system

Abstract

Provide a system. [Solution] a terminal means for a user to input daily life information; a server means for receiving input daily life information; a processing means for calculating CO2 emissions and environmental loads based on the received daily life information; a visualization means for visually displaying the calculation results; a suggestion means for generating improvement suggestions for reducing environmental impact; A system including:

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] This is a draft of the "problem the invention aims to solve" and the "means for solving the problem."

[0005] In recent years, environmental problems have become more serious, necessitating the reduction of CO2 emissions and environmental impact. However, there are few concrete systems that allow individual users to recognize the environmental impact of their lifestyles and behaviors in real time and improve their behavior based on that recognition. Existing systems often have problems with complex data entry and result interpretation, making them difficult for users to use. Therefore, the objective of this invention is to provide a system that calculates and visualizes CO2 emissions and environmental impacts caused by individual behaviors in real time, and further generates and presents specific improvement proposals, simply by allowing users to enter information about their daily lives. [Means for solving the problem]

[0006] To solve the above problems, the present invention provides the following means. The system of the present invention includes a terminal means for a user to input daily life information, a server means for receiving the input daily life information, a processing means for calculating CO2 emissions and environmental impact based on the received daily life information, a visualization means for visually displaying the calculation results, and a proposal means for generating improvement proposals for reducing the environmental impact. This system makes it easier for users to intuitively understand the impact of their lifestyle habits on the environment and to implement specific improvement measures. In particular, with regard to transportation, the system provides specific improvement proposals, such as using public transportation, to effectively reduce environmental impact.

[0007] "Users" are individuals or groups who use the system to input information about their daily lives, measure their environmental impact, and receive suggestions for improvements.

[0008] "Terminal means" refers to a device through which a user inputs daily life information, and includes electronic devices such as smartphones, tablets, and computers.

[0009] The "server means" is a central computer system for processing the daily life information received from the terminal means and generating calculation results and suggestions.

[0010] The "processing means" includes programs and algorithms executed within the server means, and has the function of calculating CO2 emissions and environmental loads based on received daily life information.

[0011] A "visualization means" is a method or technique for visually displaying calculation results, and presenting them to the user in the form of graphs, charts, dashboards, etc.

[0012] The "proposal means" has the function of generating and presenting specific improvement proposals to the user to reduce the environmental load based on the calculated environmental load.

[0013] "Daily life information" is information relating to the user's daily activities, the means of transportation used, the products consumed, and so on. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

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

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

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

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This system calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement proposals based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[0036] System Overview

[0037] Users use a device to input information about their daily lives. This device is an electronic device such as a smartphone or tablet. When a user inputs information into the device, the device sends the information to a server. The server processes the received information and sends the calculation results back to the device. The device then visualizes the results and presents them to the user.

[0038] Program processing flow

[0039] 1. Data Entry

[0040] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[0041] 2. Data Transmission

[0042] The device encrypts the information entered and sends it to the server, using a secure protocol such as HTTPS.

[0043] 3. Data Receipt and Processing

[0044] The server receives information about daily life sent by the user, and calculates CO2 emissions and environmental impact based on this information.

[0045] The server also retrieves relevant environmental impact data from existing databases and uses this to perform detailed calculations.

[0046] 4. Visualization of calculation results

[0047] The server generates data for visualizing the calculation results and sends it to the terminal, which can then display them in pie charts, bar graphs, or dashboard formats.

[0048] The terminal displays the received data in a graphical format to the user.

[0049] 5. Improvement proposals and action plans

[0050] Based on the calculation results, the server generates specific improvement suggestions for the user, such as using public transportation or recommending energy-efficient appliances.

[0051] A proposed action plan is also sent to the terminal for the user to review and receive in an actionable form.

[0052] Specific examples

[0053] Enter your commute method

[0054] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[0055] Commuting method: Car

[0056] Commuting distance: 20km

[0057] Fuel economy: 10km / L

[0058] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[0059] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to put their action plans into action.

[0060] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] Users launch a dedicated application on their device and input information about their daily life, such as "I commute 20km by car every day" or "I consume 100kWh of electricity every month."

[0064] Step 2:

[0065] The device encrypts the information entered by the user and sends it to the server via the Internet, using HTTPS as the communication protocol.

[0066] Step 3:

[0067] The server analyzes the received daily life information and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)" from the database.

[0068] Step 4:

[0069] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[0070] Step 5:

[0071] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[0072] Step 6:

[0073] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[0074] Step 7:

[0075] The server generates improvement proposals for reducing the user's CO2 emissions. For example, it generates a proposal such as "Using public transportation can reduce CO2 emissions by 80%."

[0076] Step 8:

[0077] The server creates an action plan based on specific improvement suggestions and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[0078] Step 9:

[0079] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[0080] Through the above steps, users can understand the environmental impact in real time based on their daily life information and implement specific improvement measures.

[0081] Example 1

[0082] 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."

[0083] In modern society, effectively managing and reducing CO2 emissions and environmental impacts is an urgent issue. However, many people lack the means to specifically understand the environmental impact of their daily lives, and it is difficult to receive specific suggestions on how to improve their daily behavior. In addition, existing systems lack sufficient data security, making it difficult to protect user privacy.

[0084] 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.

[0085] In this invention, the server includes a terminal means for a user to input daily life information, a means for encrypting and transmitting the input daily life information, a server means for receiving the transmitted daily life information, a processing means for analyzing the received daily life information and acquiring related data, a calculation means for calculating CO2 emissions and environmental loads based on the received and analyzed daily life information, a visualization means for visually displaying the calculation results, a proposal means for generating improvement proposals to reduce environmental loads based on the calculation results, and a display means for feeding back the generated visualization data and improvement proposals to the user. This makes it possible to provide specific and feasible measures to reduce environmental loads while protecting the user's privacy.

[0086] "User" refers to an individual or group who uses the system to input and check daily life information and implement suggested improvement measures.

[0087] "Terminal means" refers to electronic devices used by users to input daily life information, and includes smartphones, tablets, and the like.

[0088] "Encryption methods" are technologies used to securely convert data entered by users so that it cannot be read by third parties. This includes the AES encryption protocol.

[0089] "Transmission means" refers to a function for securely transferring encrypted data to a server, and includes the HTTPS protocol, etc.

[0090] The "server means" is a central processing unit for receiving and processing data sent from the terminal.

[0091] "Processing means" is a function for analyzing received data and generating calculations and proposals based on the data.

[0092] "Calculation means" refers to a function for numerically calculating CO2 emissions and environmental impact based on daily life information. This includes the Python NumPy library.

[0093] "Visualization methods" are techniques for visually displaying calculation results in an easy-to-understand manner, including pie charts and bar graphs.

[0094] The "proposal means" is a function that allows the user to generate specific and feasible improvement proposals based on the calculation results.

[0095] The "display means" is a function for providing feedback to the user on the generated visualization data and improvement suggestions.

[0096] The present invention is a system that allows users to input daily life information into a terminal, calculates CO2 emissions and environmental impact in real time, and provides improvement proposals based on the information. This system is composed of multiple means including users, terminals, and a server.

[0097] System Overview

[0098] Users launch a dedicated application on a device such as a smartphone or tablet and input specific information about their daily activities (e.g., their daily commute route, the amount of electricity they use, etc.). The device then encrypts this information and sends it to a server using a secure protocol (e.g., HTTPS).

[0099] The server analyzes the received information and calculates CO2 emissions and environmental impact. This calculation uses the Python NumPy library and environmental impact data obtained from an existing database. The calculation results are visualized in the form of pie charts and bar graphs and sent to the terminal, which displays them on a user interface.

[0100] The server then generates specific improvement suggestions for the user based on the calculation results, such as using public transportation or recommending energy-efficient appliances. These improvement suggestions are sent to the device along with additional information such as the nearest bus or train station, timetables, and travel times, and are provided in a form that the user can easily implement.

[0101] Specific examples

[0102] Enter your commute method

[0103] If a user commutes 20km by car each day, they enter the following information into their terminal:

[0104] Commuting method: Car

[0105] Commuting distance: 20km

[0106] Fuel economy: 10km / L

[0107] The device encrypts this information and sends it to a server, which calculates that a 20km commute results in approximately 4.6kg of CO2 emissions per day. The server also calculates monthly and annual total emissions and visualizes them in pie and bar charts before sending them to the device. Users can intuitively view the results in the app.

[0108] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by approximately 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, allowing users to easily implement the suggestions.

[0109] Example prompts for generative AI models

[0110] "I commute 20km by car every day. What is the CO2 emissions in this case? Can you give me some concrete suggestions for reducing my environmental impact?"

[0111] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1: Data entry

[0114] The user starts the dedicated application on the device.

[0115] The user inputs specific daily activities (e.g., daily commuting method, commuting distance, power consumption, etc.) As an input example, the user inputs information for commuting by car, commuting distance of 20 km, and fuel efficiency of 10 km / L.

[0116] Input data:

[0117] Commuting method: Car

[0118] Commuting distance: 20km

[0119] Fuel economy: 10km / L

[0120] Output data:

[0121] The terminal holds the daily life information input by the user.

[0122] Step 2: Send data

[0123] The terminal encrypts the input data using an encryption protocol (e.g., AES).

[0124] The device sends the encrypted data to the server using the HTTPS protocol.

[0125] Input data:

[0126] Daily life information entered by the user

[0127] Output data:

[0128] The encrypted daily life information is sent to a server.

[0129] Step 3: Data Receipt and Decryption

[0130] The server receives the encrypted data sent from the terminal.

[0131] The server decrypts the encrypted data and retrieves the original daily life information.

[0132] Input data:

[0133] Encrypted daily life information

[0134] Output data:

[0135] Decrypted daily life information

[0136] Step 4: Acquire and analyze relevant data

[0137] The server parses the received data.

[0138] The server retrieves relevant data (e.g., CO2 emission coefficient of fuel) from an internal database.

[0139] Input data:

[0140] Decrypted daily life information

[0141] Output data:

[0142] Complete dataset with related data

[0143] Step 5: Calculate CO2 emissions and environmental impact

[0144] The server uses the relevant data to calculate CO2 emissions and environmental impact. For example, if the commute distance is 20km and the fuel efficiency is 10km / L, the amount of gasoline consumed is 2L, which will emit 4.6kg of CO2.

[0145] The server adds the calculation results to the dataset.

[0146] Input data:

[0147] Complete dataset with related data

[0148] Output data:

[0149] Dataset containing calculation results of CO2 emissions and environmental impact

[0150] Step 6: Visualizing the results

[0151] The server generates data (e.g., pie charts, bar chart data points) to visualize the calculated CO2 emissions and environmental impact.

[0152] The server sends the visualization data to the terminal.

[0153] Input data:

[0154] Dataset containing calculation results of CO2 emissions and environmental impact

[0155] Output data:

[0156] Visualized Data

[0157] Step 7: Generate improvement suggestions

[0158] Based on the calculation results, the server generates specific improvement proposals for the user, such as "Using public transportation can reduce CO2 emissions by 80%."

[0159] The server retrieves more detailed data (e.g., bus and train stops, timetables, and travel times) from an external API (e.g., a transportation data provider).

[0160] Input data:

[0161] Calculation results of CO2 emissions and environmental impact

[0162] Additional data from external APIs

[0163] Output data:

[0164] Dataset containing improvement suggestions

[0165] Step 8: User Feedback

[0166] The terminal displays the visualization data and improvement suggestions received from the server on the user interface.

[0167] The user reviews the displayed data and takes action based on the suggested improvements, if necessary.

[0168] Input data:

[0169] A dataset containing visualization data and improvement suggestions

[0170] Output data:

[0171] Information displayed in the user interface

[0172] (Application example 1)

[0173] 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."

[0174] In modern society, it is difficult for individuals to grasp CO2 emissions and environmental impacts in real time and implement appropriate improvement measures based on that information. Existing systems lack the ability to provide effective improvement suggestions based on the daily life information entered by the user, and they rarely provide suggestions that allow users to easily take concrete action. Furthermore, the proposed improvement measures must be specific.

[0175] 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.

[0176] In this invention, the server includes an input device means for a user to input daily life information, a server means for receiving the input daily life information, a data processing means for calculating CO2 emissions and environmental load based on the received daily life information, a data visualization means for visually displaying the calculation results, a proposal generation means for generating improvement proposals to reduce the environmental load, and a generation means for realizing the improvement proposals using a generative AI model. This allows the user to visually grasp the extent to which their lifestyle habits affect the environment and receive specific and actionable improvement proposals.

[0177] "Input device means" refers to a device used by a user to input information about daily life, and includes a smartphone, tablet, etc.

[0178] "Server means" is a computer system for receiving and processing information sent from input device means.

[0179] "Data processing means" refers to a software or hardware configuration for calculating CO2 emissions and environmental loads based on received daily life information.

[0180] "Data visualization means" is a function for visually presenting calculated results to the user, and includes display in pie charts, bar graphs, and dashboard formats.

[0181] "Proposal generation means" refers to software or algorithms that create specific improvement proposals for reducing environmental impact based on the calculation results.

[0182] The "generation means" is a system that uses a generative AI model to concretize improvement suggestions for users, and is a means of providing specific action plans.

[0183] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal improvement suggestions based on user input information.

[0184] "User" refers to an individual who uses this system to input information about their daily life and receive suggestions for improving their CO2 emissions and environmental impact.

[0185] The present invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives, and provides specific improvement proposals based on the calculations. This system is composed of multiple means, including a user, an input device means, and a server means.

[0186] System Overview

[0187] User Operation

[0188] Users input information about their daily lives using input devices such as smartphones and tablets. For example, the user might input information such as "I use a car to commute to work every day." They also input detailed information such as the fuel efficiency of the car they use and the distance they commute to work.

[0189] Data transmission

[0190] The input device means transmits the input information to the server means, using a secure protocol such as HTTPS for this communication to ensure the integrity and security of the data.

[0191] Data Processing

[0192] The server has a data processing unit for calculating CO2 emissions and environmental load based on the received daily life information. Specifically, it uses an algorithm to calculate daily CO2 emissions from distance and fuel efficiency information, and calculates the cumulative value.

[0193] Data Visualization

[0194] The calculated results are presented to the user visually using data visualization tools, such as pie charts, bar graphs, and dashboard-style interfaces, in a way that is intuitively understandable to the user.

[0195] Improvement proposal generation

[0196] The server means also includes a proposal generation means for generating specific improvement proposals for reducing environmental impacts based on the received data and the calculation results. The improvement proposals are further embodied using a generative AI model. The generation means analyzes the user's behavioral patterns and presents optimal improvement measures.

[0197] Specific examples

[0198] For example, suppose a user commutes to work every day by car, travels 20 km per day, and has a fuel efficiency of 10 km / L. This information is sent to the server means, which then calculates the amount of CO2 emissions based on this information. The calculation result shows that the user emits approximately 4 kg of CO2 per day of commuting.

[0199] Furthermore, the server means uses this information to suggest "using public transportation." The generation means provides information on the user's nearest bus stops and train stations, timetables, and travel times, and presents improvement suggestions in a form that the user can put into practice. These suggestions are created using a generative AI model, so they present the optimal options for the user.

[0200] Example prompts for generative AI models

[0201] "The user commutes 20km by car every day, with a fuel efficiency of 10km / L. Calculate the monthly and annual CO2 emissions in this case, and generate a concrete proposal for the CO2 reduction effect of using public transport."

[0202] As described above, the present invention is a system that helps users to review parts of their daily lives and reduce environmental loads through specific actions.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The user inputs daily life information.

[0206] A user uses a smartphone or tablet to input information about daily life such as the means of commuting, commuting distance, fuel efficiency of the car used, etc. The input device means receives this information and transmits it to the server means in a prepared data format.

[0207] Input: Information such as commute method, commute distance, and fuel economy

[0208] Output: Formatted data sent to a server means

[0209] Step 2:

[0210] The terminal transmits the input information to the server.

[0211] The input device means transmits the input information to the server means using the HTTPS protocol, and the server receives the data in an encrypted form.

[0212] Input: Information entered by the user

[0213] Output: Encrypted data received by the server.

[0214] Step 3:

[0215] The server processes the received information and calculates the CO2 emissions.

[0216] The server uses the data processing means to calculate the amount of CO2 emissions and the environmental load based on the received data. Specifically, it calculates the amount of CO2 emissions per day and cumulative CO2 emissions based on the commuting distance and fuel efficiency information.

[0217] Input: User's life data encrypted and sent to the server

[0218] Output: Calculated CO2 emissions (e.g. 4 kg of CO2 emissions per day of commuting)

[0219] Step 4:

[0220] The server generates data to visualize the calculation results.

[0221] The server means generates data based on the calculation results using the data visualization means to provide the data visually to the user, which is converted into a pie chart or a bar graph, allowing the user to intuitively understand the CO2 emissions.

[0222] Input: Calculated CO2 emissions

[0223] Output: Visualized data (e.g., pie chart, bar graph format)

[0224] Step 5:

[0225] The server generates improvement suggestions.

[0226] The server means uses the proposal generation means to generate specific improvement proposals for reducing environmental impact based on the calculation results. Furthermore, the server means uses the generative AI model to materialize the proposals. For example, the server means may suggest using public transportation, and provide information on the nearest bus stops and train stations, as well as timetables.

[0227] Input: Calculation results and user's life data

[0228] Output: Specific improvement suggestions (e.g., public transport use and details)

[0229] Step 6:

[0230] The user receives the proposal and selects what to do.

[0231] The input device notifies the user of the improvement proposals and presents a specific action plan, and the user checks the proposals and selects an actionable proposal.

[0232] Input: Specific improvement suggestions

[0233] Output: An action plan provided to the user in an executable form

[0234] Through these steps, users can review parts of their daily lives and receive help in reducing environmental impact through concrete actions.

[0235] 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.

[0236] This invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement suggestions based on the results. This system is composed of multiple means including the user, terminal, and server, and is also combined with an emotion engine that recognizes the user's emotions.

[0237] System Overview

[0238] Users use a device to input their daily life information and emotional state. This device is composed of an electronic device such as a smartphone or tablet. When the user inputs information into the device, the device sends this information to a server. The server processes the received information, analyzes the emotional data along with the calculation results, and sends them back to the device. The results are presented as improvement suggestions based on the user's emotions.

[0239] Program processing flow

[0240] 1. Data Entry

[0241] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[0242] Additionally, the user inputs their current emotional state (e.g., stress, happiness, fatigue, etc.).

[0243] 2. Data Transmission

[0244] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using HTTPS as the communication protocol.

[0245] 3. Data Receipt and Processing

[0246] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[0247] The emotion engine analyzes users' emotion data and identifies effective improvement measures for users with high levels of stress or fatigue.

[0248] 4. Visualization of calculation results

[0249] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will emit 4kg of CO2. It also calculates monthly and annual total CO2 emissions.

[0250] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[0251] 5. Emotion-based improvement suggestions and action plans

[0252] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[0253] The server generates improvement suggestions based on the user's emotional state based on the emotional data analyzed by the emotion engine. For example, for a user experiencing high stress, it suggests relaxation activities or environmentally friendly initiatives.

[0254] 6. Information transmission and display

[0255] The server then sends the generated improvement proposals and action plan to the device, which includes detailed information such as the nearest bus stop, timetable, and travel time.

[0256] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[0257] Specific examples

[0258] Entering commuting methods and recognizing emotions

[0259] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[0260] Commuting method: Car

[0261] Commuting distance: 20km

[0262] Fuel economy: 10km / L

[0263] Emotional state: Stressed (high)

[0264] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[0265] Furthermore, the server analyzes the user's emotional data and, if a high stress level is detected, provides suggestions for improvement (e.g., changing to public transportation or commuting by bicycle to achieve relaxation). These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to execute their action plans.

[0266] In this way, the present invention is a system that combines information about a user's daily life with emotional data to realize more effective measures to reduce environmental load and improve quality of life.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] Users launch a dedicated application on their device and input specific daily activities (e.g., the mode of transportation they use to commute to work each day, the amount of electricity they use, etc.) and their current emotional state (e.g., stress, happiness, fatigue, etc.).

[0270] Step 2:

[0271] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using the secure HTTPS communication protocol.

[0272] Step 3:

[0273] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server will retrieve basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[0274] Step 4:

[0275] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[0276] Step 5:

[0277] The server converts the results of the calculations into visual data, specifically generating data to display the results in formats such as pie charts and bar graphs.

[0278] Step 6:

[0279] The server sends the generated visual data to the device, which receives it and visually presents it to the user through an interface, allowing the user to intuitively understand how much CO2 their actions are emitting.

[0280] Step 7:

[0281] The server uses an emotion engine to analyze the user's emotion data. For example, if the user inputs a high stress level, the emotion engine generates improvement suggestions that take into account the user's stress reduction.

[0282] Step 8:

[0283] The server generates customized improvement suggestions based on the analysis results of the emotion engine, such as suggesting using public transportation and adding an explanation for the reason, such as "to reduce stress."

[0284] Step 9:

[0285] The server creates a specific action plan and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[0286] Step 10:

[0287] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[0288] Through these steps, users can understand their environmental impact in real time based on their daily life information and implement specific improvement measures. In addition, by making suggestions that take into account the user's emotional state, it is possible to achieve even more effective lifestyle improvements and reductions in environmental impact.

[0289] Example 2

[0290] 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."

[0291] As environmental problems become more serious in recent years, concrete measures to reduce CO2 emissions and environmental impacts are required. However, the current situation is that there are insufficient means for individual users to understand the environmental impact of their daily actions and take effective measures to improve their lives. Another problem is that there are no systems that can propose optimal improvement measures while taking into account the user's emotional state. This makes it difficult to sustain efforts to reduce environmental impacts.

[0292] 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.

[0293] In this invention, the server includes a processing means for calculating CO2 emissions and environmental loads based on the received daily life information, an emotion analysis means for analyzing the calculation results and emotion information and generating improvement suggestions according to the user's emotional state, and a visualization means for visually displaying the calculation results and improvement suggestions. This makes it possible to calculate and visualize CO2 emissions based on the user's daily life information and emotion data, and to provide improvement suggestions that are optimal for the user's emotional state.

[0294] A "user" is an individual who utilizes the system to input daily life information and emotional information.

[0295] "Terminal means" refers to electronic devices that allow users to input daily life information and emotion information, and particularly includes devices such as smartphones and tablets.

[0296] The term "server means" refers to a computer system that receives and processes information sent from the terminal means.

[0297] "Daily life information" refers to information about the user's daily life behaviors and activities, such as the user's means of commuting and power consumption.

[0298] "Emotional information" refers to the user's own emotional state (e.g., stress, happiness, fatigue, etc.) entered by the user.

[0299] "Processing means" refers to computer programs and computing resources for calculating CO2 emissions and environmental loads based on the received daily life information.

[0300] "Emotion analysis means" refers to algorithms and software for analyzing received emotional information and generating improvement suggestions according to the user's emotional state.

[0301] "Visualization means" refers to software and a user interface for visually displaying the calculation results and improvement suggestions in the form of graphs and dashboards.

[0302] "Improvement proposal" refers to a specific proposal for reducing environmental impact that is generated based on the user's daily life information and emotional information.

[0303] "Public transportation" refers to means of transportation such as buses, trains, and subways that can be used by an unspecified number of people.

[0304] "Bicycle commuting" refers to the act of a user commuting to work by bicycle.

[0305] The above are definitions of important words included in the claims.

[0306] The present invention is a system in which a user inputs daily life information and emotional information through a terminal, a server calculates CO2 emissions and environmental load based on that information, and provides improvement proposals that take the user's emotional state into consideration. This system is composed of multiple elements, including a user, a terminal, and a server.

[0307] System configuration

[0308] 1. Terminal means

[0309] Users input daily life information and emotional information using electronic devices such as smartphones and tablets. The applications used for this purpose provide input forms that allow users to easily input information.

[0310] 2. Server Means

[0311] The server that receives the encrypted data sent from the terminal has a processing means for performing calculations. The server has the following functions:

[0312] Decrypting Data

[0313] Calculation of CO2 emissions and environmental impact based on received daily life information

[0314] Emotional information analysis

[0315] 3. Processing means

[0316] The server runs a program to calculate CO2 emissions based on the received daily life information and emotion information. In particular, it uses the following software and libraries:

[0317] Manage basic data on CO2 emissions using database software (e.g., MySQL (registered trademark))

[0318] A programming language that performs calculations (e.g., Python)

[0319] Generate visual data with a data visualization library (e.g., D3.js, Chart.js)

[0320] 4. Emotion analysis method

[0321] The server is equipped with an emotion engine to analyze the user's emotional information. The emotion engine evaluates the user's emotional state (e.g., stress, happiness, fatigue) and generates optimal improvement suggestions based on that.

[0322] 5. Visualization means

[0323] The server generates data to display the calculation results and sentiment-based improvement suggestions as visually rich data in the form of pie charts and bar graphs, which are then sent back to the device and displayed seamlessly to the user.

[0324] Specific examples

[0325] Data entry and submission

[0326] The user enters information about their daily commute, for example:

[0327] Commuting method: Car

[0328] Commuting distance: 20km

[0329] Fuel economy: 10km / L

[0330] Emotional state: Stress (high)

[0331] By entering this information into the input form and pressing the "Submit" button, the information will be encrypted and sent to the server.

[0332] Data reception and processing

[0333] The server receives the data, decrypts it, and then does the following:

[0334] Obtain basic data from a database (e.g., vehicle fuel efficiency and CO2 emissions)

[0335] Calculates CO2 emissions based on received information (e.g., 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[0336] Analyzes emotional information with an emotion engine and generates optimal improvement suggestions for users

[0337] Visualization of calculation results and improvement suggestions

[0338] The server generates data for visualizing the calculation results, such as in the form of pie charts or bar graphs, and generates improvement suggestions based on the emotion information (e.g., using public transport or commuting by bicycle), along with detailed information (e.g., the nearest bus stop, operating times, and travel time).

[0339] Sending and Displaying Information

[0340] The server sends the data to the device, which then displays it to the user. The user can then improve their lifestyle based on the suggestions. The system also has a reminder function to help users make continuous improvements.

[0341] Prompt Sentence Examples

[0342] Examples of prompts that users can enter into a generative AI model:

[0343] "I commute to work every day by car (20km) and it's stressful. Please calculate my CO2 emissions and suggest some environmentally friendly improvements."

[0344] In this way, the present invention is a specific system that combines information about a user's daily life with emotional data to realize effective measures to reduce environmental load and improve quality of life.

[0345] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0346] Step 1: Data entry

[0347] The user inputs information about their daily life and emotions through a dedicated application. Specifically, they input their commute mode, commute distance, fuel consumption, and current emotional state into an input form, and then press the "Submit" button.

[0348] Input: Commuting method (e.g., car), commuting distance (e.g., 20 km), fuel economy (e.g., 10 km / L), emotional state (e.g., stress (high))

[0349] Output: Information data entered into the terminal

[0350] Step 2: Send data

[0351] The terminal encrypts the information entered by the user and sends it to the server using AES (Advanced Encryption Standard) and the HTTPS protocol to ensure secure communication.

[0352] Input: Information data entered by the user

[0353] Data processing: AES encryption

[0354] Output: Encrypted information data

[0355] Step 3: Receiving and Decrypting Data

[0356] The server receives the encrypted information data, decrypts it, analyzes the received data, and performs the necessary processing.

[0357] Input: Encrypted information data

[0358] Data processing: AES decryption

[0359] Output: Decoded information data (commuting method, commuting distance, fuel consumption, emotional state)

[0360] Step 4: CO2 emissions and environmental impact calculations

[0361] The server calculates CO2 emissions and environmental impact based on the decrypted information. Specifically, it obtains basic data on fuel efficiency and CO2 emissions from the database and performs the following calculations.

[0362] Input: Decoded information data (commuting method, commuting distance, fuel consumption)

[0363] Data calculation: CO2 emissions calculation (Example: 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[0364] Output: Calculation result (CO2 emissions)

[0365] Step 5: Sentiment Analysis

[0366] The server analyzes the user's emotional information using an emotion engine, which identifies optimal improvement suggestions based on the user's emotional data (e.g., stress (high)).

[0367] Input: Emotional state (e.g., stress (high))

[0368] Data Computation: Emotional Data Analysis

[0369] Output: Analysis results (optimal improvement suggestions based on the user's emotional state)

[0370] Step 6: Visualizing the results

[0371] The server generates data to visually display the calculation results and improvement suggestions, using a data visualization library (e.g., D3.js, Chart.js) to display them in the form of pie charts or bar graphs.

[0372] Input: Calculation results (CO2 emissions), analysis results (improvement proposals)

[0373] Data processing: Data visualization

[0374] Output: Visualized data (graph format)

[0375] Step 7: Submitting results and suggestions

[0376] The server sends the generated visualization data and improvement suggestions to the terminal.

[0377] Input: Visualization data, improvement suggestions

[0378] Data processing: Data packaging

[0379] Output: Transmitted data

[0380] Step 8: Viewing results and suggestions

[0381] The device receives the visualized data and improvement suggestions from the server and displays them to the user. The user can view the data visualized in pie charts and bar graphs, along with the improvement suggestions based on their emotional state.

[0382] Input: Incoming data (visualization data, improvement suggestions)

[0383] Output: Results and suggestions displayed in the user interface

[0384] This allows users to understand the impact their actions have on the environment and take appropriate measures to improve the situation.

[0385] (Application example 2)

[0386] 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."

[0387] Conventional CO2 emission calculation systems calculate environmental impact based on the user's daily life information and display the results. However, because they do not take the user's emotional state into account, they do not provide appropriate improvement suggestions that reflect personal factors such as stress and physical condition. This also creates the problem of making it difficult for users to improve their environmental awareness and lifestyle habits. Furthermore, because it is difficult to recognize and reflect the user's emotional state in real time, effective improvement suggestions are lacking.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's daily life information and emotional state, means for calculating CO2 emissions and environmental load based on the received information, means for visually displaying information corresponding to the calculation results and the emotional state, and means for generating improvement suggestions for reducing the environmental load and lifestyle improvement suggestions based on the emotional state. This enables more effective environmental improvement and quality of life improvement that takes the user's emotional state into consideration.

[0389] "User" refers to an individual who utilizes the system to input daily life information and emotional state.

[0390] "Daily life information" refers to specific information about the user's daily life, such as means of transportation, commuting distance, and amount of fuel used.

[0391] "Emotional state" refers to a user's mental and emotional state, such as stress, happiness, fatigue, etc.

[0392] "Terminal means" refers to electronic devices used by users to input and receive information, such as smartphones, tablets, and devices installed in autonomous vehicles.

[0393] "Server means" refers to a central computing device for processing and storing daily life information and emotional state information received from a user, and transmitting the calculation and analysis results to a terminal.

[0394] "Processing means" refers to a computing device that includes algorithms and programs to calculate CO2 emissions and environmental loads based on daily life information and emotional state information.

[0395] "Visualization means" refers to devices and software for visually displaying information according to calculation results and emotional states in the form of pie charts, bar graphs, and dashboards.

[0396] The "suggestion means" refers to an algorithm and a program for generating improvement suggestions for reducing environmental impact and lifestyle improvement suggestions based on emotional states.

[0397] "Means of transportation" refers to all modes of transportation that users use on a daily basis, such as cars, public transportation, and bicycles.

[0398] "Using public transport" refers to using transportation provided by a public transport operator, such as a bus, train or subway.

[0399] "Stress reduction" refers to improvement suggestions and specific lifestyle action plans to reduce a user's high stress levels.

[0400] This system calculates CO2 emissions and environmental impacts in real time by having users input their daily life information and emotional state into a terminal, and provides improvement suggestions based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[0401] System configuration

[0402] User

[0403] The user inputs information about their daily life and emotional state into the terminal. The information about their daily life includes commuting distance, transportation mode, fuel consumption, etc. The emotional state includes stress, happiness, fatigue, etc.

[0404] Terminal

[0405] The devices used may be smartphones, tablets, devices installed in self-driving vehicles, etc. Users use these devices to input information about their daily lives and emotional states.

[0406] server

[0407] The server receives the daily life information and emotional state information entered by the user and performs analysis and calculations.

[0408] The system includes processing means for calculating CO2 emissions and environmental loads based on the received information.

[0409] It includes a visualization means for visually displaying information according to the calculation results and the emotional state.

[0410] The system has a suggestion means for generating improvement suggestions for reducing environmental loads and lifestyle improvement suggestions based on emotional states.

[0411] Hardware and Software

[0412] Hardware: Smartphones, tablets, and devices in self-driving vehicles.

[0413] Software: Python, HTTPS protocol, RESTful API.

[0414] Data processing and calculation

[0415] Data Entry

[0416] When a user inputs daily life information and emotional state using the device, the device sends this information to the server using the HTTPS protocol.

[0417] Data Receipt and Processing

[0418] The server calculates the CO2 emissions and environmental impact based on the received data. For example, if you commute to work every day by car, the distance is 20km, and the fuel efficiency is 10km / L, the CO2 emissions are calculated as follows:

[0419] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[0420] Emotion analysis

[0421] The emotion engine in the server analyzes the user's emotional data, and if high stress levels or fatigue are identified, it generates improvement suggestions that take these into consideration.

[0422] Proposal generation

[0423] The server generates appropriate improvement suggestions for the user based on the calculated environmental load and emotion data, such as specific action plans to use public transportation and reduce stress.

[0424] Visualization and Display

[0425] The server generates data for visually displaying the calculation results and sends it to the terminal, which then uses the received data to visually present CO2 emissions and improvement suggestions to the user in pie charts and bar graphs.

[0426] Specific examples

[0427] If a user drives 20km to work each day, with a fuel economy of 10km / L, the CO2 emissions would be calculated as follows:

[0428] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[0429] This information and the user's emotional state (e.g., stress level 7) are sent to the server, which assesses the user's high stress level and suggests using public transportation or a motorbike. It also displays the nearest bus stop, its timetable, and travel time.

[0430] Prompt Sentence Examples

[0431] Calculate CO2 emissions based on the following user data and emotional data, and generate improvement suggestions according to the emotional state.

[0432] User Data:

[0433] Commuting method: Car

[0434] Commuting distance: 20km

[0435] Fuel economy: 10km / L

[0436] Emotional Data:

[0437] Stress level: 7

[0438] Please provide specific, customized improvement suggestions.

[0439] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0440] Step 1:

[0441] Users use the device to input information about their daily life and emotional state, such as commuting distance, transportation method, fuel consumption, stress level, etc. This input data will be the basis for subsequent processing.

[0442] Step 2:

[0443] The device encrypts the inputted daily life information and emotional state and sends it to the server via HTTPS, ensuring data security during data transfer.

[0444] Input: Daily life information, emotional state

[0445] Output: Encrypted data

[0446] Step 3:

[0447] The server decrypts the received encrypted data and acquires daily life information and emotional state, after which various calculation processes are initiated based on the acquired data.

[0448] Input: Encrypted data

[0449] Output: Decoded daily life information, decoded emotional state

[0450] Step 4:

[0451] The server calculates CO2 emissions and environmental impact based on daily life information. For example, if the commute distance is 20 km and the fuel efficiency is 10 km / L, the daily CO2 emissions will be 4.6 kg. This calculation result will be used in the next step.

[0452] Input: Daily Life Information

[0453] Output: CO2 emissions, environmental impact

[0454] Step 5:

[0455] The server analyzes the user's emotional state. The emotion analysis engine evaluates the input emotional data and detects high stress levels or fatigue. Based on the analysis results, improvement suggestions are generated in the next step.

[0456] Input: Emotional state

[0457] Output: Parsed emotion data

[0458] Step 6:

[0459] The server generates appropriate improvement suggestions for users based on their CO2 emissions and analyzed emotion data. For example, for users with high stress levels, it will suggest specific lifestyle improvements such as using public transportation and reducing stress. These suggestions will also include detailed information such as the nearest transportation options and timetables.

[0460] Input: CO2 emissions, analyzed emotion data

[0461] Output: Improvement suggestions

[0462] Step 7:

[0463] The server sends the generated improvement suggestions to the terminal, and the data is visualized and presented to the user in the form of pie charts and bar graphs.

[0464] Input: Improvement suggestion

[0465] Output: Visualized data

[0466] Step 8:

[0467] The device then displays the visualized data on the screen, allowing the user to intuitively understand how much CO2 their actions are emitting and what measures they can take to improve their situation.

[0468] Input: Visualized data

[0469] Output: What the user sees on their screen

[0470] Through the above processing steps, the system of the present invention can provide effective environmental load reduction measures and lifestyle improvement suggestions based on the user's daily life information and emotional state.

[0471] 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.

[0472] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0473] 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.

[0474] [Second embodiment]

[0475] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0476] 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.

[0477] 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).

[0478] 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.

[0479] 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.

[0480] 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).

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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.

[0486] 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."

[0487] This system calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement proposals based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[0488] System Overview

[0489] Users use a device to input information about their daily lives. This device is an electronic device such as a smartphone or tablet. When a user inputs information into the device, the device sends the information to a server. The server processes the received information and sends the calculation results back to the device. The device then visualizes the results and presents them to the user.

[0490] Program processing flow

[0491] 1. Data Entry

[0492] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[0493] 2. Data Transmission

[0494] The device encrypts the information entered and sends it to the server, using a secure protocol such as HTTPS.

[0495] 3. Data Receipt and Processing

[0496] The server receives information about daily life sent by the user, and calculates CO2 emissions and environmental impact based on this information.

[0497] The server also retrieves relevant environmental impact data from existing databases and uses this to perform detailed calculations.

[0498] 4. Visualization of calculation results

[0499] The server generates data for visualizing the calculation results and sends it to the terminal, which can then display them in pie charts, bar graphs, or dashboard formats.

[0500] The terminal displays the received data in a graphical format to the user.

[0501] 5. Improvement proposals and action plans

[0502] Based on the calculation results, the server generates specific improvement suggestions for the user, such as using public transportation or recommending energy-efficient appliances.

[0503] A proposed action plan is also sent to the terminal for the user to review and receive in an actionable form.

[0504] Specific examples

[0505] Enter your commute method

[0506] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[0507] Commuting method: Car

[0508] Commuting distance: 20km

[0509] Fuel economy: 10km / L

[0510] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[0511] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to put their action plans into action.

[0512] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[0513] The processing flow will be explained below.

[0514] Step 1:

[0515] Users launch a dedicated application on their device and input information about their daily life, such as "I commute 20km by car every day" or "I consume 100kWh of electricity every month."

[0516] Step 2:

[0517] The device encrypts the information entered by the user and sends it to the server via the Internet, using HTTPS as the communication protocol.

[0518] Step 3:

[0519] The server analyzes the received daily life information and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)" from the database.

[0520] Step 4:

[0521] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[0522] Step 5:

[0523] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[0524] Step 6:

[0525] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[0526] Step 7:

[0527] The server generates improvement proposals for reducing the user's CO2 emissions. For example, it generates a proposal such as "Using public transportation can reduce CO2 emissions by 80%."

[0528] Step 8:

[0529] The server creates an action plan based on specific improvement suggestions and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[0530] Step 9:

[0531] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[0532] Through the above steps, users can understand the environmental impact in real time based on their daily life information and implement specific improvement measures.

[0533] Example 1

[0534] 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."

[0535] In modern society, effectively managing and reducing CO2 emissions and environmental impacts is an urgent issue. However, many people lack the means to specifically understand the environmental impact of their daily lives, and it is difficult to receive specific suggestions on how to improve their daily behavior. In addition, existing systems lack sufficient data security, making it difficult to protect user privacy.

[0536] 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.

[0537] In this invention, the server includes a terminal means for a user to input daily life information, a means for encrypting and transmitting the input daily life information, a server means for receiving the transmitted daily life information, a processing means for analyzing the received daily life information and acquiring related data, a calculation means for calculating CO2 emissions and environmental loads based on the received and analyzed daily life information, a visualization means for visually displaying the calculation results, a proposal means for generating improvement proposals to reduce environmental loads based on the calculation results, and a display means for feeding back the generated visualization data and improvement proposals to the user. This makes it possible to provide specific and feasible measures to reduce environmental loads while protecting the user's privacy.

[0538] "User" refers to an individual or group who uses the system to input and check daily life information and implement suggested improvement measures.

[0539] "Terminal means" refers to electronic devices used by users to input daily life information, and includes smartphones, tablets, and the like.

[0540] "Encryption methods" are technologies used to securely convert data entered by users so that it cannot be read by third parties. This includes the AES encryption protocol.

[0541] "Transmission means" refers to a function for securely transferring encrypted data to a server, and includes the HTTPS protocol, etc.

[0542] The "server means" is a central processing unit for receiving and processing data sent from the terminal.

[0543] "Processing means" is a function for analyzing received data and generating calculations and proposals based on the data.

[0544] "Calculation means" refers to a function for numerically calculating CO2 emissions and environmental impact based on daily life information. This includes the Python NumPy library.

[0545] "Visualization methods" are techniques for visually displaying calculation results in an easy-to-understand manner, including pie charts and bar graphs.

[0546] The "proposal means" is a function that allows the user to generate specific and feasible improvement proposals based on the calculation results.

[0547] The "display means" is a function for providing feedback to the user on the generated visualization data and improvement suggestions.

[0548] The present invention is a system that allows users to input daily life information into a terminal, calculates CO2 emissions and environmental impact in real time, and provides improvement proposals based on the information. This system is composed of multiple means including users, terminals, and a server.

[0549] System Overview

[0550] Users launch a dedicated application on a device such as a smartphone or tablet and input specific information about their daily activities (e.g., their daily commute route, the amount of electricity they use, etc.). The device then encrypts this information and sends it to a server using a secure protocol (e.g., HTTPS).

[0551] The server analyzes the received information and calculates CO2 emissions and environmental impact. This calculation uses the Python NumPy library and environmental impact data obtained from an existing database. The calculation results are visualized in the form of pie charts and bar graphs and sent to the terminal, which displays them on a user interface.

[0552] The server then generates specific improvement suggestions for the user based on the calculation results, such as using public transportation or recommending energy-efficient appliances. These improvement suggestions are sent to the device along with additional information such as the nearest bus or train station, timetables, and travel times, and are provided in a form that the user can easily implement.

[0553] Specific examples

[0554] Enter your commute method

[0555] If a user commutes 20km by car each day, they enter the following information into their terminal:

[0556] Commuting method: Car

[0557] Commuting distance: 20km

[0558] Fuel economy: 10km / L

[0559] The device encrypts this information and sends it to a server, which calculates that a 20km commute results in approximately 4.6kg of CO2 emissions per day. The server also calculates monthly and annual total emissions and visualizes them in pie and bar charts before sending them to the device. Users can intuitively view the results in the app.

[0560] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by approximately 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, allowing users to easily implement the suggestions.

[0561] Example prompts for generative AI models

[0562] "I commute 20km by car every day. What is the CO2 emissions in this case? Can you give me some concrete suggestions for reducing my environmental impact?"

[0563] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[0564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0565] Step 1: Data entry

[0566] The user starts the dedicated application on the device.

[0567] The user inputs specific daily activities (e.g., daily commuting method, commuting distance, power consumption, etc.) As an input example, the user inputs information for commuting by car, commuting distance of 20 km, and fuel efficiency of 10 km / L.

[0568] Input data:

[0569] Commuting method: Car

[0570] Commuting distance: 20km

[0571] Fuel economy: 10km / L

[0572] Output data:

[0573] The terminal holds the daily life information input by the user.

[0574] Step 2: Send data

[0575] The terminal encrypts the input data using an encryption protocol (e.g., AES).

[0576] The device sends the encrypted data to the server using the HTTPS protocol.

[0577] Input data:

[0578] Daily life information entered by the user

[0579] Output data:

[0580] The encrypted daily life information is sent to a server.

[0581] Step 3: Data Receipt and Decryption

[0582] The server receives the encrypted data sent from the terminal.

[0583] The server decrypts the encrypted data and retrieves the original daily life information.

[0584] Input data:

[0585] Encrypted daily life information

[0586] Output data:

[0587] Decrypted daily life information

[0588] Step 4: Acquire and analyze relevant data

[0589] The server parses the received data.

[0590] The server retrieves relevant data (e.g., CO2 emission coefficient of fuel) from an internal database.

[0591] Input data:

[0592] Decrypted daily life information

[0593] Output data:

[0594] Complete dataset with related data

[0595] Step 5: Calculate CO2 emissions and environmental impact

[0596] The server uses the relevant data to calculate CO2 emissions and environmental impact. For example, if the commute distance is 20km and the fuel efficiency is 10km / L, the amount of gasoline consumed is 2L, which will emit 4.6kg of CO2.

[0597] The server adds the calculation results to the dataset.

[0598] Input data:

[0599] Complete dataset with related data

[0600] Output data:

[0601] Dataset containing calculation results of CO2 emissions and environmental impact

[0602] Step 6: Visualizing the results

[0603] The server generates data (e.g., pie charts, bar chart data points) to visualize the calculated CO2 emissions and environmental impact.

[0604] The server sends the visualization data to the terminal.

[0605] Input data:

[0606] Dataset containing calculation results of CO2 emissions and environmental impact

[0607] Output data:

[0608] Visualized Data

[0609] Step 7: Generate improvement suggestions

[0610] Based on the calculation results, the server generates specific improvement proposals for the user, such as "Using public transportation can reduce CO2 emissions by 80%."

[0611] The server retrieves more detailed data (e.g., bus and train stops, timetables, and travel times) from an external API (e.g., a transportation data provider).

[0612] Input data:

[0613] Calculation results of CO2 emissions and environmental impact

[0614] Additional data from external APIs

[0615] Output data:

[0616] Dataset containing improvement suggestions

[0617] Step 8: User Feedback

[0618] The terminal displays the visualization data and improvement suggestions received from the server on the user interface.

[0619] The user reviews the displayed data and takes action based on the suggested improvements, if necessary.

[0620] Input data:

[0621] A dataset containing visualization data and improvement suggestions

[0622] Output data:

[0623] Information displayed in the user interface

[0624] (Application example 1)

[0625] 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."

[0626] In modern society, it is difficult for individuals to grasp CO2 emissions and environmental impacts in real time and implement appropriate improvement measures based on that information. Existing systems lack the ability to provide effective improvement suggestions based on the daily life information entered by the user, and they rarely provide suggestions that allow users to easily take concrete action. Furthermore, the proposed improvement measures must be specific.

[0627] 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.

[0628] In this invention, the server includes an input device means for a user to input daily life information, a server means for receiving the input daily life information, a data processing means for calculating CO2 emissions and environmental load based on the received daily life information, a data visualization means for visually displaying the calculation results, a proposal generation means for generating improvement proposals to reduce the environmental load, and a generation means for realizing the improvement proposals using a generative AI model. This allows the user to visually grasp the extent to which their lifestyle habits affect the environment and receive specific and actionable improvement proposals.

[0629] "Input device means" refers to a device used by a user to input information about daily life, and includes a smartphone, tablet, etc.

[0630] "Server means" is a computer system for receiving and processing information sent from input device means.

[0631] "Data processing means" refers to a software or hardware configuration for calculating CO2 emissions and environmental loads based on received daily life information.

[0632] "Data visualization means" is a function for visually presenting calculated results to the user, and includes display in pie charts, bar graphs, and dashboard formats.

[0633] "Proposal generation means" refers to software or algorithms that create specific improvement proposals for reducing environmental impact based on the calculation results.

[0634] The "generation means" is a system that uses a generative AI model to concretize improvement suggestions for users, and is a means of providing specific action plans.

[0635] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal improvement suggestions based on user input information.

[0636] "User" refers to an individual who uses this system to input information about their daily life and receive suggestions for improving their CO2 emissions and environmental impact.

[0637] The present invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives, and provides specific improvement proposals based on the calculations. This system is composed of multiple means, including a user, an input device means, and a server means.

[0638] System Overview

[0639] User Operation

[0640] Users input information about their daily lives using input devices such as smartphones and tablets. For example, the user might input information such as "I use a car to commute to work every day." They also input detailed information such as the fuel efficiency of the car they use and the distance they commute to work.

[0641] Data transmission

[0642] The input device means transmits the input information to the server means, using a secure protocol such as HTTPS for this communication to ensure the integrity and security of the data.

[0643] Data Processing

[0644] The server has a data processing unit for calculating CO2 emissions and environmental load based on the received daily life information. Specifically, it uses an algorithm to calculate daily CO2 emissions from distance and fuel efficiency information, and calculates the cumulative value.

[0645] Data Visualization

[0646] The calculated results are presented to the user visually using data visualization tools, such as pie charts, bar graphs, and dashboard-style interfaces, in a way that is intuitively understandable to the user.

[0647] Improvement proposal generation

[0648] The server means also includes a proposal generation means for generating specific improvement proposals for reducing environmental impacts based on the received data and the calculation results. The improvement proposals are further embodied using a generative AI model. The generation means analyzes the user's behavioral patterns and presents optimal improvement measures.

[0649] Specific examples

[0650] For example, suppose a user commutes to work every day by car, travels 20 km per day, and has a fuel efficiency of 10 km / L. This information is sent to the server means, which then calculates the amount of CO2 emissions based on this information. The calculation result shows that the user emits approximately 4 kg of CO2 per day of commuting.

[0651] Furthermore, the server means uses this information to suggest "using public transportation." The generation means provides information on the user's nearest bus stops and train stations, timetables, and travel times, and presents improvement suggestions in a form that the user can put into practice. These suggestions are created using a generative AI model, so they present the optimal options for the user.

[0652] Example prompts for generative AI models

[0653] "The user commutes 20km by car every day, with a fuel efficiency of 10km / L. Calculate the monthly and annual CO2 emissions in this case, and generate a concrete proposal for the CO2 reduction effect of using public transport."

[0654] As described above, the present invention is a system that helps users to review parts of their daily lives and reduce environmental loads through specific actions.

[0655] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0656] Step 1:

[0657] The user inputs daily life information.

[0658] A user uses a smartphone or tablet to input information about daily life such as the means of commuting, commuting distance, fuel efficiency of the car used, etc. The input device means receives this information and transmits it to the server means in a prepared data format.

[0659] Input: Information such as commute method, commute distance, and fuel economy

[0660] Output: Formatted data sent to a server means

[0661] Step 2:

[0662] The terminal transmits the input information to the server.

[0663] The input device means transmits the input information to the server means using the HTTPS protocol, and the server receives the data in an encrypted form.

[0664] Input: Information entered by the user

[0665] Output: Encrypted data received by the server.

[0666] Step 3:

[0667] The server processes the received information and calculates the CO2 emissions.

[0668] The server uses the data processing means to calculate the amount of CO2 emissions and the environmental load based on the received data. Specifically, it calculates the amount of CO2 emissions per day and cumulative CO2 emissions based on the commuting distance and fuel efficiency information.

[0669] Input: User's life data encrypted and sent to the server

[0670] Output: Calculated CO2 emissions (e.g. 4 kg of CO2 emissions per day of commuting)

[0671] Step 4:

[0672] The server generates data to visualize the calculation results.

[0673] The server means generates data based on the calculation results using the data visualization means to provide the data visually to the user, which is converted into a pie chart or a bar graph, allowing the user to intuitively understand the CO2 emissions.

[0674] Input: Calculated CO2 emissions

[0675] Output: Visualized data (e.g., pie chart, bar graph format)

[0676] Step 5:

[0677] The server generates improvement suggestions.

[0678] The server means uses the proposal generation means to generate specific improvement proposals for reducing environmental impact based on the calculation results. Furthermore, the server means uses the generative AI model to materialize the proposals. For example, the server means may suggest using public transportation, and provide information on the nearest bus stops and train stations, as well as timetables.

[0679] Input: Calculation results and user's life data

[0680] Output: Specific improvement suggestions (e.g., public transport use and details)

[0681] Step 6:

[0682] The user receives the proposal and selects what to do.

[0683] The input device notifies the user of the improvement proposals and presents a specific action plan, and the user checks the proposals and selects an actionable proposal.

[0684] Input: Specific improvement suggestions

[0685] Output: An action plan provided to the user in an executable form

[0686] Through these steps, users can review parts of their daily lives and receive help in reducing environmental impact through concrete actions.

[0687] 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.

[0688] This invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement suggestions based on the results. This system is composed of multiple means including the user, terminal, and server, and is also combined with an emotion engine that recognizes the user's emotions.

[0689] System Overview

[0690] Users use a device to input their daily life information and emotional state. This device is composed of an electronic device such as a smartphone or tablet. When the user inputs information into the device, the device sends this information to a server. The server processes the received information, analyzes the emotional data along with the calculation results, and sends them back to the device. The results are presented as improvement suggestions based on the user's emotions.

[0691] Program processing flow

[0692] 1. Data Entry

[0693] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[0694] Additionally, the user inputs their current emotional state (e.g., stress, happiness, fatigue, etc.).

[0695] 2. Data Transmission

[0696] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using HTTPS as the communication protocol.

[0697] 3. Data Receipt and Processing

[0698] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[0699] The emotion engine analyzes users' emotion data and identifies effective improvement measures for users with high levels of stress or fatigue.

[0700] 4. Visualization of calculation results

[0701] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will emit 4kg of CO2. It also calculates monthly and annual total CO2 emissions.

[0702] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[0703] 5. Emotion-based improvement suggestions and action plans

[0704] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[0705] The server generates improvement suggestions based on the user's emotional state based on the emotional data analyzed by the emotion engine. For example, for a user experiencing high stress, it suggests relaxation activities or environmentally friendly initiatives.

[0706] 6. Information transmission and display

[0707] The server then sends the generated improvement proposals and action plan to the device, which includes detailed information such as the nearest bus stop, timetable, and travel time.

[0708] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[0709] Specific examples

[0710] Entering commuting methods and recognizing emotions

[0711] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[0712] Commuting method: Car

[0713] Commuting distance: 20km

[0714] Fuel economy: 10km / L

[0715] Emotional state: Stressed (high)

[0716] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[0717] Furthermore, the server analyzes the user's emotional data and, if a high stress level is detected, provides suggestions for improvement (e.g., changing to public transportation or commuting by bicycle to achieve relaxation). These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to execute their action plans.

[0718] In this way, the present invention is a system that combines information about a user's daily life with emotional data to realize more effective measures to reduce environmental load and improve quality of life.

[0719] The processing flow will be explained below.

[0720] Step 1:

[0721] Users launch a dedicated application on their device and input specific daily activities (e.g., the mode of transportation they use to commute to work each day, the amount of electricity they use, etc.) and their current emotional state (e.g., stress, happiness, fatigue, etc.).

[0722] Step 2:

[0723] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using the secure HTTPS communication protocol.

[0724] Step 3:

[0725] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server will retrieve basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[0726] Step 4:

[0727] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[0728] Step 5:

[0729] The server converts the results of the calculations into visual data, specifically generating data to display the results in formats such as pie charts and bar graphs.

[0730] Step 6:

[0731] The server sends the generated visual data to the device, which receives it and visually presents it to the user through an interface, allowing the user to intuitively understand how much CO2 their actions are emitting.

[0732] Step 7:

[0733] The server uses an emotion engine to analyze the user's emotion data. For example, if the user inputs a high stress level, the emotion engine generates improvement suggestions that take into account the user's stress reduction.

[0734] Step 8:

[0735] The server generates customized improvement suggestions based on the analysis results of the emotion engine, such as suggesting using public transportation and adding an explanation for the reason, such as "to reduce stress."

[0736] Step 9:

[0737] The server creates a specific action plan and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[0738] Step 10:

[0739] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[0740] Through these steps, users can understand their environmental impact in real time based on their daily life information and implement specific improvement measures. In addition, by making suggestions that take into account the user's emotional state, it is possible to achieve even more effective lifestyle improvements and reductions in environmental impact.

[0741] Example 2

[0742] 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."

[0743] As environmental problems become more serious in recent years, concrete measures to reduce CO2 emissions and environmental impacts are required. However, the current situation is that there are insufficient means for individual users to understand the environmental impact of their daily actions and take effective measures to improve their lives. Another problem is that there are no systems that can propose optimal improvement measures while taking into account the user's emotional state. This makes it difficult to sustain efforts to reduce environmental impacts.

[0744] 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.

[0745] In this invention, the server includes a processing means for calculating CO2 emissions and environmental loads based on the received daily life information, an emotion analysis means for analyzing the calculation results and emotion information and generating improvement suggestions according to the user's emotional state, and a visualization means for visually displaying the calculation results and improvement suggestions. This makes it possible to calculate and visualize CO2 emissions based on the user's daily life information and emotion data, and to provide improvement suggestions that are optimal for the user's emotional state.

[0746] A "user" is an individual who utilizes the system to input daily life information and emotional information.

[0747] "Terminal means" refers to electronic devices that allow users to input daily life information and emotion information, and particularly includes devices such as smartphones and tablets.

[0748] The term "server means" refers to a computer system that receives and processes information sent from the terminal means.

[0749] "Daily life information" refers to information about the user's daily life behaviors and activities, such as the user's means of commuting and power consumption.

[0750] "Emotional information" refers to the user's own emotional state (e.g., stress, happiness, fatigue, etc.) entered by the user.

[0751] "Processing means" refers to computer programs and computing resources for calculating CO2 emissions and environmental loads based on the received daily life information.

[0752] "Emotion analysis means" refers to algorithms and software for analyzing received emotional information and generating improvement suggestions according to the user's emotional state.

[0753] "Visualization means" refers to software and a user interface for visually displaying the calculation results and improvement suggestions in the form of graphs and dashboards.

[0754] "Improvement proposal" refers to a specific proposal for reducing environmental impact that is generated based on the user's daily life information and emotional information.

[0755] "Public transportation" refers to means of transportation such as buses, trains, and subways that can be used by an unspecified number of people.

[0756] "Bicycle commuting" refers to the act of a user commuting to work by bicycle.

[0757] The above are definitions of important words included in the claims.

[0758] The present invention is a system in which a user inputs daily life information and emotional information through a terminal, a server calculates CO2 emissions and environmental load based on that information, and provides improvement proposals that take the user's emotional state into consideration. This system is composed of multiple elements, including a user, a terminal, and a server.

[0759] System configuration

[0760] 1. Terminal means

[0761] Users input daily life information and emotional information using electronic devices such as smartphones and tablets. The applications used for this purpose provide input forms that allow users to easily input information.

[0762] 2. Server Means

[0763] The server that receives the encrypted data sent from the terminal has a processing means for performing calculations. The server has the following functions:

[0764] Decrypting Data

[0765] Calculation of CO2 emissions and environmental impact based on received daily life information

[0766] Emotional information analysis

[0767] 3. Processing means

[0768] The server runs a program to calculate CO2 emissions based on the received daily life information and emotion information. In particular, it uses the following software and libraries:

[0769] Manage basic data on CO2 emissions using database software (e.g., MySQL)

[0770] A programming language that performs calculations (e.g., Python)

[0771] Generate visual data with a data visualization library (e.g., D3.js, Chart.js)

[0772] 4. Emotion analysis method

[0773] The server is equipped with an emotion engine to analyze the user's emotional information. The emotion engine evaluates the user's emotional state (e.g., stress, happiness, fatigue) and generates optimal improvement suggestions based on that.

[0774] 5. Visualization means

[0775] The server generates data to display the calculation results and sentiment-based improvement suggestions as visually rich data in the form of pie charts and bar graphs, which are then sent back to the device and displayed seamlessly to the user.

[0776] Specific examples

[0777] Data entry and submission

[0778] The user enters information about their daily commute, for example:

[0779] Commuting method: Car

[0780] Commuting distance: 20km

[0781] Fuel economy: 10km / L

[0782] Emotional state: Stress (high)

[0783] By entering this information into the input form and pressing the "Submit" button, the information will be encrypted and sent to the server.

[0784] Data reception and processing

[0785] The server receives the data, decrypts it, and then does the following:

[0786] Obtain basic data from a database (e.g., vehicle fuel efficiency and CO2 emissions)

[0787] Calculates CO2 emissions based on received information (e.g., 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[0788] Analyzes emotional information with an emotion engine and generates optimal improvement suggestions for users

[0789] Visualization of calculation results and improvement suggestions

[0790] The server generates data for visualizing the calculation results, such as in the form of pie charts or bar graphs, and generates improvement suggestions based on the emotion information (e.g., using public transport or commuting by bicycle), along with detailed information (e.g., the nearest bus stop, operating times, and travel time).

[0791] Sending and Displaying Information

[0792] The server sends the data to the device, which then displays it to the user. The user can then improve their lifestyle based on the suggestions. The system also has a reminder function to help users make continuous improvements.

[0793] Prompt Sentence Examples

[0794] Examples of prompts that users can enter into a generative AI model:

[0795] "I commute to work every day by car (20km) and it's stressful. Please calculate my CO2 emissions and suggest some environmentally friendly improvements."

[0796] In this way, the present invention is a specific system that combines information about a user's daily life with emotional data to realize effective measures to reduce environmental load and improve quality of life.

[0797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0798] Step 1: Data entry

[0799] The user inputs information about their daily life and emotions through a dedicated application. Specifically, they input their commute mode, commute distance, fuel consumption, and current emotional state into an input form, and then press the "Submit" button.

[0800] Input: Commuting method (e.g., car), commuting distance (e.g., 20 km), fuel economy (e.g., 10 km / L), emotional state (e.g., stress (high))

[0801] Output: Information data entered into the terminal

[0802] Step 2: Send data

[0803] The terminal encrypts the information entered by the user and sends it to the server using AES (Advanced Encryption Standard) and the HTTPS protocol to ensure secure communication.

[0804] Input: Information data entered by the user

[0805] Data processing: AES encryption

[0806] Output: Encrypted information data

[0807] Step 3: Receiving and Decrypting Data

[0808] The server receives the encrypted information data, decrypts it, analyzes the received data, and performs the necessary processing.

[0809] Input: Encrypted information data

[0810] Data processing: AES decryption

[0811] Output: Decoded information data (commuting method, commuting distance, fuel consumption, emotional state)

[0812] Step 4: CO2 emissions and environmental impact calculations

[0813] The server calculates CO2 emissions and environmental impact based on the decrypted information. Specifically, it obtains basic data on fuel efficiency and CO2 emissions from the database and performs the following calculations.

[0814] Input: Decoded information data (commuting method, commuting distance, fuel consumption)

[0815] Data calculation: CO2 emissions calculation (Example: 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[0816] Output: Calculation result (CO2 emissions)

[0817] Step 5: Sentiment Analysis

[0818] The server analyzes the user's emotional information using an emotion engine, which identifies optimal improvement suggestions based on the user's emotional data (e.g., stress (high)).

[0819] Input: Emotional state (e.g., stress (high))

[0820] Data Computation: Emotional Data Analysis

[0821] Output: Analysis results (optimal improvement suggestions based on the user's emotional state)

[0822] Step 6: Visualizing the results

[0823] The server generates data to visually display the calculation results and improvement suggestions, using a data visualization library (e.g., D3.js, Chart.js) to display them in the form of pie charts or bar graphs.

[0824] Input: Calculation results (CO2 emissions), analysis results (improvement proposals)

[0825] Data processing: Data visualization

[0826] Output: Visualized data (graph format)

[0827] Step 7: Submitting results and suggestions

[0828] The server sends the generated visualization data and improvement suggestions to the terminal.

[0829] Input: Visualization data, improvement suggestions

[0830] Data processing: Data packaging

[0831] Output: Transmitted data

[0832] Step 8: Viewing results and suggestions

[0833] The device receives the visualized data and improvement suggestions from the server and displays them to the user. The user can view the data visualized in pie charts and bar graphs, along with the improvement suggestions based on their emotional state.

[0834] Input: Incoming data (visualization data, improvement suggestions)

[0835] Output: Results and suggestions displayed in the user interface

[0836] This allows users to understand the impact their actions have on the environment and take appropriate measures to improve the situation.

[0837] (Application example 2)

[0838] 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."

[0839] Conventional CO2 emission calculation systems calculate environmental impact based on the user's daily life information and display the results. However, because they do not take the user's emotional state into account, they do not provide appropriate improvement suggestions that reflect personal factors such as stress and physical condition. This also creates the problem of making it difficult for users to improve their environmental awareness and lifestyle habits. Furthermore, because it is difficult to recognize and reflect the user's emotional state in real time, effective improvement suggestions are lacking.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's daily life information and emotional state, means for calculating CO2 emissions and environmental load based on the received information, means for visually displaying information corresponding to the calculation results and the emotional state, and means for generating improvement suggestions for reducing the environmental load and lifestyle improvement suggestions based on the emotional state. This enables more effective environmental improvement and quality of life improvement that takes the user's emotional state into consideration.

[0841] "User" refers to an individual who utilizes the system to input daily life information and emotional state.

[0842] "Daily life information" refers to specific information about the user's daily life, such as means of transportation, commuting distance, and amount of fuel used.

[0843] "Emotional state" refers to a user's mental and emotional state, such as stress, happiness, fatigue, etc.

[0844] "Terminal means" refers to electronic devices used by users to input and receive information, such as smartphones, tablets, and devices installed in autonomous vehicles.

[0845] "Server means" refers to a central computing device for processing and storing daily life information and emotional state information received from a user, and transmitting the calculation and analysis results to a terminal.

[0846] "Processing means" refers to a computing device that includes algorithms and programs to calculate CO2 emissions and environmental loads based on daily life information and emotional state information.

[0847] "Visualization means" refers to devices and software for visually displaying information according to calculation results and emotional states in the form of pie charts, bar graphs, and dashboards.

[0848] The "suggestion means" refers to an algorithm and a program for generating improvement suggestions for reducing environmental impact and lifestyle improvement suggestions based on emotional states.

[0849] "Means of transportation" refers to all modes of transportation that users use on a daily basis, such as cars, public transportation, and bicycles.

[0850] "Using public transport" refers to using transportation provided by a public transport operator, such as a bus, train or subway.

[0851] "Stress reduction" refers to improvement suggestions and specific lifestyle action plans to reduce a user's high stress levels.

[0852] This system calculates CO2 emissions and environmental impacts in real time by having users input their daily life information and emotional state into a terminal, and provides improvement suggestions based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[0853] System configuration

[0854] User

[0855] The user inputs information about their daily life and emotional state into the terminal. The information about their daily life includes commuting distance, transportation mode, fuel consumption, etc. The emotional state includes stress, happiness, fatigue, etc.

[0856] Terminal

[0857] The devices used may be smartphones, tablets, devices installed in self-driving vehicles, etc. Users use these devices to input information about their daily lives and emotional states.

[0858] server

[0859] The server receives the daily life information and emotional state information entered by the user and performs analysis and calculations.

[0860] The system includes processing means for calculating CO2 emissions and environmental loads based on the received information.

[0861] It includes a visualization means for visually displaying information according to the calculation results and the emotional state.

[0862] The system has a suggestion means for generating improvement suggestions for reducing environmental loads and lifestyle improvement suggestions based on emotional states.

[0863] Hardware and Software

[0864] Hardware: Smartphones, tablets, and devices in self-driving vehicles.

[0865] Software: Python, HTTPS protocol, RESTful API.

[0866] Data processing and calculation

[0867] Data Entry

[0868] When a user inputs daily life information and emotional state using the device, the device sends this information to the server using the HTTPS protocol.

[0869] Data Receipt and Processing

[0870] The server calculates the CO2 emissions and environmental impact based on the received data. For example, if you commute to work every day by car, the distance is 20km, and the fuel efficiency is 10km / L, the CO2 emissions are calculated as follows:

[0871] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[0872] Emotion analysis

[0873] The emotion engine in the server analyzes the user's emotional data, and if high stress levels or fatigue are identified, it generates improvement suggestions that take these into consideration.

[0874] Proposal generation

[0875] The server generates appropriate improvement suggestions for the user based on the calculated environmental load and emotion data, such as specific action plans to use public transportation and reduce stress.

[0876] Visualization and Display

[0877] The server generates data for visually displaying the calculation results and sends it to the terminal, which then uses the received data to visually present CO2 emissions and improvement suggestions to the user in pie charts and bar graphs.

[0878] Specific examples

[0879] If a user drives 20km to work each day, with a fuel economy of 10km / L, the CO2 emissions would be calculated as follows:

[0880] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[0881] This information and the user's emotional state (e.g., stress level 7) are sent to the server, which assesses the user's high stress level and suggests using public transportation or a motorbike. It also displays the nearest bus stop, its timetable, and travel time.

[0882] Prompt Sentence Examples

[0883] Calculate CO2 emissions based on the following user data and emotional data, and generate improvement suggestions according to the emotional state.

[0884] User Data:

[0885] Commuting method: Car

[0886] Commuting distance: 20km

[0887] Fuel economy: 10km / L

[0888] Emotional Data:

[0889] Stress level: 7

[0890] Please provide specific, customized improvement suggestions.

[0891] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0892] Step 1:

[0893] Users use the device to input information about their daily life and emotional state, such as commuting distance, transportation method, fuel consumption, stress level, etc. This input data will be the basis for subsequent processing.

[0894] Step 2:

[0895] The device encrypts the inputted daily life information and emotional state and sends it to the server via HTTPS, ensuring data security during data transfer.

[0896] Input: Daily life information, emotional state

[0897] Output: Encrypted data

[0898] Step 3:

[0899] The server decrypts the received encrypted data and acquires daily life information and emotional state, after which various calculation processes are initiated based on the acquired data.

[0900] Input: Encrypted data

[0901] Output: Decoded daily life information, decoded emotional state

[0902] Step 4:

[0903] The server calculates CO2 emissions and environmental impact based on daily life information. For example, if the commute distance is 20 km and the fuel efficiency is 10 km / L, the daily CO2 emissions will be 4.6 kg. This calculation result will be used in the next step.

[0904] Input: Daily Life Information

[0905] Output: CO2 emissions, environmental impact

[0906] Step 5:

[0907] The server analyzes the user's emotional state. The emotion analysis engine evaluates the input emotional data and detects high stress levels or fatigue. Based on the analysis results, improvement suggestions are generated in the next step.

[0908] Input: Emotional state

[0909] Output: Parsed emotion data

[0910] Step 6:

[0911] The server generates appropriate improvement suggestions for users based on their CO2 emissions and analyzed emotion data. For example, for users with high stress levels, it will suggest specific lifestyle improvements such as using public transportation and reducing stress. These suggestions will also include detailed information such as the nearest transportation options and timetables.

[0912] Input: CO2 emissions, analyzed emotion data

[0913] Output: Improvement suggestions

[0914] Step 7:

[0915] The server sends the generated improvement suggestions to the terminal, and the data is visualized and presented to the user in the form of pie charts and bar graphs.

[0916] Input: Improvement suggestion

[0917] Output: Visualized data

[0918] Step 8:

[0919] The device then displays the visualized data on the screen, allowing the user to intuitively understand how much CO2 their actions are emitting and what measures they can take to improve their situation.

[0920] Input: Visualized data

[0921] Output: What the user sees on their screen

[0922] Through the above processing steps, the system of the present invention can provide effective environmental load reduction measures and lifestyle improvement suggestions based on the user's daily life information and emotional state.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] [Third embodiment]

[0927] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0928] 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.

[0929] 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).

[0930] 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.

[0931] 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.

[0932] 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).

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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."

[0939] This system calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement proposals based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[0940] System Overview

[0941] Users use a device to input information about their daily lives. This device is an electronic device such as a smartphone or tablet. When a user inputs information into the device, the device sends the information to a server. The server processes the received information and sends the calculation results back to the device. The device then visualizes the results and presents them to the user.

[0942] Program processing flow

[0943] 1. Data Entry

[0944] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[0945] 2. Data Transmission

[0946] The device encrypts the information entered and sends it to the server, using a secure protocol such as HTTPS.

[0947] 3. Data Receipt and Processing

[0948] The server receives information about daily life sent by the user, and calculates CO2 emissions and environmental impact based on this information.

[0949] The server also retrieves relevant environmental impact data from existing databases and uses this to perform detailed calculations.

[0950] 4. Visualization of calculation results

[0951] The server generates data for visualizing the calculation results and sends it to the terminal, which can then display them in pie charts, bar graphs, or dashboard formats.

[0952] The terminal displays the received data in a graphical format to the user.

[0953] 5. Improvement proposals and action plans

[0954] Based on the calculation results, the server generates specific improvement suggestions for the user, such as using public transportation or recommending energy-efficient appliances.

[0955] A proposed action plan is also sent to the terminal for the user to review and receive in an actionable form.

[0956] Specific examples

[0957] Enter your commute method

[0958] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[0959] Commuting method: Car

[0960] Commuting distance: 20km

[0961] Fuel economy: 10km / L

[0962] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[0963] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to put their action plans into action.

[0964] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[0965] The processing flow will be explained below.

[0966] Step 1:

[0967] Users launch a dedicated application on their device and input information about their daily life, such as "I commute 20km by car every day" or "I consume 100kWh of electricity every month."

[0968] Step 2:

[0969] The device encrypts the information entered by the user and sends it to the server via the Internet, using HTTPS as the communication protocol.

[0970] Step 3:

[0971] The server analyzes the received daily life information and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)" from the database.

[0972] Step 4:

[0973] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[0974] Step 5:

[0975] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[0976] Step 6:

[0977] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[0978] Step 7:

[0979] The server generates improvement proposals for reducing the user's CO2 emissions. For example, it generates a proposal such as "Using public transportation can reduce CO2 emissions by 80%."

[0980] Step 8:

[0981] The server creates an action plan based on specific improvement suggestions and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[0982] Step 9:

[0983] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[0984] Through the above steps, users can understand the environmental impact in real time based on their daily life information and implement specific improvement measures.

[0985] Example 1

[0986] 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."

[0987] In modern society, effectively managing and reducing CO2 emissions and environmental impacts is an urgent issue. However, many people lack the means to specifically understand the environmental impact of their daily lives, and it is difficult to receive specific suggestions on how to improve their daily behavior. In addition, existing systems lack sufficient data security, making it difficult to protect user privacy.

[0988] 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.

[0989] In this invention, the server includes a terminal means for a user to input daily life information, a means for encrypting and transmitting the input daily life information, a server means for receiving the transmitted daily life information, a processing means for analyzing the received daily life information and acquiring related data, a calculation means for calculating CO2 emissions and environmental loads based on the received and analyzed daily life information, a visualization means for visually displaying the calculation results, a proposal means for generating improvement proposals to reduce environmental loads based on the calculation results, and a display means for feeding back the generated visualization data and improvement proposals to the user. This makes it possible to provide specific and feasible measures to reduce environmental loads while protecting the user's privacy.

[0990] "User" refers to an individual or group who uses the system to input and check daily life information and implement suggested improvement measures.

[0991] "Terminal means" refers to electronic devices used by users to input daily life information, and includes smartphones, tablets, and the like.

[0992] "Encryption methods" are technologies used to securely convert data entered by users so that it cannot be read by third parties. This includes the AES encryption protocol.

[0993] "Transmission means" refers to a function for securely transferring encrypted data to a server, and includes the HTTPS protocol, etc.

[0994] The "server means" is a central processing unit for receiving and processing data sent from the terminal.

[0995] "Processing means" is a function for analyzing received data and generating calculations and proposals based on the data.

[0996] "Calculation means" refers to a function for numerically calculating CO2 emissions and environmental impact based on daily life information. This includes the Python NumPy library.

[0997] "Visualization methods" are techniques for visually displaying calculation results in an easy-to-understand manner, including pie charts and bar graphs.

[0998] The "proposal means" is a function that allows the user to generate specific and feasible improvement proposals based on the calculation results.

[0999] The "display means" is a function for providing feedback to the user on the generated visualization data and improvement suggestions.

[1000] The present invention is a system that allows users to input daily life information into a terminal, calculates CO2 emissions and environmental impact in real time, and provides improvement proposals based on the information. This system is composed of multiple means including users, terminals, and a server.

[1001] System Overview

[1002] Users launch a dedicated application on a device such as a smartphone or tablet and input specific information about their daily activities (e.g., their daily commute route, the amount of electricity they use, etc.). The device then encrypts this information and sends it to a server using a secure protocol (e.g., HTTPS).

[1003] The server analyzes the received information and calculates CO2 emissions and environmental impact. This calculation uses the Python NumPy library and environmental impact data obtained from an existing database. The calculation results are visualized in the form of pie charts and bar graphs and sent to the terminal, which displays them on a user interface.

[1004] The server then generates specific improvement suggestions for the user based on the calculation results, such as using public transportation or recommending energy-efficient appliances. These improvement suggestions are sent to the device along with additional information such as the nearest bus or train station, timetables, and travel times, and are provided in a form that the user can easily implement.

[1005] Specific examples

[1006] Enter your commute method

[1007] If a user commutes 20km by car each day, they enter the following information into their terminal:

[1008] Commuting method: Car

[1009] Commuting distance: 20km

[1010] Fuel economy: 10km / L

[1011] The device encrypts this information and sends it to a server, which calculates that a 20km commute results in approximately 4.6kg of CO2 emissions per day. The server also calculates monthly and annual total emissions and visualizes them in pie and bar charts before sending them to the device. Users can intuitively view the results in the app.

[1012] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by approximately 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, allowing users to easily implement the suggestions.

[1013] Example prompts for generative AI models

[1014] "I commute 20km by car every day. What is the CO2 emissions in this case? Can you give me some concrete suggestions for reducing my environmental impact?"

[1015] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[1016] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1017] Step 1: Data entry

[1018] The user starts the dedicated application on the device.

[1019] The user inputs specific daily activities (e.g., daily commuting method, commuting distance, power consumption, etc.) As an input example, the user inputs information for commuting by car, commuting distance of 20 km, and fuel efficiency of 10 km / L.

[1020] Input data:

[1021] Commuting method: Car

[1022] Commuting distance: 20km

[1023] Fuel economy: 10km / L

[1024] Output data:

[1025] The terminal holds the daily life information input by the user.

[1026] Step 2: Send data

[1027] The terminal encrypts the input data using an encryption protocol (e.g., AES).

[1028] The device sends the encrypted data to the server using the HTTPS protocol.

[1029] Input data:

[1030] Daily life information entered by the user

[1031] Output data:

[1032] The encrypted daily life information is sent to a server.

[1033] Step 3: Data Receipt and Decryption

[1034] The server receives the encrypted data sent from the terminal.

[1035] The server decrypts the encrypted data and retrieves the original daily life information.

[1036] Input data:

[1037] Encrypted daily life information

[1038] Output data:

[1039] Decrypted daily life information

[1040] Step 4: Acquire and analyze relevant data

[1041] The server parses the received data.

[1042] The server retrieves relevant data (e.g., CO2 emission coefficient of fuel) from an internal database.

[1043] Input data:

[1044] Decrypted daily life information

[1045] Output data:

[1046] Complete dataset with related data

[1047] Step 5: Calculate CO2 emissions and environmental impact

[1048] The server uses the relevant data to calculate CO2 emissions and environmental impact. For example, if the commute distance is 20km and the fuel efficiency is 10km / L, the amount of gasoline consumed is 2L, which will emit 4.6kg of CO2.

[1049] The server adds the calculation results to the dataset.

[1050] Input data:

[1051] Complete dataset with related data

[1052] Output data:

[1053] Dataset containing calculation results of CO2 emissions and environmental impact

[1054] Step 6: Visualizing the results

[1055] The server generates data (e.g., pie charts, bar chart data points) to visualize the calculated CO2 emissions and environmental impact.

[1056] The server sends the visualization data to the terminal.

[1057] Input data:

[1058] Dataset containing calculation results of CO2 emissions and environmental impact

[1059] Output data:

[1060] Visualized Data

[1061] Step 7: Generate improvement suggestions

[1062] Based on the calculation results, the server generates specific improvement proposals for the user, such as "Using public transportation can reduce CO2 emissions by 80%."

[1063] The server retrieves more detailed data (e.g., bus and train stops, timetables, and travel times) from an external API (e.g., a transportation data provider).

[1064] Input data:

[1065] Calculation results of CO2 emissions and environmental impact

[1066] Additional data from external APIs

[1067] Output data:

[1068] Dataset containing improvement suggestions

[1069] Step 8: User Feedback

[1070] The terminal displays the visualization data and improvement suggestions received from the server on the user interface.

[1071] The user reviews the displayed data and takes action based on the suggested improvements, if necessary.

[1072] Input data:

[1073] A dataset containing visualization data and improvement suggestions

[1074] Output data:

[1075] Information displayed in the user interface

[1076] (Application example 1)

[1077] 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."

[1078] In modern society, it is difficult for individuals to grasp CO2 emissions and environmental impacts in real time and implement appropriate improvement measures based on that information. Existing systems lack the ability to provide effective improvement suggestions based on the daily life information entered by the user, and they rarely provide suggestions that allow users to easily take concrete action. Furthermore, the proposed improvement measures must be specific.

[1079] 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.

[1080] In this invention, the server includes an input device means for a user to input daily life information, a server means for receiving the input daily life information, a data processing means for calculating CO2 emissions and environmental load based on the received daily life information, a data visualization means for visually displaying the calculation results, a proposal generation means for generating improvement proposals to reduce the environmental load, and a generation means for realizing the improvement proposals using a generative AI model. This allows the user to visually grasp the extent to which their lifestyle habits affect the environment and receive specific and actionable improvement proposals.

[1081] "Input device means" refers to a device used by a user to input information about daily life, and includes a smartphone, tablet, etc.

[1082] "Server means" is a computer system for receiving and processing information sent from input device means.

[1083] "Data processing means" refers to a software or hardware configuration for calculating CO2 emissions and environmental loads based on received daily life information.

[1084] "Data visualization means" is a function for visually presenting calculated results to the user, and includes display in pie charts, bar graphs, and dashboard formats.

[1085] "Proposal generation means" refers to software or algorithms that create specific improvement proposals for reducing environmental impact based on the calculation results.

[1086] The "generation means" is a system that uses a generative AI model to concretize improvement suggestions for users, and is a means of providing specific action plans.

[1087] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal improvement suggestions based on user input information.

[1088] "User" refers to an individual who uses this system to input information about their daily life and receive suggestions for improving their CO2 emissions and environmental impact.

[1089] The present invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives, and provides specific improvement proposals based on the calculations. This system is composed of multiple means, including a user, an input device means, and a server means.

[1090] System Overview

[1091] User Operation

[1092] Users input information about their daily lives using input devices such as smartphones and tablets. For example, the user might input information such as "I use a car to commute to work every day." They also input detailed information such as the fuel efficiency of the car they use and the distance they commute to work.

[1093] Data transmission

[1094] The input device means transmits the input information to the server means, using a secure protocol such as HTTPS for this communication to ensure the integrity and security of the data.

[1095] Data Processing

[1096] The server has a data processing unit for calculating CO2 emissions and environmental load based on the received daily life information. Specifically, it uses an algorithm to calculate daily CO2 emissions from distance and fuel efficiency information, and calculates the cumulative value.

[1097] Data Visualization

[1098] The calculated results are presented to the user visually using data visualization tools, such as pie charts, bar graphs, and dashboard-style interfaces, in a way that is intuitively understandable to the user.

[1099] Improvement proposal generation

[1100] The server means also includes a proposal generation means for generating specific improvement proposals for reducing environmental impacts based on the received data and the calculation results. The improvement proposals are further embodied using a generative AI model. The generation means analyzes the user's behavioral patterns and presents optimal improvement measures.

[1101] Specific examples

[1102] For example, suppose a user commutes to work every day by car, travels 20 km per day, and has a fuel efficiency of 10 km / L. This information is sent to the server means, which then calculates the amount of CO2 emissions based on this information. The calculation result shows that the user emits approximately 4 kg of CO2 per day of commuting.

[1103] Furthermore, the server means uses this information to suggest "using public transportation." The generation means provides information on the user's nearest bus stops and train stations, timetables, and travel times, and presents improvement suggestions in a form that the user can put into practice. These suggestions are created using a generative AI model, so they present the optimal options for the user.

[1104] Example prompts for generative AI models

[1105] "The user commutes 20km by car every day, with a fuel efficiency of 10km / L. Calculate the monthly and annual CO2 emissions in this case, and generate a concrete proposal for the CO2 reduction effect of using public transport."

[1106] As described above, the present invention is a system that helps users to review parts of their daily lives and reduce environmental loads through specific actions.

[1107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1108] Step 1:

[1109] The user inputs daily life information.

[1110] A user uses a smartphone or tablet to input information about daily life such as the means of commuting, commuting distance, fuel efficiency of the car used, etc. The input device means receives this information and transmits it to the server means in a prepared data format.

[1111] Input: Information such as commute method, commute distance, and fuel economy

[1112] Output: Formatted data sent to a server means

[1113] Step 2:

[1114] The terminal transmits the input information to the server.

[1115] The input device means transmits the input information to the server means using the HTTPS protocol, and the server receives the data in an encrypted form.

[1116] Input: Information entered by the user

[1117] Output: Encrypted data received by the server.

[1118] Step 3:

[1119] The server processes the received information and calculates the CO2 emissions.

[1120] The server uses the data processing means to calculate the amount of CO2 emissions and the environmental load based on the received data. Specifically, it calculates the amount of CO2 emissions per day and cumulative CO2 emissions based on the commuting distance and fuel efficiency information.

[1121] Input: User's life data encrypted and sent to the server

[1122] Output: Calculated CO2 emissions (e.g. 4 kg of CO2 emissions per day of commuting)

[1123] Step 4:

[1124] The server generates data to visualize the calculation results.

[1125] The server means generates data based on the calculation results using the data visualization means to provide the data visually to the user, which is converted into a pie chart or a bar graph, allowing the user to intuitively understand the CO2 emissions.

[1126] Input: Calculated CO2 emissions

[1127] Output: Visualized data (e.g., pie chart, bar graph format)

[1128] Step 5:

[1129] The server generates improvement suggestions.

[1130] The server means uses the proposal generation means to generate specific improvement proposals for reducing environmental impact based on the calculation results. Furthermore, the server means uses the generative AI model to materialize the proposals. For example, the server means may suggest using public transportation, and provide information on the nearest bus stops and train stations, as well as timetables.

[1131] Input: Calculation results and user's life data

[1132] Output: Specific improvement suggestions (e.g., public transport use and details)

[1133] Step 6:

[1134] The user receives the proposal and selects what to do.

[1135] The input device notifies the user of the improvement proposals and presents a specific action plan, and the user checks the proposals and selects an actionable proposal.

[1136] Input: Specific improvement suggestions

[1137] Output: An action plan provided to the user in an executable form

[1138] Through these steps, users can review parts of their daily lives and receive help in reducing environmental impact through concrete actions.

[1139] 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.

[1140] This invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement suggestions based on the results. This system is composed of multiple means including the user, terminal, and server, and is also combined with an emotion engine that recognizes the user's emotions.

[1141] System Overview

[1142] Users use a device to input their daily life information and emotional state. This device is composed of an electronic device such as a smartphone or tablet. When the user inputs information into the device, the device sends this information to a server. The server processes the received information, analyzes the emotional data along with the calculation results, and sends them back to the device. The results are presented as improvement suggestions based on the user's emotions.

[1143] Program processing flow

[1144] 1. Data Entry

[1145] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[1146] Additionally, the user inputs their current emotional state (e.g., stress, happiness, fatigue, etc.).

[1147] 2. Data Transmission

[1148] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using HTTPS as the communication protocol.

[1149] 3. Data Receipt and Processing

[1150] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[1151] The emotion engine analyzes users' emotion data and identifies effective improvement measures for users with high levels of stress or fatigue.

[1152] 4. Visualization of calculation results

[1153] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will emit 4kg of CO2. It also calculates monthly and annual total CO2 emissions.

[1154] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[1155] 5. Emotion-based improvement suggestions and action plans

[1156] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[1157] The server generates improvement suggestions based on the user's emotional state based on the emotional data analyzed by the emotion engine. For example, for a user experiencing high stress, it suggests relaxation activities or environmentally friendly initiatives.

[1158] 6. Information transmission and display

[1159] The server then sends the generated improvement proposals and action plan to the device, which includes detailed information such as the nearest bus stop, timetable, and travel time.

[1160] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[1161] Specific examples

[1162] Entering commuting methods and recognizing emotions

[1163] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[1164] Commuting method: Car

[1165] Commuting distance: 20km

[1166] Fuel economy: 10km / L

[1167] Emotional state: Stressed (high)

[1168] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[1169] Furthermore, the server analyzes the user's emotional data and, if a high stress level is detected, provides suggestions for improvement (e.g., changing to public transportation or commuting by bicycle to achieve relaxation). These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to execute their action plans.

[1170] In this way, the present invention is a system that combines information about a user's daily life with emotional data to realize more effective measures to reduce environmental load and improve quality of life.

[1171] The processing flow will be explained below.

[1172] Step 1:

[1173] Users launch a dedicated application on their device and input specific daily activities (e.g., the mode of transportation they use to commute to work each day, the amount of electricity they use, etc.) and their current emotional state (e.g., stress, happiness, fatigue, etc.).

[1174] Step 2:

[1175] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using the secure HTTPS communication protocol.

[1176] Step 3:

[1177] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server will retrieve basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[1178] Step 4:

[1179] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[1180] Step 5:

[1181] The server converts the results of the calculations into visual data, specifically generating data to display the results in formats such as pie charts and bar graphs.

[1182] Step 6:

[1183] The server sends the generated visual data to the device, which receives it and visually presents it to the user through an interface, allowing the user to intuitively understand how much CO2 their actions are emitting.

[1184] Step 7:

[1185] The server uses an emotion engine to analyze the user's emotion data. For example, if the user inputs a high stress level, the emotion engine generates improvement suggestions that take into account the user's stress reduction.

[1186] Step 8:

[1187] The server generates customized improvement suggestions based on the analysis results of the emotion engine, such as suggesting using public transportation and adding an explanation for the reason, such as "to reduce stress."

[1188] Step 9:

[1189] The server creates a specific action plan and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[1190] Step 10:

[1191] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[1192] Through these steps, users can understand their environmental impact in real time based on their daily life information and implement specific improvement measures. In addition, by making suggestions that take into account the user's emotional state, it is possible to achieve even more effective lifestyle improvements and reductions in environmental impact.

[1193] Example 2

[1194] 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."

[1195] As environmental problems become more serious in recent years, concrete measures to reduce CO2 emissions and environmental impacts are required. However, the current situation is that there are insufficient means for individual users to understand the environmental impact of their daily actions and take effective measures to improve their lives. Another problem is that there are no systems that can propose optimal improvement measures while taking into account the user's emotional state. This makes it difficult to sustain efforts to reduce environmental impacts.

[1196] 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.

[1197] In this invention, the server includes a processing means for calculating CO2 emissions and environmental loads based on the received daily life information, an emotion analysis means for analyzing the calculation results and emotion information and generating improvement suggestions according to the user's emotional state, and a visualization means for visually displaying the calculation results and improvement suggestions. This makes it possible to calculate and visualize CO2 emissions based on the user's daily life information and emotion data, and to provide improvement suggestions that are optimal for the user's emotional state.

[1198] A "user" is an individual who utilizes the system to input daily life information and emotional information.

[1199] "Terminal means" refers to electronic devices that allow users to input daily life information and emotion information, and particularly includes devices such as smartphones and tablets.

[1200] The term "server means" refers to a computer system that receives and processes information sent from the terminal means.

[1201] "Daily life information" refers to information about the user's daily life behaviors and activities, such as the user's means of commuting and power consumption.

[1202] "Emotional information" refers to the user's own emotional state (e.g., stress, happiness, fatigue, etc.) entered by the user.

[1203] "Processing means" refers to computer programs and computing resources for calculating CO2 emissions and environmental loads based on the received daily life information.

[1204] "Emotion analysis means" refers to algorithms and software for analyzing received emotional information and generating improvement suggestions according to the user's emotional state.

[1205] "Visualization means" refers to software and a user interface for visually displaying the calculation results and improvement suggestions in the form of graphs and dashboards.

[1206] "Improvement proposal" refers to a specific proposal for reducing environmental impact that is generated based on the user's daily life information and emotional information.

[1207] "Public transportation" refers to means of transportation such as buses, trains, and subways that can be used by an unspecified number of people.

[1208] "Bicycle commuting" refers to the act of a user commuting to work by bicycle.

[1209] The above are definitions of important words included in the claims.

[1210] The present invention is a system in which a user inputs daily life information and emotional information through a terminal, a server calculates CO2 emissions and environmental load based on that information, and provides improvement proposals that take the user's emotional state into consideration. This system is composed of multiple elements, including a user, a terminal, and a server.

[1211] System configuration

[1212] 1. Terminal means

[1213] Users input daily life information and emotional information using electronic devices such as smartphones and tablets. The applications used for this purpose provide input forms that allow users to easily input information.

[1214] 2. Server Means

[1215] The server that receives the encrypted data sent from the terminal has a processing means for performing calculations. The server has the following functions:

[1216] Decrypting Data

[1217] Calculation of CO2 emissions and environmental impact based on received daily life information

[1218] Emotional information analysis

[1219] 3. Processing means

[1220] The server runs a program to calculate CO2 emissions based on the received daily life information and emotion information. In particular, it uses the following software and libraries:

[1221] Manage basic data on CO2 emissions using database software (e.g., MySQL)

[1222] A programming language that performs calculations (e.g., Python)

[1223] Generate visual data with a data visualization library (e.g., D3.js, Chart.js)

[1224] 4. Emotion analysis method

[1225] The server is equipped with an emotion engine to analyze the user's emotional information. The emotion engine evaluates the user's emotional state (e.g., stress, happiness, fatigue) and generates optimal improvement suggestions based on that.

[1226] 5. Visualization means

[1227] The server generates data to display the calculation results and sentiment-based improvement suggestions as visually rich data in the form of pie charts and bar graphs, which are then sent back to the device and displayed seamlessly to the user.

[1228] Specific examples

[1229] Data entry and submission

[1230] The user enters information about their daily commute, for example:

[1231] Commuting method: Car

[1232] Commuting distance: 20km

[1233] Fuel economy: 10km / L

[1234] Emotional state: Stress (high)

[1235] By entering this information into the input form and pressing the "Submit" button, the information will be encrypted and sent to the server.

[1236] Data reception and processing

[1237] The server receives the data, decrypts it, and then does the following:

[1238] Obtain basic data from a database (e.g., vehicle fuel efficiency and CO2 emissions)

[1239] Calculates CO2 emissions based on received information (e.g., 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[1240] Analyzes emotional information with an emotion engine and generates optimal improvement suggestions for users

[1241] Visualization of calculation results and improvement suggestions

[1242] The server generates data for visualizing the calculation results, such as in the form of pie charts or bar graphs, and generates improvement suggestions based on the emotion information (e.g., using public transport or commuting by bicycle), along with detailed information (e.g., the nearest bus stop, operating times, and travel time).

[1243] Sending and Displaying Information

[1244] The server sends the data to the device, which then displays it to the user. The user can then improve their lifestyle based on the suggestions. The system also has a reminder function to help users make continuous improvements.

[1245] Prompt Sentence Examples

[1246] Examples of prompts that users can enter into a generative AI model:

[1247] "I commute to work every day by car (20km) and it's stressful. Please calculate my CO2 emissions and suggest some environmentally friendly improvements."

[1248] In this way, the present invention is a specific system that combines information about a user's daily life with emotional data to realize effective measures to reduce environmental load and improve quality of life.

[1249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1250] Step 1: Data entry

[1251] The user inputs information about their daily life and emotions through a dedicated application. Specifically, they input their commute mode, commute distance, fuel consumption, and current emotional state into an input form, and then press the "Submit" button.

[1252] Input: Commuting method (e.g., car), commuting distance (e.g., 20 km), fuel economy (e.g., 10 km / L), emotional state (e.g., stress (high))

[1253] Output: Information data entered into the terminal

[1254] Step 2: Send data

[1255] The terminal encrypts the information entered by the user and sends it to the server using AES (Advanced Encryption Standard) and the HTTPS protocol to ensure secure communication.

[1256] Input: Information data entered by the user

[1257] Data processing: AES encryption

[1258] Output: Encrypted information data

[1259] Step 3: Receiving and Decrypting Data

[1260] The server receives the encrypted information data, decrypts it, analyzes the received data, and performs the necessary processing.

[1261] Input: Encrypted information data

[1262] Data processing: AES decryption

[1263] Output: Decoded information data (commuting method, commuting distance, fuel consumption, emotional state)

[1264] Step 4: CO2 emissions and environmental impact calculations

[1265] The server calculates CO2 emissions and environmental impact based on the decrypted information. Specifically, it obtains basic data on fuel efficiency and CO2 emissions from the database and performs the following calculations.

[1266] Input: Decoded information data (commuting method, commuting distance, fuel consumption)

[1267] Data calculation: CO2 emissions calculation (Example: 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[1268] Output: Calculation result (CO2 emissions)

[1269] Step 5: Sentiment Analysis

[1270] The server analyzes the user's emotional information using an emotion engine, which identifies optimal improvement suggestions based on the user's emotional data (e.g., stress (high)).

[1271] Input: Emotional state (e.g., stress (high))

[1272] Data Computation: Emotional Data Analysis

[1273] Output: Analysis results (optimal improvement suggestions based on the user's emotional state)

[1274] Step 6: Visualizing the results

[1275] The server generates data to visually display the calculation results and improvement suggestions, using a data visualization library (e.g., D3.js, Chart.js) to display them in the form of pie charts or bar graphs.

[1276] Input: Calculation results (CO2 emissions), analysis results (improvement proposals)

[1277] Data processing: Data visualization

[1278] Output: Visualized data (graph format)

[1279] Step 7: Submitting results and suggestions

[1280] The server sends the generated visualization data and improvement suggestions to the terminal.

[1281] Input: Visualization data, improvement suggestions

[1282] Data processing: Data packaging

[1283] Output: Transmitted data

[1284] Step 8: Viewing results and suggestions

[1285] The device receives the visualized data and improvement suggestions from the server and displays them to the user. The user can view the data visualized in pie charts and bar graphs, along with the improvement suggestions based on their emotional state.

[1286] Input: Incoming data (visualization data, improvement suggestions)

[1287] Output: Results and suggestions displayed in the user interface

[1288] This allows users to understand the impact their actions have on the environment and take appropriate measures to improve the situation.

[1289] (Application example 2)

[1290] 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."

[1291] Conventional CO2 emission calculation systems calculate environmental impact based on the user's daily life information and display the results. However, because they do not take the user's emotional state into account, they do not provide appropriate improvement suggestions that reflect personal factors such as stress and physical condition. This also creates the problem of making it difficult for users to improve their environmental awareness and lifestyle habits. Furthermore, because it is difficult to recognize and reflect the user's emotional state in real time, effective improvement suggestions are lacking.

[1292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's daily life information and emotional state, means for calculating CO2 emissions and environmental load based on the received information, means for visually displaying information corresponding to the calculation results and the emotional state, and means for generating improvement suggestions for reducing the environmental load and lifestyle improvement suggestions based on the emotional state. This enables more effective environmental improvement and quality of life improvement that takes the user's emotional state into consideration.

[1293] "User" refers to an individual who utilizes the system to input daily life information and emotional state.

[1294] "Daily life information" refers to specific information about the user's daily life, such as means of transportation, commuting distance, and amount of fuel used.

[1295] "Emotional state" refers to a user's mental and emotional state, such as stress, happiness, fatigue, etc.

[1296] "Terminal means" refers to electronic devices used by users to input and receive information, such as smartphones, tablets, and devices installed in autonomous vehicles.

[1297] "Server means" refers to a central computing device for processing and storing daily life information and emotional state information received from a user, and transmitting the calculation and analysis results to a terminal.

[1298] "Processing means" refers to a computing device that includes algorithms and programs to calculate CO2 emissions and environmental loads based on daily life information and emotional state information.

[1299] "Visualization means" refers to devices and software for visually displaying information according to calculation results and emotional states in the form of pie charts, bar graphs, and dashboards.

[1300] The "suggestion means" refers to an algorithm and a program for generating improvement suggestions for reducing environmental impact and lifestyle improvement suggestions based on emotional states.

[1301] "Means of transportation" refers to all modes of transportation that users use on a daily basis, such as cars, public transportation, and bicycles.

[1302] "Using public transport" refers to using transportation provided by a public transport operator, such as a bus, train or subway.

[1303] "Stress reduction" refers to improvement suggestions and specific lifestyle action plans to reduce a user's high stress levels.

[1304] This system calculates CO2 emissions and environmental impacts in real time by having users input their daily life information and emotional state into a terminal, and provides improvement suggestions based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[1305] System configuration

[1306] User

[1307] The user inputs information about their daily life and emotional state into the terminal. The information about their daily life includes commuting distance, transportation mode, fuel consumption, etc. The emotional state includes stress, happiness, fatigue, etc.

[1308] Terminal

[1309] The devices used may be smartphones, tablets, devices installed in self-driving vehicles, etc. Users use these devices to input information about their daily lives and emotional states.

[1310] server

[1311] The server receives the daily life information and emotional state information entered by the user and performs analysis and calculations.

[1312] The system includes processing means for calculating CO2 emissions and environmental loads based on the received information.

[1313] It includes a visualization means for visually displaying information according to the calculation results and the emotional state.

[1314] The system has a suggestion means for generating improvement suggestions for reducing environmental loads and lifestyle improvement suggestions based on emotional states.

[1315] Hardware and Software

[1316] Hardware: Smartphones, tablets, and devices in self-driving vehicles.

[1317] Software: Python, HTTPS protocol, RESTful API.

[1318] Data processing and calculation

[1319] Data Entry

[1320] When a user inputs daily life information and emotional state using the device, the device sends this information to the server using the HTTPS protocol.

[1321] Data Receipt and Processing

[1322] The server calculates the CO2 emissions and environmental impact based on the received data. For example, if you commute to work every day by car, the distance is 20km, and the fuel efficiency is 10km / L, the CO2 emissions are calculated as follows:

[1323] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[1324] Emotion analysis

[1325] The emotion engine in the server analyzes the user's emotional data, and if high stress levels or fatigue are identified, it generates improvement suggestions that take these into consideration.

[1326] Proposal generation

[1327] The server generates appropriate improvement suggestions for the user based on the calculated environmental load and emotion data, such as specific action plans to use public transportation and reduce stress.

[1328] Visualization and Display

[1329] The server generates data for visually displaying the calculation results and sends it to the terminal, which then uses the received data to visually present CO2 emissions and improvement suggestions to the user in pie charts and bar graphs.

[1330] Specific examples

[1331] If a user drives 20km to work each day, with a fuel economy of 10km / L, the CO2 emissions would be calculated as follows:

[1332] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[1333] This information and the user's emotional state (e.g., stress level 7) are sent to the server, which assesses the user's high stress level and suggests using public transportation or a motorbike. It also displays the nearest bus stop, its timetable, and travel time.

[1334] Prompt Sentence Examples

[1335] Calculate CO2 emissions based on the following user data and emotional data, and generate improvement suggestions according to the emotional state.

[1336] User Data:

[1337] Commuting method: Car

[1338] Commuting distance: 20km

[1339] Fuel economy: 10km / L

[1340] Emotional Data:

[1341] Stress level: 7

[1342] Please provide specific, customized improvement suggestions.

[1343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1344] Step 1:

[1345] Users use the device to input information about their daily life and emotional state, such as commuting distance, transportation method, fuel consumption, stress level, etc. This input data will be the basis for subsequent processing.

[1346] Step 2:

[1347] The device encrypts the inputted daily life information and emotional state and sends it to the server via HTTPS, ensuring data security during data transfer.

[1348] Input: Daily life information, emotional state

[1349] Output: Encrypted data

[1350] Step 3:

[1351] The server decrypts the received encrypted data and acquires daily life information and emotional state, after which various calculation processes are initiated based on the acquired data.

[1352] Input: Encrypted data

[1353] Output: Decoded daily life information, decoded emotional state

[1354] Step 4:

[1355] The server calculates CO2 emissions and environmental impact based on daily life information. For example, if the commute distance is 20 km and the fuel efficiency is 10 km / L, the daily CO2 emissions will be 4.6 kg. This calculation result will be used in the next step.

[1356] Input: Daily Life Information

[1357] Output: CO2 emissions, environmental impact

[1358] Step 5:

[1359] The server analyzes the user's emotional state. The emotion analysis engine evaluates the input emotional data and detects high stress levels or fatigue. Based on the analysis results, improvement suggestions are generated in the next step.

[1360] Input: Emotional state

[1361] Output: Parsed emotion data

[1362] Step 6:

[1363] The server generates appropriate improvement suggestions for users based on their CO2 emissions and analyzed emotion data. For example, for users with high stress levels, it will suggest specific lifestyle improvements such as using public transportation and reducing stress. These suggestions will also include detailed information such as the nearest transportation options and timetables.

[1364] Input: CO2 emissions, analyzed emotion data

[1365] Output: Improvement suggestions

[1366] Step 7:

[1367] The server sends the generated improvement suggestions to the terminal, and the data is visualized and presented to the user in the form of pie charts and bar graphs.

[1368] Input: Improvement suggestion

[1369] Output: Visualized data

[1370] Step 8:

[1371] The device then displays the visualized data on the screen, allowing the user to intuitively understand how much CO2 their actions are emitting and what measures they can take to improve their situation.

[1372] Input: Visualized data

[1373] Output: What the user sees on their screen

[1374] Through the above processing steps, the system of the present invention can provide effective environmental load reduction measures and lifestyle improvement suggestions based on the user's daily life information and emotional state.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] [Fourth embodiment]

[1379] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1380] 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.

[1381] 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).

[1382] 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.

[1383] 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.

[1384] 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).

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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."

[1392] This system calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement proposals based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[1393] System Overview

[1394] Users use a device to input information about their daily lives. This device is an electronic device such as a smartphone or tablet. When a user inputs information into the device, the device sends the information to a server. The server processes the received information and sends the calculation results back to the device. The device then visualizes the results and presents them to the user.

[1395] Program processing flow

[1396] 1. Data Entry

[1397] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[1398] 2. Data Transmission

[1399] The device encrypts the information entered and sends it to the server, using a secure protocol such as HTTPS.

[1400] 3. Data Receipt and Processing

[1401] The server receives information about daily life sent by the user, and calculates CO2 emissions and environmental impact based on this information.

[1402] The server also retrieves relevant environmental impact data from existing databases and uses this to perform detailed calculations.

[1403] 4. Visualization of calculation results

[1404] The server generates data for visualizing the calculation results and sends it to the terminal, which can then display them in pie charts, bar graphs, or dashboard formats.

[1405] The terminal displays the received data in a graphical format to the user.

[1406] 5. Improvement proposals and action plans

[1407] Based on the calculation results, the server generates specific improvement suggestions for the user, such as using public transportation or recommending energy-efficient appliances.

[1408] A proposed action plan is also sent to the terminal for the user to review and receive in an actionable form.

[1409] Specific examples

[1410] Enter your commute method

[1411] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[1412] Commuting method: Car

[1413] Commuting distance: 20km

[1414] Fuel economy: 10km / L

[1415] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[1416] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to put their action plans into action.

[1417] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[1418] The processing flow will be explained below.

[1419] Step 1:

[1420] Users launch a dedicated application on their device and input information about their daily life, such as "I commute 20km by car every day" or "I consume 100kWh of electricity every month."

[1421] Step 2:

[1422] The device encrypts the information entered by the user and sends it to the server via the Internet, using HTTPS as the communication protocol.

[1423] Step 3:

[1424] The server analyzes the received daily life information and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)" from the database.

[1425] Step 4:

[1426] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[1427] Step 5:

[1428] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[1429] Step 6:

[1430] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[1431] Step 7:

[1432] The server generates improvement proposals for reducing the user's CO2 emissions. For example, it generates a proposal such as "Using public transportation can reduce CO2 emissions by 80%."

[1433] Step 8:

[1434] The server creates an action plan based on specific improvement suggestions and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[1435] Step 9:

[1436] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[1437] Through the above steps, users can understand the environmental impact in real time based on their daily life information and implement specific improvement measures.

[1438] Example 1

[1439] 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."

[1440] In modern society, effectively managing and reducing CO2 emissions and environmental impacts is an urgent issue. However, many people lack the means to specifically understand the environmental impact of their daily lives, and it is difficult to receive specific suggestions on how to improve their daily behavior. In addition, existing systems lack sufficient data security, making it difficult to protect user privacy.

[1441] 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.

[1442] In this invention, the server includes a terminal means for a user to input daily life information, a means for encrypting and transmitting the input daily life information, a server means for receiving the transmitted daily life information, a processing means for analyzing the received daily life information and acquiring related data, a calculation means for calculating CO2 emissions and environmental loads based on the received and analyzed daily life information, a visualization means for visually displaying the calculation results, a proposal means for generating improvement proposals to reduce environmental loads based on the calculation results, and a display means for feeding back the generated visualization data and improvement proposals to the user. This makes it possible to provide specific and feasible measures to reduce environmental loads while protecting the user's privacy.

[1443] "User" refers to an individual or group who uses the system to input and check daily life information and implement suggested improvement measures.

[1444] "Terminal means" refers to electronic devices used by users to input daily life information, and includes smartphones, tablets, and the like.

[1445] "Encryption methods" are technologies used to securely convert data entered by users so that it cannot be read by third parties. This includes the AES encryption protocol.

[1446] "Transmission means" refers to a function for securely transferring encrypted data to a server, and includes the HTTPS protocol, etc.

[1447] The "server means" is a central processing unit for receiving and processing data sent from the terminal.

[1448] "Processing means" is a function for analyzing received data and generating calculations and proposals based on the data.

[1449] "Calculation means" refers to a function for numerically calculating CO2 emissions and environmental impact based on daily life information. This includes the Python NumPy library.

[1450] "Visualization methods" are techniques for visually displaying calculation results in an easy-to-understand manner, including pie charts and bar graphs.

[1451] The "proposal means" is a function that allows the user to generate specific and feasible improvement proposals based on the calculation results.

[1452] The "display means" is a function for providing feedback to the user on the generated visualization data and improvement suggestions.

[1453] The present invention is a system that allows users to input daily life information into a terminal, calculates CO2 emissions and environmental impact in real time, and provides improvement proposals based on the information. This system is composed of multiple means including users, terminals, and a server.

[1454] System Overview

[1455] Users launch a dedicated application on a device such as a smartphone or tablet and input specific information about their daily activities (e.g., their daily commute route, the amount of electricity they use, etc.). The device then encrypts this information and sends it to a server using a secure protocol (e.g., HTTPS).

[1456] The server analyzes the received information and calculates CO2 emissions and environmental impact. This calculation uses the Python NumPy library and environmental impact data obtained from an existing database. The calculation results are visualized in the form of pie charts and bar graphs and sent to the terminal, which displays them on a user interface.

[1457] The server then generates specific improvement suggestions for the user based on the calculation results, such as using public transportation or recommending energy-efficient appliances. These improvement suggestions are sent to the device along with additional information such as the nearest bus or train station, timetables, and travel times, and are provided in a form that the user can easily implement.

[1458] Specific examples

[1459] Enter your commute method

[1460] If a user commutes 20km by car each day, they enter the following information into their terminal:

[1461] Commuting method: Car

[1462] Commuting distance: 20km

[1463] Fuel economy: 10km / L

[1464] The device encrypts this information and sends it to a server, which calculates that a 20km commute results in approximately 4.6kg of CO2 emissions per day. The server also calculates monthly and annual total emissions and visualizes them in pie and bar charts before sending them to the device. Users can intuitively view the results in the app.

[1465] Furthermore, the server uses this information to generate specific improvement suggestions, such as "Using public transportation can reduce CO2 emissions by approximately 80%." These suggestions also include information on the nearest bus and train stops, timetables, and travel times, allowing users to easily implement the suggestions.

[1466] Example prompts for generative AI models

[1467] "I commute 20km by car every day. What is the CO2 emissions in this case? Can you give me some concrete suggestions for reducing my environmental impact?"

[1468] In this way, the present invention is a system that helps users to review parts of their daily lives and reduce environmental impact through specific actions.

[1469] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1470] Step 1: Data entry

[1471] The user starts the dedicated application on the device.

[1472] The user inputs specific daily activities (e.g., daily commuting method, commuting distance, power consumption, etc.) As an input example, the user inputs information for commuting by car, commuting distance of 20 km, and fuel efficiency of 10 km / L.

[1473] Input data:

[1474] Commuting method: Car

[1475] Commuting distance: 20km

[1476] Fuel economy: 10km / L

[1477] Output data:

[1478] The terminal holds the daily life information input by the user.

[1479] Step 2: Send data

[1480] The terminal encrypts the input data using an encryption protocol (e.g., AES).

[1481] The device sends the encrypted data to the server using the HTTPS protocol.

[1482] Input data:

[1483] Daily life information entered by the user

[1484] Output data:

[1485] The encrypted daily life information is sent to a server.

[1486] Step 3: Data Receipt and Decryption

[1487] The server receives the encrypted data sent from the terminal.

[1488] The server decrypts the encrypted data and retrieves the original daily life information.

[1489] Input data:

[1490] Encrypted daily life information

[1491] Output data:

[1492] Decrypted daily life information

[1493] Step 4: Acquire and analyze relevant data

[1494] The server parses the received data.

[1495] The server retrieves relevant data (e.g., CO2 emission coefficient of fuel) from an internal database.

[1496] Input data:

[1497] Decrypted daily life information

[1498] Output data:

[1499] Complete dataset with related data

[1500] Step 5: Calculate CO2 emissions and environmental impact

[1501] The server uses the relevant data to calculate CO2 emissions and environmental impact. For example, if the commute distance is 20km and the fuel efficiency is 10km / L, the amount of gasoline consumed is 2L, which will emit 4.6kg of CO2.

[1502] The server adds the calculation results to the dataset.

[1503] Input data:

[1504] Complete dataset with related data

[1505] Output data:

[1506] Dataset containing calculation results of CO2 emissions and environmental impact

[1507] Step 6: Visualizing the results

[1508] The server generates data (e.g., pie charts, bar chart data points) to visualize the calculated CO2 emissions and environmental impact.

[1509] The server sends the visualization data to the terminal.

[1510] Input data:

[1511] Dataset containing calculation results of CO2 emissions and environmental impact

[1512] Output data:

[1513] Visualized Data

[1514] Step 7: Generate improvement suggestions

[1515] Based on the calculation results, the server generates specific improvement proposals for the user, such as "Using public transportation can reduce CO2 emissions by 80%."

[1516] The server retrieves more detailed data (e.g., bus and train stops, timetables, and travel times) from an external API (e.g., a transportation data provider).

[1517] Input data:

[1518] Calculation results of CO2 emissions and environmental impact

[1519] Additional data from external APIs

[1520] Output data:

[1521] Dataset containing improvement suggestions

[1522] Step 8: User Feedback

[1523] The terminal displays the visualization data and improvement suggestions received from the server on the user interface.

[1524] The user reviews the displayed data and takes action based on the suggested improvements, if necessary.

[1525] Input data:

[1526] A dataset containing visualization data and improvement suggestions

[1527] Output data:

[1528] Information displayed in the user interface

[1529] (Application example 1)

[1530] 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."

[1531] In modern society, it is difficult for individuals to grasp CO2 emissions and environmental impacts in real time and implement appropriate improvement measures based on that information. Existing systems lack the ability to provide effective improvement suggestions based on the daily life information entered by the user, and they rarely provide suggestions that allow users to easily take concrete action. Furthermore, the proposed improvement measures must be specific.

[1532] 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.

[1533] In this invention, the server includes an input device means for a user to input daily life information, a server means for receiving the input daily life information, a data processing means for calculating CO2 emissions and environmental load based on the received daily life information, a data visualization means for visually displaying the calculation results, a proposal generation means for generating improvement proposals to reduce the environmental load, and a generation means for realizing the improvement proposals using a generative AI model. This allows the user to visually grasp the extent to which their lifestyle habits affect the environment and receive specific and actionable improvement proposals.

[1534] "Input device means" refers to a device used by a user to input information about daily life, and includes a smartphone, tablet, etc.

[1535] "Server means" is a computer system for receiving and processing information sent from input device means.

[1536] "Data processing means" refers to a software or hardware configuration for calculating CO2 emissions and environmental loads based on received daily life information.

[1537] "Data visualization means" is a function for visually presenting calculated results to the user, and includes display in pie charts, bar graphs, and dashboard formats.

[1538] "Proposal generation means" refers to software or algorithms that create specific improvement proposals for reducing environmental impact based on the calculation results.

[1539] The "generation means" is a system that uses a generative AI model to concretize improvement suggestions for users, and is a means of providing specific action plans.

[1540] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate optimal improvement suggestions based on user input information.

[1541] "User" refers to an individual who uses this system to input information about their daily life and receive suggestions for improving their CO2 emissions and environmental impact.

[1542] The present invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives, and provides specific improvement proposals based on the calculations. This system is composed of multiple means, including a user, an input device means, and a server means.

[1543] System Overview

[1544] User Operation

[1545] Users input information about their daily lives using input devices such as smartphones and tablets. For example, the user might input information such as "I use a car to commute to work every day." They also input detailed information such as the fuel efficiency of the car they use and the distance they commute to work.

[1546] Data transmission

[1547] The input device means transmits the input information to the server means, using a secure protocol such as HTTPS for this communication to ensure the integrity and security of the data.

[1548] Data Processing

[1549] The server has a data processing unit for calculating CO2 emissions and environmental load based on the received daily life information. Specifically, it uses an algorithm to calculate daily CO2 emissions from distance and fuel efficiency information, and calculates the cumulative value.

[1550] Data Visualization

[1551] The calculated results are presented to the user visually using data visualization tools, such as pie charts, bar graphs, and dashboard-style interfaces, in a way that is intuitively understandable to the user.

[1552] Improvement proposal generation

[1553] The server means also includes a proposal generation means for generating specific improvement proposals for reducing environmental impacts based on the received data and the calculation results. The improvement proposals are further embodied using a generative AI model. The generation means analyzes the user's behavioral patterns and presents optimal improvement measures.

[1554] Specific examples

[1555] For example, suppose a user commutes to work every day by car, travels 20 km per day, and has a fuel efficiency of 10 km / L. This information is sent to the server means, which then calculates the amount of CO2 emissions based on this information. The calculation result shows that the user emits approximately 4 kg of CO2 per day of commuting.

[1556] Furthermore, the server means uses this information to suggest "using public transportation." The generation means provides information on the user's nearest bus stops and train stations, timetables, and travel times, and presents improvement suggestions in a form that the user can put into practice. These suggestions are created using a generative AI model, so they present the optimal options for the user.

[1557] Example prompts for generative AI models

[1558] "The user commutes 20km by car every day, with a fuel efficiency of 10km / L. Calculate the monthly and annual CO2 emissions in this case, and generate a concrete proposal for the CO2 reduction effect of using public transport."

[1559] As described above, the present invention is a system that helps users to review parts of their daily lives and reduce environmental loads through specific actions.

[1560] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1561] Step 1:

[1562] The user inputs daily life information.

[1563] A user uses a smartphone or tablet to input information about daily life such as the means of commuting, commuting distance, fuel efficiency of the car used, etc. The input device means receives this information and transmits it to the server means in a prepared data format.

[1564] Input: Information such as commute method, commute distance, and fuel economy

[1565] Output: Formatted data sent to a server means

[1566] Step 2:

[1567] The terminal transmits the input information to the server.

[1568] The input device means transmits the input information to the server means using the HTTPS protocol, and the server receives the data in an encrypted form.

[1569] Input: Information entered by the user

[1570] Output: Encrypted data received by the server.

[1571] Step 3:

[1572] The server processes the received information and calculates the CO2 emissions.

[1573] The server uses the data processing means to calculate the amount of CO2 emissions and the environmental load based on the received data. Specifically, it calculates the amount of CO2 emissions per day and cumulative CO2 emissions based on the commuting distance and fuel efficiency information.

[1574] Input: User's life data encrypted and sent to the server

[1575] Output: Calculated CO2 emissions (e.g. 4 kg of CO2 emissions per day of commuting)

[1576] Step 4:

[1577] The server generates data to visualize the calculation results.

[1578] The server means generates data based on the calculation results using the data visualization means to provide the data visually to the user, which is converted into a pie chart or a bar graph, allowing the user to intuitively understand the CO2 emissions.

[1579] Input: Calculated CO2 emissions

[1580] Output: Visualized data (e.g., pie chart, bar graph format)

[1581] Step 5:

[1582] The server generates improvement suggestions.

[1583] The server means uses the proposal generation means to generate specific improvement proposals for reducing environmental impact based on the calculation results. Furthermore, the server means uses the generative AI model to materialize the proposals. For example, the server means may suggest using public transportation, and provide information on the nearest bus stops and train stations, as well as timetables.

[1584] Input: Calculation results and user's life data

[1585] Output: Specific improvement suggestions (e.g., public transport use and details)

[1586] Step 6:

[1587] The user receives the proposal and selects what to do.

[1588] The input device notifies the user of the improvement proposals and presents a specific action plan, and the user checks the proposals and selects an actionable proposal.

[1589] Input: Specific improvement suggestions

[1590] Output: An action plan provided to the user in an executable form

[1591] Through these steps, users can review parts of their daily lives and receive help in reducing environmental impact through concrete actions.

[1592] 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.

[1593] This invention is a system that calculates CO2 emissions and environmental impacts in real time by having users input information about their daily lives into a terminal, and provides improvement suggestions based on the results. This system is composed of multiple means including the user, terminal, and server, and is also combined with an emotion engine that recognizes the user's emotions.

[1594] System Overview

[1595] Users use a device to input their daily life information and emotional state. This device is composed of an electronic device such as a smartphone or tablet. When the user inputs information into the device, the device sends this information to a server. The server processes the received information, analyzes the emotional data along with the calculation results, and sends them back to the device. The results are presented as improvement suggestions based on the user's emotions.

[1596] Program processing flow

[1597] 1. Data Entry

[1598] Users launch a dedicated application on their device and input specific daily activities (e.g., the means of transportation used for daily commuting, the amount of electricity used, etc.).

[1599] Additionally, the user inputs their current emotional state (e.g., stress, happiness, fatigue, etc.).

[1600] 2. Data Transmission

[1601] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using HTTPS as the communication protocol.

[1602] 3. Data Receipt and Processing

[1603] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server retrieves basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[1604] The emotion engine analyzes users' emotion data and identifies effective improvement measures for users with high levels of stress or fatigue.

[1605] 4. Visualization of calculation results

[1606] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will emit 4kg of CO2. It also calculates monthly and annual total CO2 emissions.

[1607] The server converts the results of the calculations into visual data, specifically generating data to display the results in pie charts, bar graphs, dashboard formats, etc.

[1608] 5. Emotion-based improvement suggestions and action plans

[1609] The server sends the generated visual data to the device, which receives it and seamlessly displays it to the user, allowing the user to intuitively understand how much CO2 their actions are emitting.

[1610] The server generates improvement suggestions based on the user's emotional state based on the emotional data analyzed by the emotion engine. For example, for a user experiencing high stress, it suggests relaxation activities or environmentally friendly initiatives.

[1611] 6. Information transmission and display

[1612] The server then sends the generated improvement proposals and action plan to the device, which includes detailed information such as the nearest bus stop, timetable, and travel time.

[1613] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[1614] Specific examples

[1615] Entering commuting methods and recognizing emotions

[1616] If a user commutes to work every day by car (20km), he / she enters the following information into the terminal:

[1617] Commuting method: Car

[1618] Commuting distance: 20km

[1619] Fuel economy: 10km / L

[1620] Emotional state: Stressed (high)

[1621] The device sends this information to a server, which calculates that a 20km commute results in approximately 4kg of CO2 emissions per day. It also calculates monthly and yearly totals. The results are sent to the device in the form of pie charts and bar graphs, allowing users to intuitively understand the environmental impact of their commute.

[1622] Furthermore, the server analyzes the user's emotional data and, if a high stress level is detected, provides suggestions for improvement (e.g., changing to public transportation or commuting by bicycle to achieve relaxation). These suggestions also include information on the nearest bus and train stops, timetables, and travel times, making it easy for users to execute their action plans.

[1623] In this way, the present invention is a system that combines information about a user's daily life with emotional data to realize more effective measures to reduce environmental load and improve quality of life.

[1624] The processing flow will be explained below.

[1625] Step 1:

[1626] Users launch a dedicated application on their device and input specific daily activities (e.g., the mode of transportation they use to commute to work each day, the amount of electricity they use, etc.) and their current emotional state (e.g., stress, happiness, fatigue, etc.).

[1627] Step 2:

[1628] The device encrypts the daily life information and emotion data entered by the user and sends it to a server via the Internet using the secure HTTPS communication protocol.

[1629] Step 3:

[1630] The server analyzes the received daily life information and emotion data and begins calculating the environmental impact. If the received data is "commuting 20km by car every day," the server will retrieve basic data from the database, such as "car fuel efficiency (e.g., 10km / L)" and "CO2 emissions per liter (e.g., 2.3kg)."

[1631] Step 4:

[1632] The server calculates specific CO2 emissions based on the acquired basic data. For example, it calculates that a daily 20km commute will result in 4kg of CO2 emissions. It also calculates monthly and annual total CO2 emissions.

[1633] Step 5:

[1634] The server converts the results of the calculations into visual data, specifically generating data to display the results in formats such as pie charts and bar graphs.

[1635] Step 6:

[1636] The server sends the generated visual data to the device, which receives it and visually presents it to the user through an interface, allowing the user to intuitively understand how much CO2 their actions are emitting.

[1637] Step 7:

[1638] The server uses an emotion engine to analyze the user's emotion data. For example, if the user inputs a high stress level, the emotion engine generates improvement suggestions that take into account the user's stress reduction.

[1639] Step 8:

[1640] The server generates customized improvement suggestions based on the analysis results of the emotion engine, such as suggesting using public transportation and adding an explanation for the reason, such as "to reduce stress."

[1641] Step 9:

[1642] The server creates a specific action plan and sends it to the device, including details such as the nearest bus stop, timetable, and travel time.

[1643] Step 10:

[1644] The device will then display the received improvement suggestions and action plans to the user, who can refer to them and obtain specific steps to take to improve their lifestyle habits.

[1645] Through these steps, users can understand their environmental impact in real time based on their daily life information and implement specific improvement measures. In addition, by making suggestions that take into account the user's emotional state, it is possible to achieve even more effective lifestyle improvements and reductions in environmental impact.

[1646] Example 2

[1647] 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."

[1648] As environmental problems become more serious in recent years, concrete measures to reduce CO2 emissions and environmental impacts are required. However, the current situation is that there are insufficient means for individual users to understand the environmental impact of their daily actions and take effective measures to improve their lives. Another problem is that there are no systems that can propose optimal improvement measures while taking into account the user's emotional state. This makes it difficult to sustain efforts to reduce environmental impacts.

[1649] 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.

[1650] In this invention, the server includes a processing means for calculating CO2 emissions and environmental loads based on the received daily life information, an emotion analysis means for analyzing the calculation results and emotion information and generating improvement suggestions according to the user's emotional state, and a visualization means for visually displaying the calculation results and improvement suggestions. This makes it possible to calculate and visualize CO2 emissions based on the user's daily life information and emotion data, and to provide improvement suggestions that are optimal for the user's emotional state.

[1651] A "user" is an individual who utilizes the system to input daily life information and emotional information.

[1652] "Terminal means" refers to electronic devices that allow users to input daily life information and emotion information, and particularly includes devices such as smartphones and tablets.

[1653] The term "server means" refers to a computer system that receives and processes information sent from the terminal means.

[1654] "Daily life information" refers to information about the user's daily life behaviors and activities, such as the user's means of commuting and power consumption.

[1655] "Emotional information" refers to the user's own emotional state (e.g., stress, happiness, fatigue, etc.) entered by the user.

[1656] "Processing means" refers to computer programs and computing resources for calculating CO2 emissions and environmental loads based on the received daily life information.

[1657] "Emotion analysis means" refers to algorithms and software for analyzing received emotional information and generating improvement suggestions according to the user's emotional state.

[1658] "Visualization means" refers to software and a user interface for visually displaying the calculation results and improvement suggestions in the form of graphs and dashboards.

[1659] "Improvement proposal" refers to a specific proposal for reducing environmental impact that is generated based on the user's daily life information and emotional information.

[1660] "Public transportation" refers to means of transportation such as buses, trains, and subways that can be used by an unspecified number of people.

[1661] "Bicycle commuting" refers to the act of a user commuting to work by bicycle.

[1662] The above are definitions of important words included in the claims.

[1663] The present invention is a system in which a user inputs daily life information and emotional information through a terminal, a server calculates CO2 emissions and environmental load based on that information, and provides improvement proposals that take the user's emotional state into consideration. This system is composed of multiple elements, including a user, a terminal, and a server.

[1664] System configuration

[1665] 1. Terminal means

[1666] Users input daily life information and emotional information using electronic devices such as smartphones and tablets. The applications used for this purpose provide input forms that allow users to easily input information.

[1667] 2. Server Means

[1668] The server that receives the encrypted data sent from the terminal has a processing means for performing calculations. The server has the following functions:

[1669] Decrypting Data

[1670] Calculation of CO2 emissions and environmental impact based on received daily life information

[1671] Emotional information analysis

[1672] 3. Processing means

[1673] The server runs a program to calculate CO2 emissions based on the received daily life information and emotion information. In particular, it uses the following software and libraries:

[1674] Manage basic data on CO2 emissions using database software (e.g., MySQL)

[1675] A programming language that performs calculations (e.g., Python)

[1676] Generate visual data with a data visualization library (e.g., D3.js, Chart.js)

[1677] 4. Emotion analysis method

[1678] The server is equipped with an emotion engine to analyze the user's emotional information. The emotion engine evaluates the user's emotional state (e.g., stress, happiness, fatigue) and generates optimal improvement suggestions based on that.

[1679] 5. Visualization means

[1680] The server generates data to display the calculation results and sentiment-based improvement suggestions as visually rich data in the form of pie charts and bar graphs, which are then sent back to the device and displayed seamlessly to the user.

[1681] Specific examples

[1682] Data entry and submission

[1683] The user enters information about their daily commute, for example:

[1684] Commuting method: Car

[1685] Commuting distance: 20km

[1686] Fuel economy: 10km / L

[1687] Emotional state: Stress (high)

[1688] By entering this information into the input form and pressing the "Submit" button, the information will be encrypted and sent to the server.

[1689] Data reception and processing

[1690] The server receives the data, decrypts it, and then does the following:

[1691] Obtain basic data from a database (e.g., vehicle fuel efficiency and CO2 emissions)

[1692] Calculates CO2 emissions based on received information (e.g., 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[1693] Analyzes emotional information with an emotion engine and generates optimal improvement suggestions for users

[1694] Visualization of calculation results and improvement suggestions

[1695] The server generates data for visualizing the calculation results, such as in the form of pie charts or bar graphs, and generates improvement suggestions based on the emotion information (e.g., using public transport or commuting by bicycle), along with detailed information (e.g., the nearest bus stop, operating times, and travel time).

[1696] Sending and Displaying Information

[1697] The server sends the data to the device, which then displays it to the user. The user can then improve their lifestyle based on the suggestions. The system also has a reminder function to help users make continuous improvements.

[1698] Prompt Sentence Examples

[1699] Examples of prompts that users can enter into a generative AI model:

[1700] "I commute to work every day by car (20km) and it's stressful. Please calculate my CO2 emissions and suggest some environmentally friendly improvements."

[1701] In this way, the present invention is a specific system that combines information about a user's daily life with emotional data to realize effective measures to reduce environmental load and improve quality of life.

[1702] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1703] Step 1: Data entry

[1704] The user inputs information about their daily life and emotions through a dedicated application. Specifically, they input their commute mode, commute distance, fuel consumption, and current emotional state into an input form, and then press the "Submit" button.

[1705] Input: Commuting method (e.g., car), commuting distance (e.g., 20 km), fuel economy (e.g., 10 km / L), emotional state (e.g., stress (high))

[1706] Output: Information data entered into the terminal

[1707] Step 2: Send data

[1708] The terminal encrypts the information entered by the user and sends it to the server using AES (Advanced Encryption Standard) and the HTTPS protocol to ensure secure communication.

[1709] Input: Information data entered by the user

[1710] Data processing: AES encryption

[1711] Output: Encrypted information data

[1712] Step 3: Receiving and Decrypting Data

[1713] The server receives the encrypted information data, decrypts it, analyzes the received data, and performs the necessary processing.

[1714] Input: Encrypted information data

[1715] Data processing: AES decryption

[1716] Output: Decoded information data (commuting method, commuting distance, fuel consumption, emotional state)

[1717] Step 4: CO2 emissions and environmental impact calculations

[1718] The server calculates CO2 emissions and environmental impact based on the decrypted information. Specifically, it obtains basic data on fuel efficiency and CO2 emissions from the database and performs the following calculations.

[1719] Input: Decoded information data (commuting method, commuting distance, fuel consumption)

[1720] Data calculation: CO2 emissions calculation (Example: 20km ÷ 10km / L × 2.3kg = 4.6kg CO2)

[1721] Output: Calculation result (CO2 emissions)

[1722] Step 5: Sentiment Analysis

[1723] The server analyzes the user's emotional information using an emotion engine, which identifies optimal improvement suggestions based on the user's emotional data (e.g., stress (high)).

[1724] Input: Emotional state (e.g., stress (high))

[1725] Data Computation: Emotional Data Analysis

[1726] Output: Analysis results (optimal improvement suggestions based on the user's emotional state)

[1727] Step 6: Visualizing the results

[1728] The server generates data to visually display the calculation results and improvement suggestions, using a data visualization library (e.g., D3.js, Chart.js) to display them in the form of pie charts or bar graphs.

[1729] Input: Calculation results (CO2 emissions), analysis results (improvement proposals)

[1730] Data processing: Data visualization

[1731] Output: Visualized data (graph format)

[1732] Step 7: Submitting results and suggestions

[1733] The server sends the generated visualization data and improvement suggestions to the terminal.

[1734] Input: Visualization data, improvement suggestions

[1735] Data processing: Data packaging

[1736] Output: Transmitted data

[1737] Step 8: Viewing results and suggestions

[1738] The device receives the visualized data and improvement suggestions from the server and displays them to the user. The user can view the data visualized in pie charts and bar graphs, along with the improvement suggestions based on their emotional state.

[1739] Input: Incoming data (visualization data, improvement suggestions)

[1740] Output: Results and suggestions displayed in the user interface

[1741] This allows users to understand the impact their actions have on the environment and take appropriate measures to improve the situation.

[1742] (Application example 2)

[1743] 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."

[1744] Conventional CO2 emission calculation systems calculate environmental impact based on the user's daily life information and display the results. However, because they do not take the user's emotional state into account, they do not provide appropriate improvement suggestions that reflect personal factors such as stress and physical condition. This also creates the problem of making it difficult for users to improve their environmental awareness and lifestyle habits. Furthermore, because it is difficult to recognize and reflect the user's emotional state in real time, effective improvement suggestions are lacking.

[1745] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's daily life information and emotional state, means for calculating CO2 emissions and environmental load based on the received information, means for visually displaying information corresponding to the calculation results and the emotional state, and means for generating improvement suggestions for reducing the environmental load and lifestyle improvement suggestions based on the emotional state. This enables more effective environmental improvement and quality of life improvement that takes the user's emotional state into consideration.

[1746] "User" refers to an individual who utilizes the system to input daily life information and emotional state.

[1747] "Daily life information" refers to specific information about the user's daily life, such as means of transportation, commuting distance, and amount of fuel used.

[1748] "Emotional state" refers to a user's mental and emotional state, such as stress, happiness, fatigue, etc.

[1749] "Terminal means" refers to electronic devices used by users to input and receive information, such as smartphones, tablets, and devices installed in autonomous vehicles.

[1750] "Server means" refers to a central computing device for processing and storing daily life information and emotional state information received from a user, and transmitting the calculation and analysis results to a terminal.

[1751] "Processing means" refers to a computing device that includes algorithms and programs to calculate CO2 emissions and environmental loads based on daily life information and emotional state information.

[1752] "Visualization means" refers to devices and software for visually displaying information according to calculation results and emotional states in the form of pie charts, bar graphs, and dashboards.

[1753] The "suggestion means" refers to an algorithm and a program for generating improvement suggestions for reducing environmental impact and lifestyle improvement suggestions based on emotional states.

[1754] "Means of transportation" refers to all modes of transportation that users use on a daily basis, such as cars, public transportation, and bicycles.

[1755] "Using public transport" refers to using transportation provided by a public transport operator, such as a bus, train or subway.

[1756] "Stress reduction" refers to improvement suggestions and specific lifestyle action plans to reduce a user's high stress levels.

[1757] This system calculates CO2 emissions and environmental impacts in real time by having users input their daily life information and emotional state into a terminal, and provides improvement suggestions based on the calculations. This system is composed of multiple means, including users, terminals, and a server.

[1758] System configuration

[1759] User

[1760] The user inputs information about their daily life and emotional state into the terminal. The information about their daily life includes commuting distance, transportation mode, fuel consumption, etc. The emotional state includes stress, happiness, fatigue, etc.

[1761] Terminal

[1762] The devices used may be smartphones, tablets, devices installed in self-driving vehicles, etc. Users use these devices to input information about their daily lives and emotional states.

[1763] server

[1764] The server receives the daily life information and emotional state information entered by the user and performs analysis and calculations.

[1765] The system includes processing means for calculating CO2 emissions and environmental loads based on the received information.

[1766] It includes a visualization means for visually displaying information according to the calculation results and the emotional state.

[1767] The system has a suggestion means for generating improvement suggestions for reducing environmental loads and lifestyle improvement suggestions based on emotional states.

[1768] Hardware and Software

[1769] Hardware: Smartphones, tablets, and devices in self-driving vehicles.

[1770] Software: Python, HTTPS protocol, RESTful API.

[1771] Data processing and calculation

[1772] Data Entry

[1773] When a user inputs daily life information and emotional state using the device, the device sends this information to the server using the HTTPS protocol.

[1774] Data Receipt and Processing

[1775] The server calculates the CO2 emissions and environmental impact based on the received data. For example, if you commute to work every day by car, the distance is 20km, and the fuel efficiency is 10km / L, the CO2 emissions are calculated as follows:

[1776] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[1777] Emotion analysis

[1778] The emotion engine in the server analyzes the user's emotional data, and if high stress levels or fatigue are identified, it generates improvement suggestions that take these into consideration.

[1779] Proposal generation

[1780] The server generates appropriate improvement suggestions for the user based on the calculated environmental load and emotion data, such as specific action plans to use public transportation and reduce stress.

[1781] Visualization and Display

[1782] The server generates data for visually displaying the calculation results and sends it to the terminal, which then uses the received data to visually present CO2 emissions and improvement suggestions to the user in pie charts and bar graphs.

[1783] Specific examples

[1784] If a user drives 20km to work each day, with a fuel economy of 10km / L, the CO2 emissions would be calculated as follows:

[1785] 20km ÷ 10km / L × 2.3kg = 4.6kgCO2 / day

[1786] This information and the user's emotional state (e.g., stress level 7) are sent to the server, which assesses the user's high stress level and suggests using public transportation or a motorbike. It also displays the nearest bus stop, its timetable, and travel time.

[1787] Prompt Sentence Examples

[1788] Calculate CO2 emissions based on the following user data and emotional data, and generate improvement suggestions according to the emotional state.

[1789] User Data:

[1790] Commuting method: Car

[1791] Commuting distance: 20km

[1792] Fuel economy: 10km / L

[1793] Emotional Data:

[1794] Stress level: 7

[1795] Please provide specific, customized improvement suggestions.

[1796] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1797] Step 1:

[1798] Users use the device to input information about their daily life and emotional state, such as commuting distance, transportation method, fuel consumption, stress level, etc. This input data will be the basis for subsequent processing.

[1799] Step 2:

[1800] The device encrypts the inputted daily life information and emotional state and sends it to the server via HTTPS, ensuring data security during data transfer.

[1801] Input: Daily life information, emotional state

[1802] Output: Encrypted data

[1803] Step 3:

[1804] The server decrypts the received encrypted data and acquires daily life information and emotional state, after which various calculation processes are initiated based on the acquired data.

[1805] Input: Encrypted data

[1806] Output: Decoded daily life information, decoded emotional state

[1807] Step 4:

[1808] The server calculates CO2 emissions and environmental impact based on daily life information. For example, if the commute distance is 20 km and the fuel efficiency is 10 km / L, the daily CO2 emissions will be 4.6 kg. This calculation result will be used in the next step.

[1809] Input: Daily Life Information

[1810] Output: CO2 emissions, environmental impact

[1811] Step 5:

[1812] The server analyzes the user's emotional state. The emotion analysis engine evaluates the input emotional data and detects high stress levels or fatigue. Based on the analysis results, improvement suggestions are generated in the next step.

[1813] Input: Emotional state

[1814] Output: Parsed emotion data

[1815] Step 6:

[1816] The server generates appropriate improvement suggestions for users based on their CO2 emissions and analyzed emotion data. For example, for users with high stress levels, it will suggest specific lifestyle improvements such as using public transportation and reducing stress. These suggestions will also include detailed information such as the nearest transportation options and timetables.

[1817] Input: CO2 emissions, analyzed emotion data

[1818] Output: Improvement suggestions

[1819] Step 7:

[1820] The server sends the generated improvement suggestions to the terminal, and the data is visualized and presented to the user in the form of pie charts and bar graphs.

[1821] Input: Improvement suggestion

[1822] Output: Visualized data

[1823] Step 8:

[1824] The device then displays the visualized data on the screen, allowing the user to intuitively understand how much CO2 their actions are emitting and what measures they can take to improve their situation.

[1825] Input: Visualized data

[1826] Output: What the user sees on their screen

[1827] Through the above processing steps, the system of the present invention can provide effective environmental load reduction measures and lifestyle improvement suggestions based on the user's daily life information and emotional state.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] FIG. 9 illustrates 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 behaviors 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.

[1833] 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.

[1834] 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).

[1835] 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, where situational awareness is dominant.

[1836] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1837] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1838] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1839] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1840] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1841] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1842] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1843] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1844] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1845] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1846] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1847] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1848] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1849] The following is further disclosed regarding the above embodiment.

[1850] (Claim 1)

[1851] a terminal means for a user to input daily life information;

[1852] a server means for receiving input daily life information;

[1853] a processing means for calculating CO2 emissions and environmental loads based on the received daily life information;

[1854] a visualization means for visually displaying the calculation results;

[1855] a suggestion means for generating improvement suggestions for reducing environmental impact;

[1856] A system including:

[1857] (Claim 2)

[1858] 10. The system of claim 1, wherein the daily living information includes information about transportation.

[1859] (Claim 3)

[1860] 10. The system of claim 1, wherein the improvement suggestions include using public transportation.

[1861] "Example 1"

[1862] (Claim 1)

[1863] a terminal means for a user to input daily life information;

[1864] A means for encrypting and transmitting inputted daily life information;

[1865] a server means for receiving the transmitted daily life information;

[1866] processing means for analyzing the received daily life information and obtaining relevant data;

[1867] A calculation means for calculating CO2 emissions and environmental loads based on the received and analyzed daily life information;

[1868] a visualization means for visually displaying the calculation results;

[1869] a proposal means for generating improvement proposals for reducing environmental loads based on the calculation results;

[1870] a display means for providing feedback to the user on the generated visualization data and improvement suggestions;

[1871] A system including:

[1872] (Claim 2)

[1873] 10. The system of claim 1, wherein the daily living information includes information regarding transportation.

[1874] (Claim 3)

[1875] 10. The system of claim 1, wherein the improvement suggestions include using public transportation.

[1876] "Application Example 1"

[1877] (Claim 1)

[1878] an input device means for a user to input daily life information;

[1879] a server means for receiving input daily life information;

[1880] data processing means for calculating CO2 emissions and environmental loads based on the received daily life information;

[1881] a data visualization means for visually displaying the calculation results;

[1882] a proposal generation means for generating improvement proposals for reducing environmental load;

[1883] a generating means for utilizing the generative AI model to materialize improvement proposals;

[1884] A system including:

[1885] (Claim 2)

[1886] 10. The system of claim 1, wherein the daily living information includes information about transportation.

[1887] (Claim 3)

[1888] 10. The system of claim 1, wherein the improvement suggestions include using public transportation.

[1889] "Example 2: Combining Emotion Engines"

[1890] (Claim 1)

[1891] a terminal means for a user to input daily life information and emotion information;

[1892] a server means for receiving input daily life information and emotion information;

[1893] processing means for calculating CO2 emissions and environmental loads based on the received daily life information;

[1894] emotion analysis means for analyzing the calculation results and emotion information and generating improvement suggestions according to the user's emotional state;

[1895] a visualization means for visually displaying the calculation results and improvement suggestions;

[1896] A system including:

[1897] (Claim 2)

[1898] 10. The system of claim 1, wherein the daily living information includes information about transportation and electricity usage.

[1899] (Claim 3)

[1900] 2. The system of claim 1, wherein the improvement suggestions include using public transportation and commuting by bicycle.

[1901] "Application example 2 when combining emotion engines"

[1902] (Claim 1)

[1903] a terminal means for a user to input daily life information and emotional state;

[1904] a server means for receiving input daily life information and emotional states;

[1905] processing means for calculating CO2 emissions and environmental loads based on the received daily life information and emotional state;

[1906] visualization means for visually displaying information according to the calculation results and the emotional state;

[1907] a suggestion means for generating improvement suggestions for reducing environmental loads and lifestyle improvement suggestions based on emotional states;

[1908] A system including:

[1909] (Claim 2)

[1910] 10. The system of claim 1, wherein the daily living information includes information about transportation.

[1911] (Claim 3)

[1912] 10. The system of claim 1, wherein the improvement suggestions include using public transportation and reducing stress. [Explanation of symbols]

[1913] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal means for a user to input daily life information; a server means for receiving input daily life information; a processing means for calculating CO2 emissions and environmental loads based on the received daily life information; a visualization means for visually displaying the calculation results; a suggestion means for generating improvement suggestions for reducing environmental impact; A system including:

2. The system of claim 1 , wherein the daily living information includes information about transportation.

3. The system of claim 1 , wherein the improvement suggestions include using public transportation.

Images