, for example, by displaying a message such as "You reduced your carbon footprint by 35% this week. In addition, we have confirmed the positive feelings you get from commuting by bicycle."

[1530] User Involvement

[1531] Users provide their daily behavior data to the system. For example, by carrying a smartphone, location information and step counts are automatically collected. Furthermore, by practicing eco-friendly behavior, users can receive rewards from the system.

[1532] Specific examples

[1533] As a specific example of operation, we will explain the case where a factory manager uses an application called "Eco Manager for Factory Robots." Robots in the factory collect energy consumption data in real time and send it to a server. The server compares the acquired weather data with past data and calculates an environmental load index. Based on this, it provides feedback to the factory manager, such as "We recommend the following operations to reduce energy consumption by 10%."

[1534] Example prompt for a generative AI model:

[1535] Calculate the factory's environmental impact index based on weather and energy consumption data and suggest eco-friendly improvements.

[1536] Through these procedures, the system of the present invention can optimize energy consumption in factories and reduce environmental impact.

[1537] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1538] Step 1:

[1539] The server obtains real-time weather data from the weather information service's API via the Internet. It sends an API request as input and receives the latest weather data as output. This data is stored in the server's database. Specifically, it sends an HTTP request using an API key and receives weather data in JSON format as a response.

[1540] Step 2:

[1541] The server retrieves weather data from the past 30 years and compares it with real-time data to calculate the Global Warming Risk Index. It uses past and current weather data as input and obtains the Global Warming Risk Index as output. The server stores the calculation results in a database. Specifically, it sends a query to the historical weather database, compares it with current data to calculate the temperature difference, and then indexes the result.

[1542] Step 3:

[1543] The terminal periodically collects data on the user's daily activities from the smart device to automatically collect it. As input, it takes in sensor information and GPS data from the smart device. As output, it sends the collected daily activity data to the server. Specifically, the terminal periodically acquires data from the wearable device or smartphone and uploads it to the server.

[1544] Step 4:

[1545] The server analyzes the collected daily behavior data and evaluates the user's eco-friendly behavior. It uses the user's daily behavior data as input and generates behavior evaluation results and feedback messages as output. Specifically, it analyzes the data using a machine learning algorithm and scores eco-friendly behavior.

[1546] Step 5:

[1547] The server generates a feedback message based on the analysis results and notifies the user device. It uses the analysis results and a feedback template as input and generates a customized feedback message as output. Specific operations include generating a message such as "You reduced your carbon footprint by 35% this week" and sending a push notification to the user's smartphone.

[1548] Step 6:

[1549] The server collects energy consumption data within the factory and, based on the analysis results, suggests eco-friendly actions to factory managers. Energy consumption data and real-time weather data are used as input, and specific improvement suggestions are generated as output. For example, the server analyzes energy consumption data and generates a suggestion message such as, "To reduce energy consumption by 10%, we recommend the following actions."

[1550] Step 7:

[1551] The server implements a system that rewards factory managers for their eco-friendly behavior. It uses behavioral data and reward conditions as input and generates reward information as output. Specifically, it sets up a points system, sets rewards (e.g., discount coupons for eco-friendly products) when a certain number of points are accumulated, and sends a notification.

[1552] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1553] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1554] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1555] [Fourth embodiment]

[1556] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1557] 7, a 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.

[1558] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1559] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1560] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1561] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1562] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1563] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1564] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1565] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1566] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1567] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1568] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1569] This invention is a system that uses real-time weather data to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[1570] Obtaining and displaying the Global Warming Risk Index

[1571] server:

[1572] The server retrieves real-time weather data from weather information services via the Internet.

[1573] The acquired data will be stored in a database for comparison with weather data from the past 30 years.

[1574] The server compares past average temperature data with current data and calculates the Global Warming Risk Index, which is a numerical representation of the progress and impact of global warming.

[1575] The calculation results are stored in a database that is updated continuously.

[1576] Device:

[1577] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[1578] The device will then display the obtained index to the user, for example displaying a specific message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1579] User Data Collection and Feedback

[1580] Device:

[1581] Users record their daily eco-friendly actions in the app, for example, by entering "I cycled to work today."

[1582] In addition, user behavior data is automatically collected in conjunction with smart devices (e.g., smartwatches and energy consumption meters).

[1583] server:

[1584] The server receives and analyzes the collected data on the user's daily activities.

[1585] Based on the collected data, the eco-friendly actions taken by the user are recorded and the data is compiled for one week.

[1586] Based on the results, the system evaluates the user's eco-friendly behavior and generates a feedback message, such as "This week you reduced your carbon footprint by 35% more than usual."

[1587] Device:

[1588] The server sends feedback messages to the user, allowing the user to understand the specific effects of their actions in real time.

[1589] Implementing a reward system

[1590] server:

[1591] We will implement a system that awards and accumulates points for users' eco-friendly actions.

[1592] The server sets rewards for users when they accumulate a certain number of points, for example, 100 points, and offers discount coupons for eco-friendly products.

[1593] Device:

[1594] Sending a notification to the user that they have earned a reward, for example, "You have earned 100 points! New rewards are available!"

[1595] Users can check the reward details and select and use them through the app.

[1596] Specific examples

[1597] 1. Obtaining and displaying the Global Warming Risk Index:

[1598] Every day, the server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[1599] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[1600] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1601] 2. User Data Collection and Feedback:

[1602] The user records in the app, "I went on an eco-walk today."

[1603] The device sends the data to the server, which analyzes one week's worth of data.

[1604] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[1605] A feedback message is sent to the user's terminal.

[1606] 3. Implementing a reward system:

[1607] The server accumulates points for users' eco-friendly behavior.

[1608] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[1609] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[1610] Users select and redeem rewards through the app.

[1611] The above is an embodiment of the present invention. This system allows users to feel the effects of global warming in real time and incorporate eco-friendly behavior into their daily lives.

[1612] The processing flow will be explained below.

[1613] Step 1: Obtaining weather data

[1614] server:

[1615] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[1616] Save the retrieved data in the database.

[1617] Step 2: Compare with historical data

[1618] server:

[1619] The server retrieves the latest stored weather data and weather data from the past 30 years from the database.

[1620] Calculate the average temperature over the past 30 years.

[1621] Step 3: Calculate the Global Warming Risk Index

[1622] server:

[1623] The latest weather data is compared with historical average temperature data to calculate the Global Warming Risk Index, which indicates the percentage of current temperatures above the historical average.

[1624] The calculation results are saved in the database again.

[1625] Step 4: Displaying the Crisis Index

[1626] Device:

[1627] The user launches the app.

[1628] The app sends a request to the server to get the latest Global Warming Risk Index.

[1629] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[1630] Step 5: Collect user data

[1631] User:

[1632] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[1633] The smart device and app work together to automatically collect daily behavior data (location information, number of steps, energy consumption, etc.).

[1634] Step 6: Sending data

[1635] Device:

[1636] The collected user data is sent from the device to a server.

[1637] Step 7: Data analysis

[1638] server:

[1639] The server analyzes the collected data and evaluates the user's eco-friendly behavior.

[1640] The data for one week is compiled and the effect of eco-friendly behavior is calculated (e.g., reducing carbon footprint by 35% more than usual).

[1641] Step 8: Feedback Generation

[1642] server:

[1643] The server generates user feedback based on the analysis (e.g., "You reduced your carbon footprint by 35% this week").

[1644] Step 9: Notification of feedback

[1645] Device:

[1646] The feedback message sent from the server is displayed on the terminal.

[1647] The user can review the feedback.

[1648] Step 10: Awarding reward points

[1649] server:

[1650] The server awards points to users for their eco-friendly behavior.

[1651] The accumulated points are stored in a database.

[1652] Step 11: Set up and notify rewards

[1653] server:

[1654] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[1655] Device:

[1656] The device notifies the user that a reward is available (e.g., "You've collected 100 points! New rewards are available.").

[1657] Step 12: Use your rewards

[1658] User:

[1659] Users can check the reward details and select and use them through the app.

[1660] Example 1

[1661] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1662] In modern society, there is a lack of concrete mechanisms to grasp the impact of global warming in real time and to encourage people to be eco-friendly. There is also a need for a system that allows users to instantly recognize the extent to which their daily actions contribute to the environment and receive feedback and appropriate rewards for their actions.

[1663] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1664] In this invention, the server includes means for acquiring environmental information in real time, means for calculating a global warming impact index by comparing it with past environmental information, means for displaying the calculated global warming impact index on a user interface, means for collecting end-user daily behavior data, means for analyzing end-user behavior based on the collected data and generating feedback, means for notifying the user interface of the generated feedback, and means for providing rewards to end-users for their environmental protection behavior, thereby enabling users to recognize the impact of global warming in real time, immediately understand the effects of their own eco-friendly behavior, and receive appropriate feedback and rewards.

[1665] "Real-time" refers to data and information being processed and displayed immediately at the moment it is acquired or updated.

[1666] "Environmental information" refers to data related to the natural environment, such as temperature, humidity, precipitation, and wind speed.

[1667] The "global warming impact index" refers to an indicator that numerically shows the progress and impact of global warming based on past and present environmental data.

[1668] "User interface" refers to the display screen and operating means used to exchange information and instructions between the user and the system.

[1669] "End User" refers to the final user who directly uses a system or application.

[1670] "Daily Behavioral Data" refers to data relating to the End User's daily activities and habits, such as commuting method and energy consumption.

[1671] "Feedback" refers to information provided by a system that evaluates and comments on a user's actions, allowing the user to understand the results and impact of those actions.

[1672] "Rewards" refers to rewards or incentives provided to users for specific actions or achievements, including, for example, points or discount coupons.

[1673] This invention is a system that uses real-time environmental information to show users the effects of global warming and encourage eco-friendly behavior. This system consists of three main components: a server, a terminal, and a user. The specific operation of each component is described below.

[1674] Obtaining and displaying the global warming impact index

[1675] server:

[1676] The server obtains real-time environmental information from weather information providers, such as online services like WeatherAPI and OpenWeatherMap.

[1677] The server stores the acquired environmental information in a database, typically a relational database such as MySQL or PostgreSQL.

[1678] The server compares data from the past 30 years with current data and calculates a global warming impact index using Python's NumPy library.

[1679] The calculated global warming impact index is stored in a database and updated as needed.

[1680] Device:

[1681] When a user launches the app, the device sends a request to the server to obtain the latest global warming impact index, using a RESTful API.

[1682] The device displays the obtained index to the user. For example, a notification such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years" is displayed. React Native is used for the display.

[1683] User Data Collection and Feedback

[1684] Device:

[1685] Users record their daily eco-friendly actions within the app, such as "I cycled to work today."

[1686] The device connects to smart devices (e.g., smartwatches and energy consumption meters) and automatically collects user behavior data. This connection uses the Google Fit API and HealthKit.

[1687] server:

[1688] The server receives user behavior data sent from the device and stores it in a database. The received data is analyzed and aggregated for one week. This aggregation is performed using Python libraries such as Pandas and Scikit-learn.

[1689] The server generates a feedback message based on the analysis results, such as "This week you reduced your carbon footprint by 35% more than usual."

[1690] Device:

[1691] Feedback messages sent from the server are sent to the device. Firebase Cloud Messaging is used for notifications, allowing users to understand the effects of their actions in real time.

[1692] Implementing a reward system

[1693] server:

[1694] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management.

[1695] The server sets rewards when a user accumulates a certain number of points, and provides discount coupons for eco-friendly products when the user accumulates 100 points, for example.

[1696] Device:

[1697] The device will then notify the user that they have earned a reward. They will receive a notification saying, "You have earned 100 points. New rewards are available."

[1698] Users can check reward details and use the rewards they select through the app. React Native is also used to display and operate reward information.

[1699] Specific examples

[1700] 1. Obtaining and displaying the Global Warming Impact Index:

[1701] Every day, the server obtains environmental information for Tokyo from a weather information service and stores the new data in a database.

[1702] The server compares temperature data from the past 30 years with the most recent data and calculates that this week's average temperature is 5% higher.

[1703] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1704] 2. User Data Collection and Feedback:

[1705] The user records in the app, "I went on an eco-walk today."

[1706] The device sends the data to the server, which analyzes one week's worth of data.

[1707] The server evaluates the user's behavior and generates feedback such as, "You reduced your carbon footprint by 35% this week."

[1708] A feedback message is sent to the user's terminal.

[1709] 3. Implementing a reward system:

[1710] The server accumulates points for users' eco-friendly behavior.

[1711] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[1712] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[1713] Users select and redeem rewards through the app.

[1714] Prompt Sentence Examples

[1715] "Write a Python program that compares weather data from the past 30 years with current data and calculates the progress of global warming."

[1716] By inputting this prompt into a generative AI model, the corresponding Python code is generated.

[1717] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1718] Step 1:

[1719] server:

[1720] Every day, the server retrieves the latest environmental information from the environmental information service via the network. Specifically, it uses WeatherAPI or OpenWeatherMap to send an HTTP request. The retrieved data is in JSON format and includes information such as temperature, humidity, precipitation, and wind speed. The input is geographic location information and an API key, and the output is the latest environmental information data. This data is parsed and stored in a database.

[1721] Step 2:

[1722] server:

[1723] The server retrieves stored environmental information and environmental data for the past 30 years. The server uses MySQL or PostgreSQL to execute SQL queries to retrieve historical temperature data. The input is an SQL query for temperature data for the past 30 years, and the output is the retrieved historical temperature data. This data is processed using the NumPy library to calculate the average historical temperature.

[1724] Step 3:

[1725] server:

[1726] The acquired current environmental information is compared with historical average temperature data to calculate a global warming impact index. This calculation uses the NumPy library and shows the difference between the historical average temperature and the current temperature as a percentage. The input is the current temperature data and the historical average temperature data, and the output is the calculated global warming impact index. This index is saved in a database.

[1727] Step 4:

[1728] Device:

[1729] When a user launches the app, the device sends an HTTP GET request to the server to retrieve the latest global warming impact index. The input is the user request, and the output is the latest global warming impact index. The device parses this and displays it in an easy-to-understand format for the user. Specifically, it uses React Native to display a message such as "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1730] Step 5:

[1731] User:

[1732] Users record their daily eco-friendly actions within the app. For example, they can enter, "I cycled to work today." If a smartwatch or energy consumption meter is connected, the device automatically collects data using the Google Fit API or HealthKit. The input is the user's behavioral data or data from the smart device, and the output is the data recorded within the app.

[1733] Step 6:

[1734] Device:

[1735] The device sends the collected user behavior data to the server. The input is the collected behavior data, and the output is an HTTP POST request to the server. The server receives this and stores it in a database.

[1736] Step 7:

[1737] server:

[1738] The server analyzes the received behavioral data and aggregates the data for one week. The data is aggregated using Python's Pandas library. The input is the user's behavioral data for one week, and the output is the aggregated results. Based on this result, the server evaluates the user's eco-friendly behavior and generates a feedback message.

[1739] Step 8:

[1740] Device:

[1741] The generated feedback message is sent to the user's device using Firebase Cloud Messaging. The input is the feedback message sent from the server, and the output is the notification sent to the user's device. The user can understand the effect of their actions in real time.

[1742] Step 9:

[1743] server:

[1744] The server assigns and accumulates points for users' eco-friendly behavior. Redis is used for point management. The input is user behavior data and point calculation logic, and the output is the accumulated points. When a certain number of points is reached, a reward is set, such as a discount coupon for an eco-friendly product.

[1745] Step 10:

[1746] Device:

[1747] The device will send a notification to the user that they have earned a reward. The notification will say, "You have earned 100 points. A new reward is available." The input is the reward notification sent from the server, and the output is the notification to the device. The user can check the reward details through the app and use the selected reward.

[1748] (Application example 1)

[1749] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1750] Currently, there are systems that detect the effects of global warming in real time and promote eco-friendly behavior based on that information, but there are not enough ways for users to specifically incorporate these systems into their lives or to immediately feel the effects. As a result, there is a problem that users' environmental awareness and actions are not aligned. There is also a lack of ways to clearly show the extent to which purchasing behavior in a virtual environment contributes to the environment.

[1751] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1752] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting users' daily behavior data, means for analyzing users' behavior based on the collected data and generating feedback, means for notifying the user device of the generated feedback, means for providing rewards for users' environmental protection behavior, and means for encouraging users to purchase products in the virtual environment using the global warming risk index. This allows users to grasp the effects of global warming in real time, making it easier for them to immediately understand the impact of their actions on the environment, and also encouraging them to purchase products in the virtual environment.

[1753] The "means for obtaining weather data in real time" refers to a means for obtaining the latest weather data from a weather information service via the Internet.

[1754] The "means of calculating the global warming risk index by comparing it with past weather data" is a means of comparing the latest weather data with weather data from the past 30 years, and as a result, expressing the degree of progress of global warming in numerical terms.

[1755] The "means for displaying the calculated global warming risk index on a user device" refers to a means for visually displaying the calculated index on a device used by a user.

[1756] The "means for collecting data on the user's daily activities" refers to a means for recording or automatically acquiring the eco-friendly activities that the user performs on a daily basis.

[1757] "Means for analyzing user behavior based on collected data and generating feedback" refers to means for analyzing collected user data, evaluating user behavior based on the analysis results, and generating specific feedback.

[1758] The "means for notifying the user device of the generated feedback" refers to a means for notifying the user device of the content of the generated feedback.

[1759] The "means for providing rewards for users' environmental protection actions" refers to a means for giving rewards such as points or discount coupons to users for eco-friendly actions they take.

[1760] "Means for promoting product purchasing behavior of users within a virtual environment using the global warming risk index" refers to means for motivating users to purchase eco-friendly products within a virtual environment using the calculated global warming risk index.

[1761] This invention is a system that detects the effects of global warming in real time and encourages users to behave eco-friendly. It mainly consists of three elements: a server, a terminal, and a user. The specific operation and technical details of this system are as follows:

[1762] Obtaining and displaying the Global Warming Risk Index

[1763] server:

[1764] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it accesses a weather information API to obtain current temperature data. The obtained data is stored in a database and compared with weather data from the past 30 years. The results of this comparison are used to calculate a global warming risk index. This index quantifies the difference between past average temperature data and current data, and quantitatively indicates the progress of global warming. The calculated index is updated in the database as needed.

[1765] Device:

[1766] When a user launches the application, the device sends a request to the server to retrieve the latest global warming risk index. The device then displays the index and provides a specific message to the user, such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years."

[1767] User Data Collection and Feedback

[1768] Device:

[1769] Users record their daily eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." It is also possible to link the app with smart devices such as smartwatches and energy consumption meters to automatically collect user behavior data.

[1770] server:

[1771] The server receives and analyzes user behavior data sent from the device. Based on the collected data, it evaluates the user's behavior over the course of a week and generates a feedback message. For example, it could evaluate the user's behavior by saying, "This week, you reduced your carbon footprint by 35% more than usual."

[1772] Device:

[1773] The server notifies the user of the feedback messages sent from the server, allowing the user to grasp the specific effects of their actions in real time.

[1774] Implementing a reward system

[1775] server:

[1776] The server will also implement a points system that rewards users for their eco-friendly behavior. When a certain number of points are accumulated, specific rewards can be set, such as discount coupons for eco-friendly products.

[1777] Device:

[1778] The device will notify the user when they have accumulated points and provide information to enable them to view, select, and use reward details. For example, a notification such as "You have accumulated 100 points. New rewards are available."

[1779] Application in virtual stores

[1780] server:

[1781] The server uses the calculated global warming risk index to encourage users to purchase eco-friendly products in the virtual environment. Based on this index, the server specifically indicates how much the product selected by the user contributes to preventing global warming.

[1782] Device:

[1783] When users purchase eco-friendly products displayed in the virtual store, the device compares the impact with real-time weather data. For example, it provides a message such as, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years, but by choosing this water bottle, you can reduce CO2 emissions by approximately 20 kg over the course of a year."

[1784] Example prompts for generative AI models

[1785] An example prompt is:

[1786] Obtain real-time weather data for Tokyo and compare it with the past 30 years to calculate a global warming risk index. Based on the obtained index, provide a message that informs users about the impact of purchasing eco-friendly products. For example, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years. By choosing a reusable water bottle, you can reduce CO2 emissions by approximately 20 kg per year."

[1787] Overall, the system allows users to feel the effects of global warming in real time and proactively incorporate eco-friendly behaviors into their lives. Product selection within the virtual environment will also be further encouraged by clearly showing the tangible contribution to environmental conservation.

[1788] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1789] Step 1:

[1790] The server obtains real-time weather data from a weather information service via the Internet. Specifically, it sends an API request to obtain current temperature data. At this time, the server receives the response data from the weather information service and parses it in a format such as JSON. The input is the response data from the weather information API, and the output is the parsed current temperature data.

[1791] Step 2:

[1792] The server retrieves weather data from the past 30 years and the latest weather data from the database, compares them, and calculates the Global Warming Crisis Index. Specifically, it calculates the temperature increase rate using the latest temperature data and historical average temperature data. In this case, the server receives current temperature data and data from the past 30 years as arguments, and outputs the Global Warming Crisis Index as the calculation result. The input is the latest temperature data and historical temperature data, and the output is the Global Warming Crisis Index.

[1793] Step 3:

[1794] The server stores the calculated Global Warming Crisis Index in a database and updates it as needed. Specifically, it inserts or updates the Global Warming Crisis Index into the database as a new record. The input is the calculation result of the Global Warming Crisis Index, and the output is the updated state of the database.

[1795] Step 4:

[1796] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index. Specifically, it sends an HTTP request to obtain the latest Global Warming Risk Index from the server. The input is the HTTP request, and the output is the obtained Global Warming Risk Index.

[1797] Step 5:

[1798] The device displays the acquired Global Warming Risk Index to the user. Specifically, it updates the UI component that visually displays the acquired value. For example, it displays a message on the screen saying, "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years." The input is the acquired Global Warming Risk Index, and the output is the displayed message.

[1799] Step 6:

[1800] Users record their daily eco-friendly actions in the app. Specifically, users enter their actions into a form within the app, and the app saves the data. The input is the action data entered by the user, and the output is the saved action data.

[1801] Step 7:

[1802] The terminal works in conjunction with smart devices to automatically collect user behavioral data. Specifically, it acquires data from devices such as smartwatches and energy consumption meters and sends it to the app. The input is behavioral data from the smart device, and the output is behavioral data recorded in the app.

[1803] Step 8:

[1804] The server receives and analyzes the collected user behavior data. Specifically, it retrieves the behavior data from the database and performs statistical analysis to evaluate the user's eco-friendly behavior. The input is the behavior data, and the output is the evaluation result.

[1805] Step 9:

[1806] The server generates a feedback message based on the analysis results. Specifically, it uses a generative AI model to generate a feedback message for the user's actions. The input is the analysis results, and the output is the feedback message.

[1807] Step 10:

[1808] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the feedback message, and the output is the message sent to the user's terminal.

[1809] Step 11:

[1810] The terminal notifies the user of the feedback message by displaying the message to the user using a notification function. The input is the received feedback message, and the output is the displayed notification.

[1811] Step 12:

[1812] The server implements a system that awards points to users for their eco-friendly behavior. Specifically, it calculates points based on user behavior data and stores them in a database. The input is the user behavior data, and the output is the awarded points.

[1813] Step 13:

[1814] The server sets a reward for users who have accumulated 100 points and registers it in the database. Specifically, it sets a reward such as a discount coupon for eco-friendly products for users who have reached a certain number of points. The input is the user's accumulated points, and the output is the set reward.

[1815] Step 14:

[1816] The terminal sends a notification to the user that a reward has been earned. Specifically, it notifies the user with a message that a reward is available. The input is the set reward, and the output is the sent notification.

[1817] Step 15:

[1818] Within the virtual store system, the terminal presents the user with the impact of purchasing a product based on the Global Warming Risk Index. Specifically, when a user purchases a reusable product, the terminal displays in real time the extent to which that purchase will contribute to preventing global warming. The input is the Global Warming Risk Index and product information, and the output is the impact information presented to the user.

[1819] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1820] This system calculates the impact of global warming based on real-time and historical weather data and displays the results on a user's device. It also collects and analyzes the user's daily behavior and emotional state, generating feedback and rewarding the user for their eco-friendly behavior. The specific operation of each component and their interaction are described below.

[1821] Obtaining and displaying the Global Warming Risk Index

[1822] server:

[1823] The server periodically obtains real-time weather data from the weather information service API via the Internet.

[1824] The acquired data is stored in a database.

[1825] The server compares weather data from the past 30 years with the latest data to calculate the global warming risk index.

[1826] The calculated results are stored in a database.

[1827] Device:

[1828] When a user launches the app, the device sends a request to the server to get the latest Global Warming Risk Index.

[1829] The obtained index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[1830] User Data Collection and Feedback

[1831] Device:

[1832] Users record eco-friendly actions in the app, for example, by typing, "I cycled to work today."

[1833] By linking the smart device with the app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[1834] Additionally, an emotion engine is used to collect the user's emotional state (e.g., happy, sad, excited, etc.), which includes voice analysis, text input analysis, and camera-based facial expression analysis.

[1835] server:

[1836] The collected user data and emotion data are sent to the server.

[1837] The server analyzes the received data and evaluates the user's eco-friendly behavior, taking into account emotional data, such as how the user felt about a particular behavior.

[1838] Generate feedback messages based on the analysis, including personalized feedback based on emotional data (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive mood you experience while cycling to work.").

[1839] A feedback message is sent to the user's terminal.

[1840] Device:

[1841] Users can check the feedback messages in the app.

[1842] Implementing a reward system

[1843] server:

[1844] The server implements a system that gives and accumulates points for users' eco-friendly actions.

[1845] When a user accumulates a certain number of points, a reward (e.g., a discount coupon for an eco-product) is set.

[1846] Device:

[1847] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[1848] Users can check the reward details and select and use them through the app.

[1849] Specific examples

[1850] 1. Obtaining and displaying the Global Warming Risk Index

[1851] The server retrieves weather data for Tokyo from the weather information service and stores the new data in a database.

[1852] The server compares temperature data from the past 30 years with the latest data and calculates that this week's average temperature is 5% higher.

[1853] When a user launches the app, the index is displayed on the device, showing that "the average temperature in Tokyo this week is 5% higher than the average over the past 30 years."

[1854] 2. User Data Collection and Feedback

[1855] The user records in the app, "I went on an eco-walk today," and the device sends that data to the server.

[1856] Furthermore, the emotion engine analyzes the user's facial expression to detect the emotion of "joy" and transmits this to the server.

[1857] The server analyzes a week's worth of data and generates feedback such as, "This week you reduced your carbon footprint by 35% and felt more positive while cycling to work."

[1858] The feedback is displayed on the terminal for the user to review.

[1859] 3. Implementing a reward system

[1860] The server accumulates points for users' eco-friendly behavior.

[1861] The server sets a discount coupon for eco-products for users who have accumulated 100 points.

[1862] The device notifies the user, "You have accumulated 100 points. New rewards are available."

[1863] Users can select and redeem rewards through the app.

[1864] The above is a concrete example of how the present invention can be implemented. This system allows users to feel the effects of global warming in real time, and by utilizing emotion data, users can more effectively incorporate eco-friendly behaviors into their daily lives.

[1865] The processing flow will be explained below.

[1866] Step 1: Obtaining weather data

[1867] server:

[1868] The server sends a request to the weather information service API via the Internet to obtain real-time weather data.

[1869] The acquired data includes items such as temperature, humidity, and precipitation, and is stored in a database.

[1870] Step 2: Obtain and compare historical data

[1871] server:

[1872] The server retrieves weather data from the database for the past 30 years.

[1873] The latest weather data obtained is compared with past average temperature data to calculate the global warming risk index.

[1874] The calculated global warming risk index is stored in a database.

[1875] Step 3: Displaying the Crisis Index

[1876] Device:

[1877] When a user launches the app, the device sends a request to the server to obtain the latest Global Warming Risk Index.

[1878] The obtained global warming risk index is displayed to the user (e.g., "The average temperature in Tokyo this week is 5% higher than the average over the past 30 years").

[1879] Step 4: Collect user data

[1880] User:

[1881] Users input eco-friendly actions into the app (e.g., "I cycled to work today").

[1882] By connecting the smart device and app, daily behavior data (location information, number of steps, energy consumption, etc.) is automatically collected.

[1883] Step 5: Collecting sentiment data

[1884] Device:

[1885] The app's built-in emotion engine recognizes the user's emotional state through user input, the camera, and the microphone. For example, it can determine whether the user is having fun through voice analysis, or recognize emotions by analyzing facial expressions via the camera.

[1886] The collected emotion data is sent to the server.

[1887] Step 6: Sending data

[1888] Device:

[1889] The collected daily behavior data and emotion data are sent to a server.

[1890] Step 7: Data analysis and feedback generation

[1891] server:

[1892] The server analyzes the received user data and evaluates the impact of eco-friendly actions, while also considering emotional data to analyze how the user felt about each action.

[1893] Generate feedback messages based on the analysis results (e.g., "You reduced your carbon footprint by 35% this week. Additionally, we've confirmed the positive feelings you get from cycling to work.").

[1894] A feedback message is sent to the user's terminal.

[1895] Step 8: Notification of feedback

[1896] Device:

[1897] The terminal notifies the user of the feedback message sent from the server.

[1898] Users can view feedback in the app.

[1899] Step 9: Reward Points Awarded

[1900] server:

[1901] The server awards points to users for their eco-friendly behavior.

[1902] When points reach a certain threshold, a reward (e.g., a discount coupon for an eco-friendly product) is set.

[1903] Step 10: Notification and Redemption of Rewards

[1904] Device:

[1905] The device will notify the user that they have earned a reward (e.g., "You've earned 100 points! New rewards are available!").

[1906] User:

[1907] Users can check the reward details and select and use them through the app.

[1908] The above is the processing flow in the embodiment of the invention. This flow allows users to feel the effects of global warming in real time and continuously take eco-friendly actions, including their emotions.

[1909] Example 2

[1910] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1911] Global warming has become a serious problem in modern society, and people need to feel its effects on a daily basis. Furthermore, there is a need for a method to visualize the environmental impact of individual actions and promote sustainable behavior. However, there is no comprehensive system available that collects real-time weather data, compares it with past data, collects and analyzes users' daily behavior data, and understands their emotional state. This makes the process of users accurately evaluating their eco-friendly behavior and receiving rewards complicated, making effective efforts difficult.

[1912] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1913] In this invention, the server includes means for acquiring weather data in real time, means for calculating a global warming risk index by comparing it with past weather data, means for displaying the calculated global warming risk index on the user device, means for collecting data on the user's daily behavior, means for analyzing the user's behavior based on the collected data and emotional state data and generating feedback, means for notifying the user device of the generated feedback, and means for providing rewards for the user's eco-friendly behavior, thereby enabling the user to grasp the effects of global warming in real time, have their eco-friendly behavior accurately evaluated, and be encouraged to be more environmentally friendly through rewards.

[1914] "Weather data" is a general term for information related to weather conditions, such as temperature, humidity, precipitation, and wind speed.

[1915] "Real-time" refers to conditions and data that are immediately available at the present time.

[1916] "Historical weather data" includes records of information relating to observed weather conditions over a particular period of time.

[1917] The Global Warming Risk Index is an index that evaluates the impact of global warming by comparing past weather data with the latest weather data.

[1918] "User device" is a general term for electronic devices used by users to receive and display information, such as smartphones, tablets, and PCs.

[1919] The "daily behavior data" includes information related to the user's daily activities, such as data on transportation methods and lifestyle habits.

[1920] "Emotional state data" is data that indicates the user's emotional state, and includes the results of analyzing emotions such as joy, sadness, and excitement.

[1921] "Analysis" is the act of evaluating the characteristics and trends of collected data.

[1922] "Feedback" refers to advice and evaluation given to users based on collected and analyzed data.

[1923] "Eco-friendly behavior" is a general term for actions that are considered environmentally friendly, including recycling, reusing, saving electricity, and saving energy.

[1924] "Rewards" refers to incentives or benefits provided to users for specific actions or achievements.

[1925] "Via the Internet" refers to a method of sending and receiving data using the Internet.

[1926] "Weather information provision service" refers to a service or system that provides weather-related data.

[1927] "Smart devices" are terminal devices that can connect to the Internet, such as smartphones, smartwatches, and fitness trackers.

[1928] "Emotion engine" is a general term for algorithms and systems that analyze emotions from a user's voice, facial expressions, text, etc.

[1929] This system acquires weather data in real time, compares it with past weather data to calculate a global warming risk index, and collects and analyzes the user's daily behavior data and emotional state to generate feedback and provide rewards for eco-friendly behavior. The specific operation of each component and how they work together are explained below.

[1930] Obtaining and displaying the Global Warming Risk Index

[1931] Server behavior:

[1932] The server periodically obtains real-time weather data via the internet using the API of a weather information service. For example, a timer can be set to send a request to the API at midnight every day. The obtained weather data is stored in a database. The server then retrieves weather data from the database for the past 30 years, compares it with the latest data, and runs an algorithm to calculate the global warming risk index. The calculated index is then stored back in the database.

[1933] Terminal behavior:

[1934] When a user launches the app on their device, the device sends a request to the server to obtain the latest global warming risk index. The obtained index is displayed to the user. For example, the display might say, "This week's average temperature in Tokyo is 5% higher than the average over the past 30 years."

[1935] Collecting user data and generating feedback

[1936] Terminal behavior:

[1937] Users can record their eco-friendly actions in the app. For example, they can enter, "Today I commuted to work by bicycle." By linking their smart device with the app, daily activity data (location information, number of steps, energy consumption, etc.) is automatically collected. Furthermore, an emotion engine uses voice and camera data to analyze the user's emotional state (e.g., joy, sadness, excitement, etc.). This includes voice analysis, text input analysis, and facial expression analysis using a camera.

[1938] Server behavior:

[1939] The server receives the collected data and emotional state data sent from the device and analyzes them. A specific algorithm is used for the analysis to evaluate the user's eco-friendly behavior, taking their emotional state into account in the process. For example, the server may evaluate the user as having a positive mood while commuting by bicycle. Based on the analysis results, a personalized feedback message is generated. For example, a feedback message such as "You have reduced your carbon footprint by 35% this week. In addition, your positive mood while commuting by bicycle has been confirmed" is generated and sent to the user's device.

[1940] Terminal behavior:

[1941] Users can check feedback messages through the app.

[1942] Implementing a reward system

[1943] Server behavior:

[1944] The server implements a system that awards and accumulates points for users' eco-friendly behavior. For example, "10 points for commuting by bicycle." When a certain number of points are accumulated, for example, 100 points, the user is offered a discount coupon for an eco-friendly product.

[1945] Terminal behavior:

[1946] The device will send a notification to the user informing them that a reward is available. For example, a notification saying "You have accumulated 100 points. New rewards are available." The user can check the reward details through the app and select and use the desired reward.

[1947] The above is a specific embodiment for carrying out the present invention. This system allows users to understand the impact of global warming in real time, have their eco-friendly behavior evaluated, and encourage further environmentally friendly behavior through rewards.

[1948] Prompt Sentence Examples

[1949] "I went on an eco-walk today."

[1950] "This month's carbon footprint is lower than last month's."

[1951] "I'd like to use a discount coupon for a new eco-friendly product."

[1952] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1953] Obtaining and displaying the Global Warming Risk Index

[1954] Step 1: Regularly acquire weather data

[1955] Server operation: The server periodically sets a timer and sends a request to the weather information service API. For example, the server calls the API every day at midnight.

[1956] Input: Timer event, API request

[1957] Output: Real-time weather data (e.g. temperature, humidity, precipitation)

[1958] What happens: The server authenticates using the API key and retrieves weather data for the specified region.

[1959] Step 2: Save your data

[1960] Server operation: Save the acquired weather data in a database.

[1961] Input: Weather data (e.g., JSON format)

[1962] Output: Weather data stored in a database

[1963] What it does: Adds a new record to the database, storing the date and time of acquisition and regional details.

[1964] Step 3: Calculate the Global Warming Risk Index

[1965] Server operation: Compares weather data from the past 30 years with the latest data and calculates the Global Warming Risk Index.

[1966] Input: Weather data from the past 30 years and the latest data obtained from the database

[1967] Output: Calculated Global Warming Risk Index

[1968] What it does: It runs an algorithm to calculate the rate of change in average temperature, for example, comparing the average temperature of the most recent year with the average temperature of the past 30 years and calculating the difference as a percentage.

[1969] Step 4: User retrieves and displays the index

[1970] What happens on the device: When a user launches the app, it sends a request to the server to get the latest Global Warming Risk Index.

[1971] Input: User action (launching the app)

[1972] Output: Obtained Global Warming Risk Index

[1973] Specific behavior: Parse the data sent as a response from the server and display it in the app interface. For example, it might say, "This week's average temperature in Tokyo is 5% higher than the average for the past 30 years."

[1974] Collecting user data and generating feedback

[1975] Step 1: Recording user behavior data

[1976] Device Action: The user types "I biked to work today" in the app.

[1977] Input: User action input (text format)

[1978] Output: Behavioral data stored in the app

[1979] Specific operation: Saves the entered text data in an internal database.

[1980] Step 2: Collecting emotion data

[1981] Device operation: The emotion engine analyzes audio data and camera footage to detect the user's emotional state.

[1982] Input: Audio data, camera footage

[1983] Output: Detected emotion data (e.g., happy, sad)

[1984] What it does: Implements voice and facial expression analysis algorithms to classify and detect emotional states, such as detecting joy from the user's voice tone.

[1985] Step 3: Send data to the server

[1986] Device operation: The collected user behavior data and emotion data are sent to the server as an HTTP request.

[1987] Input: User behavior data, emotion data

[1988] Output: Data sent to the server

[1989] What it does: Formats an HTTP request and sends it to the server with the required data as a payload.

[1990] Step 4: Data analysis and feedback generation

[1991] Server operation: The server analyzes the received data and evaluates the user's eco-friendly behavior.

[1992] Input: Submitted user behavior and emotion data

[1993] Output: Analysis results and feedback messages

[1994] What it does: Runs analytical algorithms and evaluates user behavior based on specific metrics. For example, calculates the carbon footprint reduction percentage over the week. Generates and customizes feedback messages. For example, "You've reduced your carbon footprint by 35% this week. What's more, cycling to work has confirmed your positive mood."

[1995] Step 5: View your feedback

[1996] Device Action: Display a feedback message in the user's app.

[1997] Input: Feedback message received from the server

[1998] Output: Feedback message displayed in the app

[1999] Specific behavior: Parse the feedback message and display it in the user interface. For example, display something like "This week's evaluation results are..."

[2000] Implementing a reward system

[2001] Step 1: Earn points for eco-friendly actions

[2002] Server operation: Executes the logic to award points to users for their daily eco-friendly actions.

[2003] Input: User behavior data and analysis results

[2004] Output: Points awarded

[2005] Specific operation: Points are calculated based on user behavior and stored in a database. For example, "10 points for each bicycle commute."

[2006] Step 2: Reward Settings and Notifications

[2007] Server operation: Monitors the point accumulation status and sets rewards for users who have accumulated a certain number of points.

[2008] Input: User's accumulated points

[2009] Output: The configured reward and notification message.

[2010] Specific behavior: When the user's points reach 100, provide a discount coupon for eco-friendly products. Generate and send a notification message to the user. Example: "You've accumulated 100 points. New rewards are available."

[2011] Step 3: Earn and use your rewards

[2012] Device operation: The user checks the reward details through the app and selects and uses the reward they want.

[2013] Input: User action (reward selection)

[2014] Output: Rewards used

[2015] Specific behavior: Display the reward details and select the reward you want. For example, use the reward by pressing the "Use eco product discount coupon" button.

[2016] (Application example 2)

[2017] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2018] While factories are required to optimize energy consumption and reduce their environmental impact, conventional systems have difficulty analyzing environmental impacts using real-time and historical weather data and suggesting specific eco-friendly actions to factory managers. Furthermore, there is no reward system for eco-friendly actions, which means that factory managers are not sufficiently motivated.

[2019] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2020] In this invention, the server includes a means for acquiring weather data in real time, a means for calculating a global warming risk index by comparing it with past weather data, and a means for collecting and analyzing energy consumption data in the factory, thereby enabling optimization of energy consumption in the factory and reduction of environmental load.

[2021] "Means for obtaining weather data in real time" refers to a function for obtaining the latest weather data from a weather information service via the Internet.

[2022] "Means for calculating the global warming risk index by comparing with past weather data" is a function that quantifies the impact of global warming by comparing past weather data with current weather data.

[2023] The "means for displaying the calculated global warming crisis index on the user device" is a function for visually conveying the calculated global warming crisis index on the user's device.

[2024] The "means for collecting data on daily user behavior" is a function for collecting information on daily user behavior.

[2025] The "means for analyzing user behavior based on collected data and generating feedback" is a function that analyzes the user's daily behavior data and creates feedback based on the results.

[2026] The "means for notifying the user device of the generated feedback" is a function for notifying the user device of the generated feedback.

[2027] The "means for collecting and analyzing energy consumption data within the factory" is a function for collecting and analyzing data on energy use within the factory.

[2028] "Means of proposing eco-friendly actions to factory managers based on analysis results" is a function that suggests environmentally friendly actions to factory managers based on the analysis results of collected data.

[2029] The "means for providing rewards for eco-friendly behavior of factory managers" is a function that provides rewards when factory managers behave in an environmentally friendly manner.

[2030] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Each element functions using specific hardware and software. Below, we will explain how each element works together to realize the invention.

[2031] Server Features

[2032] The server implements the following methods:

[2033] 1. Means of obtaining weather data in real time: The server periodically obtains the latest weather data from weather information services via the Internet, thereby ensuring that the latest weather information is always available.

[2034] 2. Means for calculating the Global Warming Risk Index by comparing with past weather data: The server compares weather data from the past 30 years with current weather data and calculates the Global Warming Risk Index. This calculation uses a high-performance database and analytical algorithms.

[2035] 3. Collecting and analyzing energy consumption data within the factory: Energy consumption data is collected from sensors and smart devices installed within the factory and analyzed using machine learning models.

[2036] Device Features

[2037] The terminal acts as a user device and implements the following:

[2038] 1. A means for displaying the calculated Global Warming Risk Index on the user device: The Global Warming Risk Index obtained from the server is visually displayed to the user. Specifically, a message such as "The average temperature in Tokyo this week is 5% higher than the average for the past 30 years" is displayed on the screen of a smartphone or tablet.

[2039] 2. Means of collecting user's daily behavior data: Automatically collect user's daily behavior data from smart devices, including location information, number of steps, energy consumption, etc.

[2040] 3. Means of notifying the user device of the generated feedback: The feedback generated by the server is notified to the user device, for example, by displaying a message such as "You reduced your carbon footprint by 35% this week. In addition, we have confirmed the positive feelings you get from commuting by bicycle."

[2041] User Involvement

[2042] Users provide their daily behavior data to the system. For example, by carrying a smartphone, location information and step counts are automatically collected. Furthermore, by practicing eco-friendly behavior, users can receive rewards from the system.

[2043] Specific examples

[2044] As a specific example of operation, we will explain the case where a factory manager uses an application called "Eco Manager for Factory Robots." Robots in the factory collect energy consumption data in real time and send it to a server. The server compares the acquired weather data with past data and calculates an environmental load index. Based on this, it provides feedback to the factory manager, such as "We recommend the following operations to reduce energy consumption by 10%."

[2045] Example prompt for a generative AI model:

[2046] Calculate the factory's environmental impact index based on weather and energy consumption data and suggest eco-friendly improvements.

[2047] Through these procedures, the system of the present invention can optimize energy consumption in factories and reduce environmental impact.

[2048] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2049] Step 1:

[2050] The server obtains real-time weather data from the weather information service's API via the Internet. It sends an API request as input and receives the latest weather data as output. This data is stored in the server's database. Specifically, it sends an HTTP request using an API key and receives weather data in JSON format as a response.

[2051] Step 2:

[2052] The server retrieves weather data from the past 30 years and compares it with real-time data to calculate the Global Warming Risk Index. It uses past and current weather data as input and obtains the Global Warming Risk Index as output. The server stores the calculation results in a database. Specifically, it sends a query to the historical weather database, compares it with current data to calculate the temperature difference, and then indexes the result.

[2053] Step 3:

[2054] The terminal periodically collects data on the user's daily activities from the smart device to automatically collect it. As input, it takes in sensor information and GPS data from the smart device. As output, it sends the collected daily activity data to the server. Specifically, the terminal periodically acquires data from the wearable device or smartphone and uploads it to the server.

[2055] Step 4:

[2056] The server analyzes the collected daily behavior data and evaluates the user's eco-friendly behavior. It uses the user's daily behavior data as input and generates behavior evaluation results and feedback messages as output. Specifically, it analyzes the data using a machine learning algorithm and scores eco-friendly behavior.

[2057] Step 5:

[2058] The server generates a feedback message based on the analysis results and notifies the user device. It uses the analysis results and a feedback template as input and generates a customized feedback message as output. Specific operations include generating a message such as "You reduced your carbon footprint by 35% this week" and sending a push notification to the user's smartphone.

[2059] Step 6:

[2060] The server collects energy consumption data within the factory and, based on the analysis results, suggests eco-friendly actions to factory managers. Energy consumption data and real-time weather data are used as input, and specific improvement suggestions are generated as output. For example, the server analyzes energy consumption data and generates a suggestion message such as, "To reduce energy consumption by 10%, we recommend the following actions."

[2061] Step 7:

[2062] The server implements a system that rewards factory managers for their eco-friendly behavior. It uses behavioral data and reward conditions as input and generates reward information as output. Specifically, it sets up a points system, sets rewards (e.g., discount coupons for eco-friendly products) when a certain number of points are accumulated, and sends a notification.

[2063] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2064] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2065] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2066] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2067] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2068] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2069] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2070] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain...

Claims

1. a means for obtaining real-time weather data; a means for calculating a global warming risk index by comparing it with historical weather data; means for displaying the calculated Global Warming Risk Index on a user device; A means for collecting daily behavior data of a user; A means for analyzing user behavior based on the collected data and generating feedback; means for notifying a user device of the generated feedback; A means of rewarding users for their eco-friendly behavior A system including:

2. 2. The system of claim 1, wherein the means for obtaining weather data obtains data from a weather information service via the Internet.

3. The system according to claim 1 , further comprising means for automatically collecting daily activity data of a user from a smart device.

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