System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input behavioral data; means for a terminal to transmit the behavioral data to a server; means for the server to analyze the behavioral data and assess environmental impact; means for the server to generate sustainable options based on the assessment results; and means for the server to provide the options to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Environmental problems are becoming more serious in modern society, and there is a need to accurately understand the environmental impacts of individual and corporate actions and promote sustainable choices. However, traditional methods for assessing environmental impacts are complex, costly, and require specialized knowledge, making them difficult for individuals and small and medium-sized enterprises to access. Furthermore, educators and policymakers face challenges in sorting through large amounts of data and deriving practical improvement measures. This creates a problem of slow progress in raising environmental awareness and improving concrete behavior. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system in which a user inputs behavioral data and transmits the data to a server via a terminal. The server analyzes the received data and evaluates the environmental impact. The system further includes a means for generating sustainable options based on the evaluation results and providing them to the user.

[0006] Specifically, the server checks behavioral data for errors and visualizes the analysis results for users. The server also generates environmental impact reports for companies and provides analytical data to educators and policymakers via an API. This system allows users to make specific environmental improvement proposals based on the analysis results, thereby improving environmental awareness and behavior among individuals, companies, and even educators and policymakers.

[0007] "User" refers to an individual or company that uses the system to input data about their daily activities and business activities, check the analysis results, and make sustainable choices.

[0008] "Behavioral data" refers to information about users' daily lives and business activities, and is data necessary for assessing environmental impact (e.g., electricity consumption, waste output, means of transportation, etc.).

[0009] "Terminal" refers to a device (e.g., smartphone, tablet, or PC) through which a user inputs behavioral data and sends it to a server.

[0010] "Server" refers to a computer system that receives and analyzes behavioral data sent by users to assess environmental impacts and generate and provide sustainable options.

[0011] "Analysis means" refers to the function that includes AI engines and software that run on the server and analyzes behavioral data to assess environmental impact.

[0012] "Environmental impact" refers to the impact that a user's actions or activities have on the natural environment (e.g., CO2 emissions, water consumption, waste volume).

[0013] "Sustainable options" refers to specific actions or measures that users can take to reduce environmental impacts based on the analysis results (e.g., energy-saving measures, recycling recommendations).

[0014] "Error checking" refers to the process by which the server verifies the format and content of the behavioral data it receives to ensure there are no errors.

[0015] "Visualization means" refers to the function of presenting analysis results in a visually easy-to-understand form (e.g., graphs, charts).

[0016] A "report" is a written or digital document summarizing the results of an analysis, specifically intended for a company, detailing the environmental impact and proposing remedial measures.

[0017] "API" refers to an application programming interface that allows a server to provide analytical data to external parties, and is designed for use by educators and policymakers. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention relates to a system that inputs data on users' daily activities and corporate activities, analyzes the data on a server, evaluates the environmental impact, and generates and provides sustainable options. This system allows individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[0040] Program Overview

[0041] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results.

[0042] Data entry and submission

[0043] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is then converted into a standard data format (e.g., JSON) by the device.

[0044] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[0045] Data analysis

[0046] The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[0047] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity usage. The analysis results are sent back to the server, which then moves on to the next step.

[0048] Generate results and suggestions

[0049] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, it suggests switching to LED lighting and choosing more energy-efficient home appliances for households with high electricity consumption. For businesses, it suggests specific improvement measures to increase the energy efficiency of their manufacturing processes.

[0050] Data visualization and presentation

[0051] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[0052] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[0053] User practices

[0054] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[0055] Specific examples

[0056] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[0057] Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis results in "100 kg CO2 / month." Based on this, the server generates specific recommendations, such as "using LED lighting" and "introducing energy-efficient home appliances." These recommendations are displayed on the user's dashboard with graphs and charts.

[0058] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[0059] As described above, this system contributes to solving environmental problems by helping users and businesses make sustainable choices.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user might enter "200 kWh per month" in the "Electricity Usage" field.

[0063] Step 2:

[0064] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[0065] json

[0066] {

[0067] "userId": "12345",

[0068] "data": {

[0069] "electricity": "200kWh"

[0070] }

[0071] }

[0072] Step 3:

[0073] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[0074] Step 4:

[0075] The server receives the HTTP request and parses the JSON data sent. First, it checks whether the data format is correct or not.

[0076] Step 5:

[0077] The server transfers data that passes the error check to the AI ​​engine, which then inputs the data into the AI ​​model running on the server and begins analysis.

[0078] Step 6:

[0079] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[0080] Step 7:

[0081] The AI ​​engine sends the analysis results back to the server, which receives them and proceeds to the next step.

[0082] Step 8:

[0083] Based on the analysis results received by the server, it generates specific environmental improvement proposals for the user, such as a list of proposals such as "using LED lighting" or "introducing energy-efficient home appliances."

[0084] Step 9:

[0085] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[0086] Step 10:

[0087] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[0088] Step 11:

[0089] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[0090] Step 12:

[0091] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[0092] Through these steps, the ImpactScope system assesses environmental impacts, generates sustainable options, and supports users and companies in making concrete improvements to their behavior.

[0093] Example 1

[0094] 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."

[0095] In modern society, it is important for individuals and businesses to choose appropriate actions to reduce their environmental impact. However, most people do not understand the extent to which their daily actions and corporate activities affect the environment. There is also a lack of information and advice on how to find environmentally friendly options. This makes it difficult to achieve a sustainable society.

[0096] 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.

[0097] In this invention, the server includes means for a user to input behavioral data, means for a terminal to transmit the behavioral data to the server, means for the server to perform error checking on the behavioral data, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, and means for the server to generate a diagram for visualizing the evaluation results and sustainable options and provide it to the user. This enables individuals and companies to understand their own environmental impacts and reduce environmental loads through specific sustainable options.

[0098] "User" refers to an individual or company that uses the system to input data about their daily activities or business activities.

[0099] "Behavioral data" refers to information about a user's daily behavior and business activities, including electricity consumption, waste output, and commuting methods.

[0100] A "terminal" is a device used by a user to input behavioral data and transmit the data to a server, and includes a smartphone or computer.

[0101] "Server" refers to the central computer system that receives, analyzes, and generates sustainable options based on behavioral data sent by users.

[0102] "Error checking" refers to the process by which the server examines the format and content of the behavioral data it receives to detect errors.

[0103] "Analysis" refers to the process by which the server uses an AI engine to evaluate behavioral data and generate indicators related to environmental impact.

[0104] "Environmental impact" refers to the impact that a user's actions have on the environment, and specifically includes CO2 emissions, water consumption, waste volume, etc.

[0105] "Sustainable options" refers to specific actions and options for reducing environmental impact that are generated by the server based on the analysis results.

[0106] "Graphs" refers to graphs and charts that visually display analysis results and sustainable options.

[0107] A "report" refers to a document that compiles detailed analysis results and recommendations generated for a company.

[0108] A "generative AI model" is a type of artificial intelligence technology used for analysis, and refers to an algorithm that evaluates environmental impacts based on behavioral data.

[0109] The present invention is a system in which a user inputs behavioral data, which is analyzed by a server to evaluate the environmental impact, and which provides sustainable options. Specific embodiments of the system are described below.

[0110] System Overview

[0111] This system mainly consists of a terminal where users input behavioral data, a server that receives and analyzes the input data, and a means for presenting sustainable options based on the analysis results.

[0112] Data Entry

[0113] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste output, and commuting method (private car, public transport). This data is then converted into a standard data format (e.g., JSON) by the device.

[0114] Data transmission

[0115] The terminal sends the generated data to the server as an HTTP request. At this time, the data is sent using a secure protocol (e.g., HTTPS) to maintain the integrity and security of the data.

[0116] Data reception and error checking

[0117] The server receives the data sent from the device and checks its format and content for errors. Specifically, it checks whether the data is in JSON format and whether all required fields are included. If the data is invalid, it returns an error message to the user.

[0118] Data analysis

[0119] Once the check is complete, the data is transferred to an AI engine on the server. The AI ​​engine evaluates the environmental impact based on the behavioral data, calculating CO2 emissions from electricity consumption, for example. The analysis results are then sent back to the server.

[0120] Generating sustainable options

[0121] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, if electricity usage is high, it will suggest the introduction of LED lighting and energy-efficient home appliances. These suggestions are presented as specific action items.

[0122] Data visualization and presentation

[0123] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to understand their environmental impact at a glance. The generated graphs and charts are displayed on a dashboard in a web portal or smartphone app. It also generates detailed environmental impact reports for businesses and provides data to educators and policymakers via an API.

[0124] User practices

[0125] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[0126] Specific examples

[0127] As a concrete example, let's assume that a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks the format and content for errors. Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis result is calculated as "100 kg CO2 / month." Based on this, the server makes suggestions such as "using LED lighting" and "introducing energy-saving home appliances." These suggestions are displayed on the user's dashboard with graphs and charts. The user checks them and actually purchases and installs LED lighting in their home.

[0128] Prompt Sentence Examples

[0129] "Enter your monthly household electricity usage of 200 kWh to view your environmental impact analysis and sustainable options."

[0130] keyword

[0131] Generative AI model, prompt sentence

[0132] As described above, this system allows individuals and businesses to make environmentally friendly choices and contribute to the realization of a sustainable society.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1: Data entry

[0135] Users: Use a smartphone app or web portal to enter data about their daily activities and business activities.

[0136] Specific operation: The user opens the app, enters the amount of electricity usage as "200kWh per month," and clicks the submit button.

[0137] Input: Data related to users' daily activities and business activities (e.g., electricity usage)

[0138] Output: Behavioral data in standard data format (JSON format)

[0139] Step 2: Data conversion

[0140] Terminal: Receives data entered by the user and converts it into a standard data format (e.g., JSON format).

[0141] Specific operation: The terminal receives the input data "Power usage: 200kWh" and converts it into JSON format "{"Power usage": "200kWh"}".

[0142] Input: Raw data entered by the user

[0143] Output: Behavioral data in JSON format

[0144] Step 3: Send data

[0145] Terminal: Sends the generated data to the server as an HTTP request.

[0146] Specific operation: The device uses the HTTPS protocol to send the data "{"power usage": "200kWh"}" to the server.

[0147] Input: JSON formatted behavior data

[0148] Output: HTTP request to the server

[0149] Step 4: Receiving Data

[0150] Server: Receives data sent from the terminal.

[0151] Specific operation: The server listens for HTTP requests on the receiving port and receives the data that arrives.

[0152] Input: HTTP request sent from the terminal

[0153] Output: Received behavior data in JSON format

[0154] Step 5: Error checking

[0155] Server: Checks the format and content of the received data for errors.

[0156] What happens: The server uses a JSON schema to check that "{"Power Usage": "200kWh"}" is in the correct format and contains all required fields.

[0157] Input: JSON formatted behavior data

[0158] Output: Normal data or error message

[0159] Step 6: Data analysis

[0160] Server: Transfers the checked data to the AI ​​engine and evaluates the environmental impact.

[0161] Specific operation: The server calls the AI ​​engine's analysis API and inputs behavioral data. The AI ​​engine analyzes the behavioral data and calculates CO2 emissions.

[0162] Input: Normal data

[0163] Output: Analysis results of environmental impact (e.g., "100 kg CO2 / month")

[0164] Step 7: Generate sustainable options

[0165] Server: Based on the analysis results, it generates optimal sustainable options for users and companies.

[0166] Specific actions: The server evaluates the analysis results and generates specific action items such as "use LED lighting" or "introduce energy-saving home appliances."

[0167] Input: Analysis results

[0168] Output: Sustainable Choice

[0169] Step 8: Visualize the data

[0170] Server: Generates graphs and charts to visually display analysis results and sustainable options.

[0171] Specific operation: The server creates graphs and charts based on the analysis results and inserts them into the user's dashboard in HTML format.

[0172] Input: Analysis results and sustainable options

[0173] Output: Visualized data (graphs and charts)

[0174] Step 9: Provide data

[0175] Server: Generates environmental impact reports for companies and provides data via APIs as needed.

[0176] Specific operation: The server compiles the analysis data and proposals into a PDF report and provides a download link to companies. It also prepares an API to provide the analysis data to educators and policymakers.

[0177] Input: Analytical data and sustainable options

[0178] Output: Environmental Impact Report and API endpoints

[0179] Step 10: User Practice

[0180] Users: Review the analysis results and sustainable options provided to improve their own lives and business activities.

[0181] Specific actions: A user views the dashboard, purchases and installs LED lighting in their home, and a business implements suggested energy efficiency improvements.

[0182] Input: Analysis results and sustainable options

[0183] Output: Reduction of environmental impact (results of concrete actions)

[0184] Through these steps, the system provides concrete support for users and businesses to make sustainable choices and reduce their environmental impact.

[0185] (Application example 1)

[0186] 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."

[0187] To achieve a sustainable society, individuals and businesses are required to make environmentally friendly choices. However, currently, there is a lack of systems to assess environmental impacts and provide sustainable options. Another problem is that there is no established method to collect real-time data on energy consumption and resource use in industrial environments and provide immediate improvement recommendations. This makes it difficult to achieve efficient energy use and reduce waste.

[0188] 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.

[0189] In this invention, the server includes a means for workers to input data on energy consumption and resource utilization in industrial environments in real time through smart glasses, a means for the smart glasses to visually display assessment results and sustainable proposals in real time, and a means for the server to perform analysis using a generative AI model based on the environmental data provided through the smart glasses, thereby enabling workers to understand environmental impacts in real time and immediately adopt sustainable options.

[0190] "User" refers to an individual or company that uses the system.

[0191] "Behavioral data" refers to information about users' daily behavior and business activities, and specifically includes energy consumption and resource usage.

[0192] "Terminal" refers to an electronic device that allows a user to input behavioral data and transmit it to a server.

[0193] "Server" refers to a central computer system that receives and analyzes behavioral data and generates and provides sustainable options based on the evaluation results.

[0194] "Smart glasses" refer to wearable devices worn by workers that allow them to input and visualize data in real time.

[0195] "Energy consumption" refers to the consumption of electricity and fuel used in industrial environments.

[0196] "Resource use" refers to the amount of raw materials and resources used in an industrial environment.

[0197] "Real-time" refers to data being entered, analyzed, and displayed immediately, without delay.

[0198] "Evaluation results" refer to the indicators and numerical values ​​of environmental impact obtained by the server through analysis of behavioral data.

[0199] "Sustainable proposals" refer to specific actions and improvement plans to reduce environmental impact based on the analysis results.

[0200] "Means for visual display" refers to a display function that allows analysis results and proposals to be presented to workers in an easy-to-understand manner.

[0201] "Generative AI models" refer to artificial intelligence algorithms and models used to analyze behavioral data and assess environmental impacts.

[0202] This invention is a system that uses smart glasses to collect data on energy consumption and resource usage in industrial environments in real time, analyzes the data, and evaluates the environmental impact and provides sustainable solutions. This system enables efficient energy use and waste reduction, improving sustainability in industrial environments.

[0203] Specifically, the system consists of the following components:

[0204] 1. Data input method: Workers wear smart glasses and input data on energy consumption and resource utilization in industrial environments in real time.

[0205] 2. Data transmission method: The smart glasses convert the collected data into a standard data format and send it to the server via an HTTP request.

[0206] 3. Data analysis method: The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[0207] 4. Method for generating evaluation results: The AI ​​engine evaluates the environmental impact based on behavioral data, specifically calculating indicators such as CO2 emissions and waste volume from energy consumption.

[0208] 5. Proposal generation method: The server generates sustainable options based on the analysis results. For example, for a factory with high energy consumption, it proposes switching to LED lighting and other measures to improve energy efficiency.

[0209] 6. Result display means: The server displays the analysis results and sustainable options on the smart glasses display in real time, allowing workers to immediately check the evaluation results and specific suggestions.

[0210] Hardware and Software

[0211] This system uses the following hardware and software:

[0212] Hardware: Smart glasses (e.g., SmartGlasses), servers

[0213] Software: AI engine for data analysis, HTTP communication protocol, data visualization tool

[0214] Specific examples

[0215] For example, if a part of a factory uses 500 kWh of electricity per month and generates 20 kg of waste, workers can input this data in real time through the smart glasses. The AI ​​engine analyzes the data and displays the result: "CO2 emissions: 250 kg / month." Based on this result, suggestions such as "switching to LED lighting" and "using recyclable materials" are made. These suggestions are displayed in real time on the smart glasses' display, allowing workers to immediately implement improvement measures.

[0216] Prompt Sentence Examples

[0217] Use the data below to conduct an environmental impact assessment of your factory and propose sustainable options.

[0218] data:

[0219] Energy usage: 500kWh

[0220] Waste generated: 20kg

[0221] As described above, the present invention allows workers to understand the environmental impact within a factory and receive sustainable improvement proposals in real time, thereby contributing to the realization of a sustainable society.

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] Workers wear smart glasses that input real-time data on energy consumption and resource use within industrial environments.

[0225] Input: Energy consumption and resource utilization data within an industrial environment.

[0226] Output: Data is stored in the smart glasses.

[0227] Specific operation: Workers operate the interface of the smart glasses to input data such as power consumption and waste generation.

[0228] Step 2:

[0229] The device (smart glasses) converts the collected data into a standard data format and sends it to the server via an HTTP request.

[0230] Input: Data stored on smart glasses.

[0231] Output: The data sent to the server.

[0232] Specific operation: The smart glasses software converts the input data into JSON format, generates an HTTP POST request, and sends it to the server.

[0233] Step 3:

[0234] The server performs an error check on the received behavioral data to ensure that there are no errors in the data format or content.

[0235] Input: Behavioral data sent to the server.

[0236] Output: Data verified to be error-free.

[0237] Specific operation: A program on the server runs routines that validate data format, numeric range, etc., to check for inconsistencies.

[0238] Step 4:

[0239] The server transfers the data to an AI engine, which analyzes environmental impacts based on behavioral data.

[0240] Input: Behavioral data that has been verified to be error-free.

[0241] Output: An indicator of the environmental impact (e.g. CO2 emissions) as a result of the analysis.

[0242] Specific operation: The server inputs behavioral data into an AI engine and runs an algorithm to calculate environmental impact indicators such as CO2 emissions and waste generation.

[0243] Step 5:

[0244] The server generates sustainable options based on the analysis results.

[0245] Input: Analysis results by the AI ​​engine.

[0246] Output: Proposal of sustainable options.

[0247] Specific actions: Based on the analysis results, the server executes logic to generate specific suggestions for improving energy efficiency (e.g., switching to LED lighting, using recyclable materials).

[0248] Step 6:

[0249] The server displays the assessment results and sustainable options in real time on the smart glasses' display.

[0250] Input: Proposal of sustainable options.

[0251] Output: Evaluation results and suggestions displayed on the smart glasses display.

[0252] How it works: The server sends the evaluation results and suggestions to the smart glasses, which use a rendering engine to visually display them on the display. This information is updated in real time so that the worker can check it at any time.

[0253] Through these processing steps, workers can understand the environmental impact within the factory in real time and immediately incorporate sustainable options. As a specific example, in a factory that uses 500 kWh of electricity per month and generates 20 kg of waste, CO2 emissions are analyzed and suggestions such as switching to LED lighting are displayed in real time.

[0254] 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.

[0255] This invention relates to a system that inputs behavioral data related to users' daily activities and corporate activities, analyzes it on a server, evaluates the environmental impact, and generates and provides sustainable options. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide appropriate sustainable options accordingly. This system enables individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[0256] Program Overview

[0257] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results. In addition, an emotion engine analyzes the user's emotions and adjusts the suggestions accordingly.

[0258] Data entry and submission

[0259] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is converted into a standard data format (e.g., JSON format) by the device. In addition, the user's emotions can be collected, for example, through emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app.

[0260] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[0261] Data analysis

[0262] The server performs an error check on the received behavioral and emotional data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and emotion engine to begin analysis.

[0263] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from power consumption. The emotion engine analyzes the emotion data and evaluates the user's current emotional state. The analysis results are sent back to the server, which then moves on to the next step.

[0264] Generate results and suggestions

[0265] The server generates specific environmental improvement proposals for users based on the analysis results returned by the AI ​​engine and emotion engine. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. The presentation and content of the proposals are also adjusted based on the emotion data from the emotion engine. Specific improvement measures for increasing the energy efficiency of manufacturing processes are also presented to companies.

[0266] Data visualization and presentation

[0267] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[0268] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[0269] User practices

[0270] Users review the analysis results and sustainable options provided, and use them to improve their own lives and business activities. For example, at home, they might purchase LED lighting and choose energy-efficient home appliances. At work, they might improve energy efficiency and reduce waste. By taking into account the emotion analysis results from the emotion engine, users can receive suggestions that match their own psychological state, reducing resistance to putting ideas into practice and making it easier for them to take sustainable actions.

[0271] Specific examples

[0272] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[0273] At the same time, while the user is using the app, the emotion recognition function analyzes the user's face and voice to collect emotional data such as "stress level," which is also sent to the server.

[0274] Once the error check is complete, the server transfers the behavioral data to the AI ​​engine, which analyzes the CO2 emissions. The emotion engine also analyzes the emotional data and evaluates the user's emotional state. For example, the analysis result "100kg CO2 / month" corresponding to 200kWh of electricity usage and the emotion analysis result "stress state" are returned.

[0275] Based on these analysis results, the server generates environmental improvement suggestions such as "using LED lighting" or "introducing energy-saving home appliances," and presents the suggestions in a concise and easy-to-implement format, taking into account the user's stress level. These suggestions are then displayed on the user's dashboard with graphs and charts.

[0276] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[0277] As described above, this system contributes to solving environmental problems by allowing users and businesses to make sustainable choices, and by adjusting the content of suggestions using an emotion engine, it reduces the psychological burden on users and promotes sustainable behavior.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user enters "200 kWh per month" in the "Electricity Usage" field.

[0281] Step 2:

[0282] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[0283] json

[0284] {

[0285] "userId": "12345",

[0286] "data": {

[0287] "electricity": "200kWh"

[0288] }

[0289] }

[0290] Step 3:

[0291] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[0292] Step 4:

[0293] Collecting user emotional data. For example, an emotion recognition function embedded in a smartphone app uses a camera and microphone to analyze the user's facial expressions and voice to generate emotional data.

[0294] Step 5:

[0295] The device converts the emotion data into JSON format and sends it to the server. The emotion data will be in the following format:

[0296] json

[0297] {

[0298] "userId": "12345",

[0299] "emotion": "stressed"

[0300] }

[0301] Step 6:

[0302] The server receives the HTTP request and analyzes the behavioral and emotional data sent. First, it checks whether the data format is correct.

[0303] Step 7:

[0304] The server transfers the data that passes the error check to the AI ​​engine and emotion engine. Specifically, behavioral data is input into the AI ​​model, and emotion data is input into the emotion analysis model.

[0305] Step 8:

[0306] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[0307] Step 9:

[0308] The emotion engine analyzes the emotion data and evaluates the user's current emotional state, for example recognizing the state "stressed."

[0309] Step 10:

[0310] The AI ​​engine and emotion engine send their analysis results back to the server, which then generates specific suggestions for improving the user's environment based on these results.

[0311] Step 11:

[0312] The server generates sustainable options based on the evaluation results and adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it will present suggestions such as "using LED lighting" or "introducing energy-saving appliances" in a concise and easy-to-implement format.

[0313] Step 12:

[0314] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[0315] Step 13:

[0316] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[0317] Step 14:

[0318] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[0319] Step 15:

[0320] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[0321] Example 2

[0322] 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."

[0323] In modern society, the impact of everyday user and corporate activities on the environment has become a major issue. However, making sustainable choices requires specific assessments of environmental impacts and appropriate guidelines for action based on those assessments. Furthermore, users' emotional states can be important factors in taking sustainable actions on an ongoing basis, but current systems lack the means to comprehensively assess and adjust these. Therefore, there is a need to provide an environment that makes it easier for users to make sustainable choices based on appropriate information.

[0324] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing behavioral data and emotional data to evaluate environmental influences and the emotional state, means for generating sustainable options based on the evaluation results and adjusting the options according to the emotional state, and means for providing the adjusted options to the user. This makes it possible for the user to understand environmental influences, receive sustainable options that match their own emotional state, and easily implement them.

[0325] "Behavioral data" refers to information about users' daily activities and corporate activities, and refers to specific data such as electricity consumption, waste output, and transportation usage.

[0326] "Emotional data" refers to information that indicates the user's emotional state, such as "stress level" and "happiness" collected through facial recognition and voiceprint analysis.

[0327] A "terminal" is a device or software, such as a smartphone app or web portal, that allows a user to input data and send it to a server.

[0328] "Server" means a digital processing system for receiving and analyzing behavioral and emotional data, generating and providing sustainable options to users.

[0329] An "AI engine" is a computational system that includes algorithms and programs for analyzing behavioral data and assessing environmental impacts.

[0330] An "emotion engine" is a computational system that includes algorithms and programs for analyzing emotion data and assessing a user's emotional state.

[0331] "Sustainable options" are specific recommended actions and proposals for reducing environmental impact and promoting the realization of a sustainable society.

[0332] "Adjustment" refers to changing the content and presentation of sustainable options depending on the user's emotional state.

[0333] "Visualization" refers to converting analytical results into a visual format such as a graph or chart in order to display them in an easy-to-understand manner.

[0334] A "dashboard" is an interface that visually displays analysis results and sustainable options, allowing users to easily check them.

[0335] This system inputs behavioral and emotional data related to users' daily activities and business activities, analyzes the data on a server to evaluate the environmental impact, and uses an emotion engine to recognize the user's emotional state and provide appropriate sustainable options. This system aims to contribute to the realization of a sustainable society.

[0336] This system has a terminal where users input behavioral data and emotional data, a server that receives and analyzes this data, a means for presenting sustainable options based on the analysis results, and a means for the emotion engine to analyze the user's emotions and adjust the content of the suggestions.

[0337] Data entry and submission

[0338] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). Users also provide their own emotional data through facial recognition and voiceprint analysis via the app's emotion recognition feature. This collected data is stored on the device and converted to JSON format.

[0339] The terminal sends this converted data to the server as an HTTP request, which the server receives and proceeds to the next analysis step.

[0340] Data analysis

[0341] The server receives the JSON data sent from the device and first performs an error check. Behavioral data that passes the error check is transferred to the AI ​​engine, and emotional data is transferred to the emotion engine. The AI ​​engine calculates environmental indicators such as CO2 emissions, water consumption, and waste volume based on the behavioral data. The emotion engine analyzes the emotional data and evaluates the user's emotional state (e.g., "stress state").

[0342] Generate results and suggestions

[0343] The server integrates the analysis results returned by the AI ​​engine and emotion engine to generate specific suggestions for improving the environment. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. Furthermore, based on the results of the emotion engine, it makes simple and easy-to-implement suggestions to users who are under stress.

[0344] Data visualization and presentation

[0345] The server converts the analysis results into graphs and charts for easy visual understanding. Users can view these visualized data on a smartphone app or web portal dashboard. The system also generates detailed environmental impact reports for businesses and provides analysis data via an API for educators and policymakers.

[0346] User practices

[0347] Users can check the analysis results and environmental improvement suggestions provided on the dashboard and take action based on them. For example, at home, they can purchase and install LED lighting or choose energy-efficient home appliances. At business, they can improve energy efficiency or implement waste reduction measures. In this way, users can reduce their environmental impact through concrete actions.

[0348] Specific examples

[0349] For example, suppose a user uses 200 kWh of electricity per month. The user enters this data into a smartphone app and presses the send button. The device converts the data into JSON format and uploads it to the server. At the same time, the emotion recognition function analyzes the user's face and voice, collects emotional data on "stress state," and sends it to the server.

[0350] After receiving this data, the server performs an error check. After the error check, the behavioral data is transferred to the AI ​​engine, and the emotional data is transferred to the emotion engine. The AI ​​engine returns the result that "200 kWh of electricity usage per month will result in 100 kg of CO2 emissions," and the emotion engine returns the analysis result that "the user's emotional state is stressed."

[0351] Based on these analysis results, the server generates specific environmental improvement suggestions, such as "using LED lighting" or "introducing energy-saving home appliances," and adjusts the suggestions to be simple and easy to implement, taking into account the user's stress level. These suggestions are displayed on the user's dashboard with graphs and charts.

[0352] An example of a prompt is, "If a user uses 200 kWh of electricity per month and is in a stressed state, please explain in detail how the program will display the analysis results and suggestions on a dashboard along with graphs." In this way, users can take specific actions based on the analysis results and suggestions to reduce their environmental impact.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1: Data entry

[0355] Users use a smartphone app or web portal to input data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste generation, and commuting method (private car, public transportation). This data is entered into the device. The input data is collected based on the user's behavior.

[0356] Step 2: Collecting emotion data

[0357] Users use the smartphone app's emotion recognition function to provide emotional data through facial recognition and voiceprint analysis. Specifically, the app uses the camera and microphone to analyze the user's facial expressions and voice, detecting emotions such as "stress" and "happiness." This emotional data is also input into the device.

[0358] Step 3: Transform the data

[0359] The device converts the input behavioral and emotional data into JSON format. For example, it converts data on 200 kWh of electricity usage and stress level into JSON format and creates a single data package. This conversion allows the data to be sent to the server in a standard format.

[0360] Step 4: Sending data

[0361] The device generates an HTTP request with the data converted into JSON format and sends it to the server. The data sent includes behavioral data and emotion data. Once the data is sent from the device to the server, the next analysis step begins.

[0362] Step 5: Error checking

[0363] The server parses the received JSON data and performs an error check. This checks whether there are any errors in the data format or content. For example, it verifies whether the amount of electricity used is a number, and whether the emotion data is in the correct format. Once the error check is complete, the data can proceed to analysis.

[0364] Step 6: Analyze behavioral data

[0365] The server transfers the behavioral data that has passed the error check to the AI ​​engine. The AI ​​engine evaluates the environmental impact based on the behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity consumption. For example, the evaluation result may be, "100 kg of CO2 will be emitted from 200 kWh of electricity consumption per month."

[0366] Step 7: Analyze the sentiment data

[0367] The server transfers the emotion data to the emotion engine. The emotion engine analyzes the emotion data and evaluates the user's emotional state. For example, the result may be "The user's emotional state is stressed." Based on this analysis, the suggestions are adjusted according to the user's emotional state.

[0368] Step 8: Generate results and recommendations

[0369] The server combines the analysis results of the AI ​​engine and the emotion engine to generate specific suggestions for improving the environment. For example, they might suggest switching to LED lighting to reduce power consumption or recommend installing energy-efficient home appliances. Based on the results of the emotion engine, the server also provides simple, easy-to-implement suggestions to users who are under stress.

[0370] Step 9: Visualize the data

[0371] The server generates graphs and charts to make the analysis results visually easy to understand. For example, CO2 emissions are displayed in a pie chart, and specific values ​​are displayed in a bar graph. This visualization allows users to intuitively understand their own environmental impact.

[0372] Step 10: Provide data

[0373] The server then sends the generated graphs and charts to the device, where they are displayed on a dashboard in a web portal or smartphone app. Users can view the analysis results and sustainable choices on the dashboard. The system also provides detailed environmental impact reports for businesses and analytical data via an API for educators and policymakers.

[0374] Step 11: User Practice

[0375] Users can review the analysis results and sustainable choices provided by the dashboard and use them to improve their own lives and business activities. For example, households can purchase LED lighting and choose energy-efficient home appliances, while businesses can implement energy efficiency improvements and waste reduction measures. This allows users to reduce their environmental impact through concrete actions.

[0376] Through these specific processing steps, users and businesses can make environmentally sustainable choices and contribute to the realization of a sustainable society.

[0377] (Application example 2)

[0378] 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."

[0379] This invention relates to a system that evaluates the environmental impact of users' daily activities and corporate activities and provides sustainable options. However, conventional systems do not consider the user's emotional state when proposing sustainable options, making it difficult to translate these options into concrete action. Furthermore, there has been a lack of proposals for simultaneously reducing environmental impact and improving customer satisfaction in certain industries, particularly brick-and-mortar stores.

[0380] 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 the user to input behavioral data, means for the terminal to transmit the behavioral data to the server, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, means for the server to provide the options to the user, means for an emotion recognition engine to analyze the user's emotion data, and means for the server to suggest options adapted to the emotions based on the analysis results. This makes it possible to provide specific, emotion-sensitive sustainable options for users' daily behavior and corporate activities, and in particular, in physical stores, it becomes possible to monitor energy consumption, waste volume, and customer satisfaction and generate management proposals based on the results.

[0381] "User" refers to an individual or company that uses the system.

[0382] "Behavioral data" refers to information such as electricity consumption, waste volume, and transportation methods related to users' daily behavior and corporate activities.

[0383] "Terminal" refers to a device that allows a user to input behavioral data and transmit it to a server. Examples include smartphones and computers.

[0384] "Server" refers to a computer system that receives behavioral data sent from a terminal, analyzes it, and provides the results to the user.

[0385] "Analysis" refers to the process by which the server processes the received behavioral data and evaluates the environmental impact.

[0386] "Environmental impact" refers to the impact that a user's actions have on the environment, and is evaluated using indicators such as CO2 emissions and waste volume.

[0387] "Sustainable options" refers to specific suggestions that encourage users to adopt environmentally friendly actions and choices, such as using energy-efficient appliances or taking public transport.

[0388] An "emotion recognition engine" refers to software technology that analyzes a user's emotional state and uses the results to make appropriate suggestions.

[0389] "Emotional data" refers to data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, etc.

[0390] "Monitoring" refers to the process of regularly observing and recording specific indicators (e.g., energy use, waste volume, customer satisfaction).

[0391] "Operation proposals" refer to specific improvement measures and guidelines for action that the server provides to physical store operators based on the analysis results.

[0392] "Customer satisfaction" refers to an indicator of the level of satisfaction that customers who visit a physical store feel toward the service.

[0393] "Visualization" refers to the process of visually displaying analysis results in graphs, charts, etc., and presenting them to users in an easy-to-understand manner.

[0394] The present invention relates to a system that inputs behavioral data and emotional data related to users' daily activities and business activities, analyzes the data on a server, evaluates the environmental impact, and provides sustainable options. Specifically, the system includes a terminal, a server, an emotion recognition engine, and a data visualization means.

[0395] Data entry and submission

[0396] Users input behavioral data using a device (e.g., a smartphone). This behavioral data includes energy consumption, waste volume, transportation methods, etc. This data is converted into a standard data format (e.g., JSON format). In addition, emotion data of the user is collected using emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app. This data is sent to the server via an HTTP request.

[0397] Data reception and analysis

[0398] The server receives the data sent from the device and first checks for errors. After checking, the behavioral data is transferred to an AI engine to evaluate the environmental impact (e.g., CO2 emissions). The emotion data is analyzed by an emotion recognition engine to evaluate the user's emotional state. This provides information such as whether the user is stressed or relaxed.

[0399] Generate results and suggestions

[0400] The server generates sustainable options based on the analysis results obtained from the AI ​​engine and emotion recognition engine. For example, if energy usage is high, it will suggest installing energy-efficient appliances or switching to LED lighting. It also adjusts the content and presentation of the suggestions based on emotion data. For users in a stressed state, it provides simple, easy-to-implement suggestions.

[0401] Data visualization and presentation

[0402] The server visually displays the analysis results in graphs and charts, allowing users to intuitively understand their own environmental impact. For example, it may provide information such as the CO2 emissions corresponding to 200 kWh of energy consumption (e.g., 100 kg CO2 / month). This data is displayed on the device's dashboard. This display is generated using the matplotlib library.

[0403] User practices

[0404] Users can review the sustainable options presented and take specific actions. Store managers can use the information provided by the app to implement specific measures to reduce energy consumption and waste. For example, introducing LED lighting can reduce environmental impact.

[0405] Prompt Sentence Examples

[0406] "Enter your store's monthly energy use and waste volume, as well as customer reviews. The results assess your environmental impact and provide sustainable options."

[0407] As described above, the system of the present invention can promote environmentally conscious behavior by integrating and analyzing behavioral data and emotional data, and providing optimal sustainable options to users.

[0408] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0409] Step 1:

[0410] The user inputs behavioral data using a device. The user inputs data related to daily activities and store operations, such as energy consumption, waste volume, and transportation methods, into the application. At this time, the emotion recognition function analyzes the user's facial expressions and voice to collect emotional data. The input data and emotional data are converted into JSON format.

[0411] Step 2:

[0412] The device sends input data and emotion data to the server. The device generates an HTTP request and uploads the input behavioral data and emotion data to the server. The server receives the data and performs error checks to ensure it is in the correct format. Here, the input is behavioral data and emotion data, and the output is error-checked data.

[0413] Step 3:

[0414] The server analyzes the behavioral data. After error checking, the server transfers the behavioral data to an AI engine, which calculates environmental impacts such as CO2 emissions, energy consumption, and waste volume. The input is the error-checked behavioral data, and the output is the analyzed environmental impact data.

[0415] Step 4:

[0416] The server analyzes the emotion data. The server sends the error-checked emotion data to an emotion recognition engine to evaluate the user's current emotional state, where the input is the error-checked emotion data and the output is the user's emotional state data.

[0417] Step 5:

[0418] The server generates sustainable options based on the analysis results. The server combines the analysis results obtained from the AI ​​engine and emotion recognition engine to generate specific environmental improvement measures. For example, for users with high energy usage, it suggests introducing energy-saving home appliances and switching to LED lighting. The input is environmental impact data and emotional state data, and the output is sustainable options.

[0419] Step 6:

[0420] The server visualizes the proposals. The server displays the sustainable options generated in the previous step in a visual format such as graphs and charts. This makes it easier for users to intuitively understand their own environmental impacts. The input is the sustainable options, and the output is the visualized data.

[0421] Step 7:

[0422] The server provides the user with the proposed solutions. The server sends the visualized analysis results and sustainable options to the user's device. The user can then review the results in the application and take specific actions. The input is the visualized data, and the output is a notification to the user.

[0423] Through these processing steps, users can obtain specific, emotionally sensitive, and sustainable choices based on their own behavioral data. This system promotes environmentally friendly behavior and supports users' sustainable behavior.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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."

[0440] This invention relates to a system that inputs data on users' daily activities and corporate activities, analyzes the data on a server, evaluates the environmental impact, and generates and provides sustainable options. This system allows individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[0441] Program Overview

[0442] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results.

[0443] Data entry and submission

[0444] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is then converted into a standard data format (e.g., JSON) by the device.

[0445] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[0446] Data analysis

[0447] The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[0448] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity usage. The analysis results are sent back to the server, which then moves on to the next step.

[0449] Generate results and suggestions

[0450] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, it suggests switching to LED lighting and choosing more energy-efficient home appliances for households with high electricity consumption. For businesses, it suggests specific improvement measures to increase the energy efficiency of their manufacturing processes.

[0451] Data visualization and presentation

[0452] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[0453] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[0454] User practices

[0455] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[0456] Specific examples

[0457] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[0458] Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis results in "100 kg CO2 / month." Based on this, the server generates specific recommendations, such as "using LED lighting" and "introducing energy-efficient home appliances." These recommendations are displayed on the user's dashboard with graphs and charts.

[0459] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[0460] As described above, this system contributes to solving environmental problems by helping users and businesses make sustainable choices.

[0461] The processing flow will be explained below.

[0462] Step 1:

[0463] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user might enter "200 kWh per month" in the "Electricity Usage" field.

[0464] Step 2:

[0465] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[0466] json

[0467] {

[0468] "userId": "12345",

[0469] "data": {

[0470] "electricity": "200kWh"

[0471] }

[0472] }

[0473] Step 3:

[0474] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[0475] Step 4:

[0476] The server receives the HTTP request and parses the JSON data sent. First, it checks whether the data format is correct or not.

[0477] Step 5:

[0478] The server transfers data that passes the error check to the AI ​​engine, which then inputs the data into the AI ​​model running on the server and begins analysis.

[0479] Step 6:

[0480] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[0481] Step 7:

[0482] The AI ​​engine sends the analysis results back to the server, which receives them and proceeds to the next step.

[0483] Step 8:

[0484] Based on the analysis results received by the server, it generates specific environmental improvement proposals for the user, such as a list of proposals such as "using LED lighting" or "introducing energy-efficient home appliances."

[0485] Step 9:

[0486] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[0487] Step 10:

[0488] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[0489] Step 11:

[0490] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[0491] Step 12:

[0492] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[0493] Through these steps, the ImpactScope system assesses environmental impacts, generates sustainable options, and supports users and companies in making concrete improvements to their behavior.

[0494] Example 1

[0495] 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."

[0496] In modern society, it is important for individuals and businesses to choose appropriate actions to reduce their environmental impact. However, most people do not understand the extent to which their daily actions and corporate activities affect the environment. There is also a lack of information and advice on how to find environmentally friendly options. This makes it difficult to achieve a sustainable society.

[0497] 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.

[0498] In this invention, the server includes means for a user to input behavioral data, means for a terminal to transmit the behavioral data to the server, means for the server to perform error checking on the behavioral data, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, and means for the server to generate a diagram for visualizing the evaluation results and sustainable options and provide it to the user. This enables individuals and companies to understand their own environmental impacts and reduce environmental loads through specific sustainable options.

[0499] "User" refers to an individual or company that uses the system to input data about their daily activities or business activities.

[0500] "Behavioral data" refers to information about a user's daily behavior and business activities, including electricity consumption, waste output, and commuting methods.

[0501] A "terminal" is a device used by a user to input behavioral data and transmit the data to a server, and includes a smartphone or computer.

[0502] "Server" refers to the central computer system that receives, analyzes, and generates sustainable options based on behavioral data sent by users.

[0503] "Error checking" refers to the process by which the server examines the format and content of the behavioral data it receives to detect errors.

[0504] "Analysis" refers to the process by which the server uses an AI engine to evaluate behavioral data and generate indicators related to environmental impact.

[0505] "Environmental impact" refers to the impact that a user's actions have on the environment, and specifically includes CO2 emissions, water consumption, waste volume, etc.

[0506] "Sustainable options" refers to specific actions and options for reducing environmental impact that are generated by the server based on the analysis results.

[0507] "Graphs" refers to graphs and charts that visually display analysis results and sustainable options.

[0508] A "report" refers to a document that compiles detailed analysis results and recommendations generated for a company.

[0509] A "generative AI model" is a type of artificial intelligence technology used for analysis, and refers to an algorithm that evaluates environmental impacts based on behavioral data.

[0510] The present invention is a system in which a user inputs behavioral data, which is analyzed by a server to evaluate the environmental impact, and which provides sustainable options. Specific embodiments of the system are described below.

[0511] System Overview

[0512] This system mainly consists of a terminal where users input behavioral data, a server that receives and analyzes the input data, and a means for presenting sustainable options based on the analysis results.

[0513] Data Entry

[0514] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste output, and commuting method (private car, public transport). This data is then converted into a standard data format (e.g., JSON) by the device.

[0515] Data transmission

[0516] The terminal sends the generated data to the server as an HTTP request. At this time, the data is sent using a secure protocol (e.g., HTTPS) to maintain the integrity and security of the data.

[0517] Data reception and error checking

[0518] The server receives the data sent from the device and checks its format and content for errors. Specifically, it checks whether the data is in JSON format and whether all required fields are included. If the data is invalid, it returns an error message to the user.

[0519] Data analysis

[0520] Once the check is complete, the data is transferred to an AI engine on the server. The AI ​​engine evaluates the environmental impact based on the behavioral data, calculating CO2 emissions from electricity consumption, for example. The analysis results are then sent back to the server.

[0521] Generating sustainable options

[0522] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, if electricity usage is high, it will suggest the introduction of LED lighting and energy-efficient home appliances. These suggestions are presented as specific action items.

[0523] Data visualization and presentation

[0524] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to understand their environmental impact at a glance. The generated graphs and charts are displayed on a dashboard in a web portal or smartphone app. It also generates detailed environmental impact reports for businesses and provides data to educators and policymakers via an API.

[0525] User practices

[0526] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[0527] Specific examples

[0528] As a concrete example, let's assume that a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks the format and content for errors. Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis result is calculated as "100 kg CO2 / month." Based on this, the server makes suggestions such as "using LED lighting" and "introducing energy-saving home appliances." These suggestions are displayed on the user's dashboard with graphs and charts. The user checks them and actually purchases and installs LED lighting in their home.

[0529] Prompt Sentence Examples

[0530] "Enter your monthly household electricity usage of 200 kWh to view your environmental impact analysis and sustainable options."

[0531] keyword

[0532] Generative AI model, prompt sentence

[0533] As described above, this system allows individuals and businesses to make environmentally friendly choices and contribute to the realization of a sustainable society.

[0534] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0535] Step 1: Data entry

[0536] Users: Use a smartphone app or web portal to enter data about their daily activities and business activities.

[0537] Specific operation: The user opens the app, enters the amount of electricity usage as "200kWh per month," and clicks the submit button.

[0538] Input: Data related to users' daily activities and business activities (e.g., electricity usage)

[0539] Output: Behavioral data in standard data format (JSON format)

[0540] Step 2: Data conversion

[0541] Terminal: Receives data entered by the user and converts it into a standard data format (e.g., JSON format).

[0542] Specific operation: The terminal receives the input data "Power usage: 200kWh" and converts it into JSON format "{"Power usage": "200kWh"}".

[0543] Input: Raw data entered by the user

[0544] Output: Behavioral data in JSON format

[0545] Step 3: Send data

[0546] Terminal: Sends the generated data to the server as an HTTP request.

[0547] Specific operation: The device uses the HTTPS protocol to send the data "{"power usage": "200kWh"}" to the server.

[0548] Input: JSON formatted behavior data

[0549] Output: HTTP request to the server

[0550] Step 4: Receiving Data

[0551] Server: Receives data sent from the terminal.

[0552] Specific operation: The server listens for HTTP requests on the receiving port and receives the data that arrives.

[0553] Input: HTTP request sent from the terminal

[0554] Output: Received behavior data in JSON format

[0555] Step 5: Error checking

[0556] Server: Checks the format and content of the received data for errors.

[0557] What happens: The server uses a JSON schema to check that "{"Power Usage": "200kWh"}" is in the correct format and contains all required fields.

[0558] Input: JSON formatted behavior data

[0559] Output: Normal data or error message

[0560] Step 6: Data analysis

[0561] Server: Transfers the checked data to the AI ​​engine and evaluates the environmental impact.

[0562] Specific operation: The server calls the AI ​​engine's analysis API and inputs behavioral data. The AI ​​engine analyzes the behavioral data and calculates CO2 emissions.

[0563] Input: Normal data

[0564] Output: Analysis results of environmental impact (e.g., "100 kg CO2 / month")

[0565] Step 7: Generate sustainable options

[0566] Server: Based on the analysis results, it generates optimal sustainable options for users and companies.

[0567] Specific actions: The server evaluates the analysis results and generates specific action items such as "use LED lighting" or "introduce energy-saving home appliances."

[0568] Input: Analysis results

[0569] Output: Sustainable Choice

[0570] Step 8: Visualize the data

[0571] Server: Generates graphs and charts to visually display analysis results and sustainable options.

[0572] Specific operation: The server creates graphs and charts based on the analysis results and inserts them into the user's dashboard in HTML format.

[0573] Input: Analysis results and sustainable options

[0574] Output: Visualized data (graphs and charts)

[0575] Step 9: Provide data

[0576] Server: Generates environmental impact reports for companies and provides data via APIs as needed.

[0577] Specific operation: The server compiles the analysis data and proposals into a PDF report and provides a download link to companies. It also prepares an API to provide the analysis data to educators and policymakers.

[0578] Input: Analytical data and sustainable options

[0579] Output: Environmental Impact Report and API endpoints

[0580] Step 10: User Practice

[0581] Users: Review the analysis results and sustainable options provided to improve their own lives and business activities.

[0582] Specific actions: A user views the dashboard, purchases and installs LED lighting in their home, and a business implements suggested energy efficiency improvements.

[0583] Input: Analysis results and sustainable options

[0584] Output: Reduction of environmental impact (results of concrete actions)

[0585] Through these steps, the system provides concrete support for users and businesses to make sustainable choices and reduce their environmental impact.

[0586] (Application example 1)

[0587] 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."

[0588] To achieve a sustainable society, individuals and businesses are required to make environmentally friendly choices. However, currently, there is a lack of systems to assess environmental impacts and provide sustainable options. Another problem is that there is no established method to collect real-time data on energy consumption and resource use in industrial environments and provide immediate improvement recommendations. This makes it difficult to achieve efficient energy use and reduce waste.

[0589] 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.

[0590] In this invention, the server includes a means for workers to input data on energy consumption and resource utilization in industrial environments in real time through smart glasses, a means for the smart glasses to visually display assessment results and sustainable proposals in real time, and a means for the server to perform analysis using a generative AI model based on the environmental data provided through the smart glasses, thereby enabling workers to understand environmental impacts in real time and immediately adopt sustainable options.

[0591] "User" refers to an individual or company that uses the system.

[0592] "Behavioral data" refers to information about users' daily behavior and business activities, and specifically includes energy consumption and resource usage.

[0593] "Terminal" refers to an electronic device that allows a user to input behavioral data and transmit it to a server.

[0594] "Server" refers to a central computer system that receives and analyzes behavioral data and generates and provides sustainable options based on the evaluation results.

[0595] "Smart glasses" refer to wearable devices worn by workers that allow them to input and visualize data in real time.

[0596] "Energy consumption" refers to the consumption of electricity and fuel used in industrial environments.

[0597] "Resource use" refers to the amount of raw materials and resources used in an industrial environment.

[0598] "Real-time" refers to data being entered, analyzed, and displayed immediately, without delay.

[0599] "Evaluation results" refer to the indicators and numerical values ​​of environmental impact obtained by the server through analysis of behavioral data.

[0600] "Sustainable proposals" refer to specific actions and improvement plans to reduce environmental impact based on the analysis results.

[0601] "Means for visual display" refers to a display function that allows analysis results and proposals to be presented to workers in an easy-to-understand manner.

[0602] "Generative AI models" refer to artificial intelligence algorithms and models used to analyze behavioral data and assess environmental impacts.

[0603] This invention is a system that uses smart glasses to collect data on energy consumption and resource usage in industrial environments in real time, analyzes the data, and evaluates the environmental impact and provides sustainable solutions. This system enables efficient energy use and waste reduction, improving sustainability in industrial environments.

[0604] Specifically, the system consists of the following components:

[0605] 1. Data input method: Workers wear smart glasses and input data on energy consumption and resource utilization in industrial environments in real time.

[0606] 2. Data transmission method: The smart glasses convert the collected data into a standard data format and send it to the server via an HTTP request.

[0607] 3. Data analysis method: The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[0608] 4. Method for generating evaluation results: The AI ​​engine evaluates the environmental impact based on behavioral data, specifically calculating indicators such as CO2 emissions and waste volume from energy consumption.

[0609] 5. Proposal generation method: The server generates sustainable options based on the analysis results. For example, for a factory with high energy consumption, it proposes switching to LED lighting and other measures to improve energy efficiency.

[0610] 6. Result display means: The server displays the analysis results and sustainable options on the smart glasses display in real time, allowing workers to immediately check the evaluation results and specific suggestions.

[0611] Hardware and Software

[0612] This system uses the following hardware and software:

[0613] Hardware: Smart glasses (e.g., SmartGlasses), servers

[0614] Software: AI engine for data analysis, HTTP communication protocol, data visualization tool

[0615] Specific examples

[0616] For example, if a part of a factory uses 500 kWh of electricity per month and generates 20 kg of waste, workers can input this data in real time through the smart glasses. The AI ​​engine analyzes the data and displays the result: "CO2 emissions: 250 kg / month." Based on this result, suggestions such as "switching to LED lighting" and "using recyclable materials" are made. These suggestions are displayed in real time on the smart glasses' display, allowing workers to immediately implement improvement measures.

[0617] Prompt Sentence Examples

[0618] Use the data below to conduct an environmental impact assessment of your factory and propose sustainable options.

[0619] data:

[0620] Energy usage: 500kWh

[0621] Waste generated: 20kg

[0622] As described above, the present invention allows workers to understand the environmental impact within a factory and receive sustainable improvement proposals in real time, thereby contributing to the realization of a sustainable society.

[0623] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0624] Step 1:

[0625] Workers wear smart glasses that input real-time data on energy consumption and resource use within industrial environments.

[0626] Input: Energy consumption and resource utilization data within an industrial environment.

[0627] Output: Data is stored in the smart glasses.

[0628] Specific operation: Workers operate the interface of the smart glasses to input data such as power consumption and waste generation.

[0629] Step 2:

[0630] The device (smart glasses) converts the collected data into a standard data format and sends it to the server via an HTTP request.

[0631] Input: Data stored on smart glasses.

[0632] Output: The data sent to the server.

[0633] Specific operation: The smart glasses software converts the input data into JSON format, generates an HTTP POST request, and sends it to the server.

[0634] Step 3:

[0635] The server performs an error check on the received behavioral data to ensure that there are no errors in the data format or content.

[0636] Input: Behavioral data sent to the server.

[0637] Output: Data verified to be error-free.

[0638] Specific operation: A program on the server runs routines that validate data format, numeric range, etc., to check for inconsistencies.

[0639] Step 4:

[0640] The server transfers the data to an AI engine, which analyzes environmental impacts based on behavioral data.

[0641] Input: Behavioral data that has been verified to be error-free.

[0642] Output: An indicator of the environmental impact (e.g. CO2 emissions) as a result of the analysis.

[0643] Specific operation: The server inputs behavioral data into an AI engine and runs an algorithm to calculate environmental impact indicators such as CO2 emissions and waste generation.

[0644] Step 5:

[0645] The server generates sustainable options based on the analysis results.

[0646] Input: Analysis results by the AI ​​engine.

[0647] Output: Proposal of sustainable options.

[0648] Specific actions: Based on the analysis results, the server executes logic to generate specific suggestions for improving energy efficiency (e.g., switching to LED lighting, using recyclable materials).

[0649] Step 6:

[0650] The server displays the assessment results and sustainable options in real time on the smart glasses' display.

[0651] Input: Proposal of sustainable options.

[0652] Output: Evaluation results and suggestions displayed on the smart glasses display.

[0653] How it works: The server sends the evaluation results and suggestions to the smart glasses, which use a rendering engine to visually display them on the display. This information is updated in real time so that the worker can check it at any time.

[0654] Through these processing steps, workers can understand the environmental impact within the factory in real time and immediately incorporate sustainable options. As a specific example, in a factory that uses 500 kWh of electricity per month and generates 20 kg of waste, CO2 emissions are analyzed and suggestions such as switching to LED lighting are displayed in real time.

[0655] 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.

[0656] This invention relates to a system that inputs behavioral data related to users' daily activities and corporate activities, analyzes it on a server, evaluates the environmental impact, and generates and provides sustainable options. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide appropriate sustainable options accordingly. This system enables individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[0657] Program Overview

[0658] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results. In addition, an emotion engine analyzes the user's emotions and adjusts the suggestions accordingly.

[0659] Data entry and submission

[0660] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is converted into a standard data format (e.g., JSON format) by the device. In addition, the user's emotions can be collected, for example, through emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app.

[0661] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[0662] Data analysis

[0663] The server performs an error check on the received behavioral and emotional data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and emotion engine to begin analysis.

[0664] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from power consumption. The emotion engine analyzes the emotion data and evaluates the user's current emotional state. The analysis results are sent back to the server, which then moves on to the next step.

[0665] Generate results and suggestions

[0666] The server generates specific environmental improvement proposals for users based on the analysis results returned by the AI ​​engine and emotion engine. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. The presentation and content of the proposals are also adjusted based on the emotion data from the emotion engine. Specific improvement measures for increasing the energy efficiency of manufacturing processes are also presented to companies.

[0667] Data visualization and presentation

[0668] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[0669] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[0670] User practices

[0671] Users review the analysis results and sustainable options provided, and use them to improve their own lives and business activities. For example, at home, they might purchase LED lighting and choose energy-efficient home appliances. At work, they might improve energy efficiency and reduce waste. By taking into account the emotion analysis results from the emotion engine, users can receive suggestions that match their own psychological state, reducing resistance to putting ideas into practice and making it easier for them to take sustainable actions.

[0672] Specific examples

[0673] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[0674] At the same time, while the user is using the app, the emotion recognition function analyzes the user's face and voice to collect emotional data such as "stress level," which is also sent to the server.

[0675] Once the error check is complete, the server transfers the behavioral data to the AI ​​engine, which analyzes the CO2 emissions. The emotion engine also analyzes the emotional data and evaluates the user's emotional state. For example, the analysis result "100kg CO2 / month" corresponding to 200kWh of electricity usage and the emotion analysis result "stress state" are returned.

[0676] Based on these analysis results, the server generates environmental improvement suggestions such as "using LED lighting" or "introducing energy-saving home appliances," and presents the suggestions in a concise and easy-to-implement format, taking into account the user's stress level. These suggestions are then displayed on the user's dashboard with graphs and charts.

[0677] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[0678] As described above, this system contributes to solving environmental problems by allowing users and businesses to make sustainable choices, and by adjusting the content of suggestions using an emotion engine, it reduces the psychological burden on users and promotes sustainable behavior.

[0679] The processing flow will be explained below.

[0680] Step 1:

[0681] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user enters "200 kWh per month" in the "Electricity Usage" field.

[0682] Step 2:

[0683] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[0684] json

[0685] {

[0686] "userId": "12345",

[0687] "data": {

[0688] "electricity": "200kWh"

[0689] }

[0690] }

[0691] Step 3:

[0692] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[0693] Step 4:

[0694] Collecting user emotional data. For example, an emotion recognition function embedded in a smartphone app uses a camera and microphone to analyze the user's facial expressions and voice to generate emotional data.

[0695] Step 5:

[0696] The device converts the emotion data into JSON format and sends it to the server. The emotion data will be in the following format:

[0697] json

[0698] {

[0699] "userId": "12345",

[0700] "emotion": "stressed"

[0701] }

[0702] Step 6:

[0703] The server receives the HTTP request and analyzes the behavioral and emotional data sent. First, it checks whether the data format is correct.

[0704] Step 7:

[0705] The server transfers the data that passes the error check to the AI ​​engine and emotion engine. Specifically, behavioral data is input into the AI ​​model, and emotion data is input into the emotion analysis model.

[0706] Step 8:

[0707] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[0708] Step 9:

[0709] The emotion engine analyzes the emotion data and evaluates the user's current emotional state, for example recognizing the state "stressed."

[0710] Step 10:

[0711] The AI ​​engine and emotion engine send their analysis results back to the server, which then generates specific suggestions for improving the user's environment based on these results.

[0712] Step 11:

[0713] The server generates sustainable options based on the evaluation results and adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it will present suggestions such as "using LED lighting" or "introducing energy-saving appliances" in a concise and easy-to-implement format.

[0714] Step 12:

[0715] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[0716] Step 13:

[0717] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[0718] Step 14:

[0719] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[0720] Step 15:

[0721] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[0722] Example 2

[0723] 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."

[0724] In modern society, the impact of everyday user and corporate activities on the environment has become a major issue. However, making sustainable choices requires specific assessments of environmental impacts and appropriate guidelines for action based on those assessments. Furthermore, users' emotional states can be important factors in taking sustainable actions on an ongoing basis, but current systems lack the means to comprehensively assess and adjust these. Therefore, there is a need to provide an environment that makes it easier for users to make sustainable choices based on appropriate information.

[0725] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing behavioral data and emotional data to evaluate environmental influences and the emotional state, means for generating sustainable options based on the evaluation results and adjusting the options according to the emotional state, and means for providing the adjusted options to the user. This makes it possible for the user to understand environmental influences, receive sustainable options that match their own emotional state, and easily implement them.

[0726] "Behavioral data" refers to information about users' daily activities and corporate activities, and refers to specific data such as electricity consumption, waste output, and transportation usage.

[0727] "Emotional data" refers to information that indicates the user's emotional state, such as "stress level" and "happiness" collected through facial recognition and voiceprint analysis.

[0728] A "terminal" is a device or software, such as a smartphone app or web portal, that allows a user to input data and send it to a server.

[0729] "Server" means a digital processing system for receiving and analyzing behavioral and emotional data, generating and providing sustainable options to users.

[0730] An "AI engine" is a computational system that includes algorithms and programs for analyzing behavioral data and assessing environmental impacts.

[0731] An "emotion engine" is a computational system that includes algorithms and programs for analyzing emotion data and assessing a user's emotional state.

[0732] "Sustainable options" are specific recommended actions and proposals for reducing environmental impact and promoting the realization of a sustainable society.

[0733] "Adjustment" refers to changing the content and presentation of sustainable options depending on the user's emotional state.

[0734] "Visualization" refers to converting analytical results into a visual format such as a graph or chart in order to display them in an easy-to-understand manner.

[0735] A "dashboard" is an interface that visually displays analysis results and sustainable options, allowing users to easily check them.

[0736] This system inputs behavioral and emotional data related to users' daily activities and business activities, analyzes the data on a server to evaluate the environmental impact, and uses an emotion engine to recognize the user's emotional state and provide appropriate sustainable options. This system aims to contribute to the realization of a sustainable society.

[0737] This system has a terminal where users input behavioral data and emotional data, a server that receives and analyzes this data, a means for presenting sustainable options based on the analysis results, and a means for the emotion engine to analyze the user's emotions and adjust the content of the suggestions.

[0738] Data entry and submission

[0739] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). Users also provide their own emotional data through facial recognition and voiceprint analysis via the app's emotion recognition feature. This collected data is stored on the device and converted to JSON format.

[0740] The terminal sends this converted data to the server as an HTTP request, which the server receives and proceeds to the next analysis step.

[0741] Data analysis

[0742] The server receives the JSON data sent from the device and first performs an error check. Behavioral data that passes the error check is transferred to the AI ​​engine, and emotional data is transferred to the emotion engine. The AI ​​engine calculates environmental indicators such as CO2 emissions, water consumption, and waste volume based on the behavioral data. The emotion engine analyzes the emotional data and evaluates the user's emotional state (e.g., "stress state").

[0743] Generate results and suggestions

[0744] The server integrates the analysis results returned by the AI ​​engine and emotion engine to generate specific suggestions for improving the environment. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. Furthermore, based on the results of the emotion engine, it makes simple and easy-to-implement suggestions to users who are under stress.

[0745] Data visualization and presentation

[0746] The server converts the analysis results into graphs and charts for easy visual understanding. Users can view these visualized data on a smartphone app or web portal dashboard. The system also generates detailed environmental impact reports for businesses and provides analysis data via an API for educators and policymakers.

[0747] User practices

[0748] Users can check the analysis results and environmental improvement suggestions provided on the dashboard and take action based on them. For example, at home, they can purchase and install LED lighting or choose energy-efficient home appliances. At business, they can improve energy efficiency or implement waste reduction measures. In this way, users can reduce their environmental impact through concrete actions.

[0749] Specific examples

[0750] For example, suppose a user uses 200 kWh of electricity per month. The user enters this data into a smartphone app and presses the send button. The device converts the data into JSON format and uploads it to the server. At the same time, the emotion recognition function analyzes the user's face and voice, collects emotional data on "stress state," and sends it to the server.

[0751] After receiving this data, the server performs an error check. After the error check, the behavioral data is transferred to the AI ​​engine, and the emotional data is transferred to the emotion engine. The AI ​​engine returns the result that "200 kWh of electricity usage per month will result in 100 kg of CO2 emissions," and the emotion engine returns the analysis result that "the user's emotional state is stressed."

[0752] Based on these analysis results, the server generates specific environmental improvement suggestions, such as "using LED lighting" or "introducing energy-saving home appliances," and adjusts the suggestions to be simple and easy to implement, taking into account the user's stress level. These suggestions are displayed on the user's dashboard with graphs and charts.

[0753] An example of a prompt is, "If a user uses 200 kWh of electricity per month and is in a stressed state, please explain in detail how the program will display the analysis results and suggestions on a dashboard along with graphs." In this way, users can take specific actions based on the analysis results and suggestions to reduce their environmental impact.

[0754] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0755] Step 1: Data entry

[0756] Users use a smartphone app or web portal to input data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste generation, and commuting method (private car, public transportation). This data is entered into the device. The input data is collected based on the user's behavior.

[0757] Step 2: Collecting emotion data

[0758] Users use the smartphone app's emotion recognition function to provide emotional data through facial recognition and voiceprint analysis. Specifically, the app uses the camera and microphone to analyze the user's facial expressions and voice, detecting emotions such as "stress" and "happiness." This emotional data is also input into the device.

[0759] Step 3: Transform the data

[0760] The device converts the input behavioral and emotional data into JSON format. For example, it converts data on 200 kWh of electricity usage and stress level into JSON format and creates a single data package. This conversion allows the data to be sent to the server in a standard format.

[0761] Step 4: Sending data

[0762] The device generates an HTTP request with the data converted into JSON format and sends it to the server. The data sent includes behavioral data and emotion data. Once the data is sent from the device to the server, the next analysis step begins.

[0763] Step 5: Error checking

[0764] The server parses the received JSON data and performs an error check. This checks whether there are any errors in the data format or content. For example, it verifies whether the amount of electricity used is a number, and whether the emotion data is in the correct format. Once the error check is complete, the data can proceed to analysis.

[0765] Step 6: Analyze behavioral data

[0766] The server transfers the behavioral data that has passed the error check to the AI ​​engine. The AI ​​engine evaluates the environmental impact based on the behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity consumption. For example, the evaluation result may be, "100 kg of CO2 will be emitted from 200 kWh of electricity consumption per month."

[0767] Step 7: Analyze the sentiment data

[0768] The server transfers the emotion data to the emotion engine. The emotion engine analyzes the emotion data and evaluates the user's emotional state. For example, the result may be "The user's emotional state is stressed." Based on this analysis, the suggestions are adjusted according to the user's emotional state.

[0769] Step 8: Generate results and recommendations

[0770] The server combines the analysis results of the AI ​​engine and the emotion engine to generate specific suggestions for improving the environment. For example, they might suggest switching to LED lighting to reduce power consumption or recommend installing energy-efficient home appliances. Based on the results of the emotion engine, the server also provides simple, easy-to-implement suggestions to users who are under stress.

[0771] Step 9: Visualize the data

[0772] The server generates graphs and charts to make the analysis results visually easy to understand. For example, CO2 emissions are displayed in a pie chart, and specific values ​​are displayed in a bar graph. This visualization allows users to intuitively understand their own environmental impact.

[0773] Step 10: Provide data

[0774] The server then sends the generated graphs and charts to the device, where they are displayed on a dashboard in a web portal or smartphone app. Users can view the analysis results and sustainable choices on the dashboard. The system also provides detailed environmental impact reports for businesses and analytical data via an API for educators and policymakers.

[0775] Step 11: User Practice

[0776] Users can review the analysis results and sustainable choices provided by the dashboard and use them to improve their own lives and business activities. For example, households can purchase LED lighting and choose energy-efficient home appliances, while businesses can implement energy efficiency improvements and waste reduction measures. This allows users to reduce their environmental impact through concrete actions.

[0777] Through these specific processing steps, users and businesses can make environmentally sustainable choices and contribute to the realization of a sustainable society.

[0778] (Application example 2)

[0779] 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."

[0780] This invention relates to a system that evaluates the environmental impact of users' daily activities and corporate activities and provides sustainable options. However, conventional systems do not consider the user's emotional state when proposing sustainable options, making it difficult to translate these options into concrete action. Furthermore, there has been a lack of proposals for simultaneously reducing environmental impact and improving customer satisfaction in certain industries, particularly brick-and-mortar stores.

[0781] 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 the user to input behavioral data, means for the terminal to transmit the behavioral data to the server, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, means for the server to provide the options to the user, means for an emotion recognition engine to analyze the user's emotion data, and means for the server to suggest options adapted to the emotions based on the analysis results. This makes it possible to provide specific, emotion-sensitive sustainable options for users' daily behavior and corporate activities, and in particular, in physical stores, it becomes possible to monitor energy consumption, waste volume, and customer satisfaction and generate management proposals based on the results.

[0782] "User" refers to an individual or company that uses the system.

[0783] "Behavioral data" refers to information such as electricity consumption, waste volume, and transportation methods related to users' daily behavior and corporate activities.

[0784] "Terminal" refers to a device that allows a user to input behavioral data and transmit it to a server. Examples include smartphones and computers.

[0785] "Server" refers to a computer system that receives behavioral data sent from a terminal, analyzes it, and provides the results to the user.

[0786] "Analysis" refers to the process by which the server processes the received behavioral data and evaluates the environmental impact.

[0787] "Environmental impact" refers to the impact that a user's actions have on the environment, and is evaluated using indicators such as CO2 emissions and waste volume.

[0788] "Sustainable options" refers to specific suggestions that encourage users to adopt environmentally friendly actions and choices, such as using energy-efficient appliances or taking public transport.

[0789] An "emotion recognition engine" refers to software technology that analyzes a user's emotional state and uses the results to make appropriate suggestions.

[0790] "Emotional data" refers to data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, etc.

[0791] "Monitoring" refers to the process of regularly observing and recording specific indicators (e.g., energy use, waste volume, customer satisfaction).

[0792] "Operation proposals" refer to specific improvement measures and guidelines for action that the server provides to physical store operators based on the analysis results.

[0793] "Customer satisfaction" refers to an indicator of the level of satisfaction that customers who visit a physical store feel toward the service.

[0794] "Visualization" refers to the process of visually displaying analysis results in graphs, charts, etc., and presenting them to users in an easy-to-understand manner.

[0795] The present invention relates to a system that inputs behavioral data and emotional data related to users' daily activities and business activities, analyzes the data on a server, evaluates the environmental impact, and provides sustainable options. Specifically, the system includes a terminal, a server, an emotion recognition engine, and a data visualization means.

[0796] Data entry and submission

[0797] Users input behavioral data using a device (e.g., a smartphone). This behavioral data includes energy consumption, waste volume, transportation methods, etc. This data is converted into a standard data format (e.g., JSON format). In addition, emotion data of the user is collected using emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app. This data is sent to the server via an HTTP request.

[0798] Data reception and analysis

[0799] The server receives the data sent from the device and first checks for errors. After checking, the behavioral data is transferred to an AI engine to evaluate the environmental impact (e.g., CO2 emissions). The emotion data is analyzed by an emotion recognition engine to evaluate the user's emotional state. This provides information such as whether the user is stressed or relaxed.

[0800] Generate results and suggestions

[0801] The server generates sustainable options based on the analysis results obtained from the AI ​​engine and emotion recognition engine. For example, if energy usage is high, it will suggest installing energy-efficient appliances or switching to LED lighting. It also adjusts the content and presentation of the suggestions based on emotion data. For users in a stressed state, it provides simple, easy-to-implement suggestions.

[0802] Data visualization and presentation

[0803] The server visually displays the analysis results in graphs and charts, allowing users to intuitively understand their own environmental impact. For example, it may provide information such as the CO2 emissions corresponding to 200 kWh of energy consumption (e.g., 100 kg CO2 / month). This data is displayed on the device's dashboard. This display is generated using the matplotlib library.

[0804] User practices

[0805] Users can review the sustainable options presented and take specific actions. Store managers can use the information provided by the app to implement specific measures to reduce energy consumption and waste. For example, introducing LED lighting can reduce environmental impact.

[0806] Prompt Sentence Examples

[0807] "Enter your store's monthly energy use and waste volume, as well as customer reviews. The results assess your environmental impact and provide sustainable options."

[0808] As described above, the system of the present invention can promote environmentally conscious behavior by integrating and analyzing behavioral data and emotional data, and providing optimal sustainable options to users.

[0809] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0810] Step 1:

[0811] The user inputs behavioral data using a device. The user inputs data related to daily activities and store operations, such as energy consumption, waste volume, and transportation methods, into the application. At this time, the emotion recognition function analyzes the user's facial expressions and voice to collect emotional data. The input data and emotional data are converted into JSON format.

[0812] Step 2:

[0813] The device sends input data and emotion data to the server. The device generates an HTTP request and uploads the input behavioral data and emotion data to the server. The server receives the data and performs error checks to ensure it is in the correct format. Here, the input is behavioral data and emotion data, and the output is error-checked data.

[0814] Step 3:

[0815] The server analyzes the behavioral data. After error checking, the server transfers the behavioral data to an AI engine, which calculates environmental impacts such as CO2 emissions, energy consumption, and waste volume. The input is the error-checked behavioral data, and the output is the analyzed environmental impact data.

[0816] Step 4:

[0817] The server analyzes the emotion data. The server sends the error-checked emotion data to an emotion recognition engine to evaluate the user's current emotional state, where the input is the error-checked emotion data and the output is the user's emotional state data.

[0818] Step 5:

[0819] The server generates sustainable options based on the analysis results. The server combines the analysis results obtained from the AI ​​engine and emotion recognition engine to generate specific environmental improvement measures. For example, for users with high energy usage, it suggests introducing energy-saving home appliances and switching to LED lighting. The input is environmental impact data and emotional state data, and the output is sustainable options.

[0820] Step 6:

[0821] The server visualizes the proposals. The server displays the sustainable options generated in the previous step in a visual format such as graphs and charts. This makes it easier for users to intuitively understand their own environmental impacts. The input is the sustainable options, and the output is the visualized data.

[0822] Step 7:

[0823] The server provides the user with the proposed solutions. The server sends the visualized analysis results and sustainable options to the user's device. The user can then review the results in the application and take specific actions. The input is the visualized data, and the output is a notification to the user.

[0824] Through these processing steps, users can obtain specific, emotionally sensitive, and sustainable choices based on their own behavioral data. This system promotes environmentally friendly behavior and supports users' sustainable behavior.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] [Third embodiment]

[0829] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0830] 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.

[0831] 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).

[0832] 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.

[0833] 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.

[0834] 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).

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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."

[0841] This invention relates to a system that inputs data on users' daily activities and corporate activities, analyzes the data on a server, evaluates the environmental impact, and generates and provides sustainable options. This system allows individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[0842] Program Overview

[0843] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results.

[0844] Data entry and submission

[0845] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is then converted into a standard data format (e.g., JSON) by the device.

[0846] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[0847] Data analysis

[0848] The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[0849] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity usage. The analysis results are sent back to the server, which then moves on to the next step.

[0850] Generate results and suggestions

[0851] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, it suggests switching to LED lighting and choosing more energy-efficient home appliances for households with high electricity consumption. For businesses, it suggests specific improvement measures to increase the energy efficiency of their manufacturing processes.

[0852] Data visualization and presentation

[0853] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[0854] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[0855] User practices

[0856] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[0857] Specific examples

[0858] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[0859] Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis results in "100 kg CO2 / month." Based on this, the server generates specific recommendations, such as "using LED lighting" and "introducing energy-efficient home appliances." These recommendations are displayed on the user's dashboard with graphs and charts.

[0860] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[0861] As described above, this system contributes to solving environmental problems by helping users and businesses make sustainable choices.

[0862] The processing flow will be explained below.

[0863] Step 1:

[0864] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user might enter "200 kWh per month" in the "Electricity Usage" field.

[0865] Step 2:

[0866] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[0867] json

[0868] {

[0869] "userId": "12345",

[0870] "data": {

[0871] "electricity": "200kWh"

[0872] }

[0873] }

[0874] Step 3:

[0875] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[0876] Step 4:

[0877] The server receives the HTTP request and parses the JSON data sent. First, it checks whether the data format is correct or not.

[0878] Step 5:

[0879] The server transfers data that passes the error check to the AI ​​engine, which then inputs the data into the AI ​​model running on the server and begins analysis.

[0880] Step 6:

[0881] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[0882] Step 7:

[0883] The AI ​​engine sends the analysis results back to the server, which receives them and proceeds to the next step.

[0884] Step 8:

[0885] Based on the analysis results received by the server, it generates specific environmental improvement proposals for the user, such as a list of proposals such as "using LED lighting" or "introducing energy-efficient home appliances."

[0886] Step 9:

[0887] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[0888] Step 10:

[0889] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[0890] Step 11:

[0891] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[0892] Step 12:

[0893] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[0894] Through these steps, the ImpactScope system assesses environmental impacts, generates sustainable options, and supports users and companies in making concrete improvements to their behavior.

[0895] Example 1

[0896] 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."

[0897] In modern society, it is important for individuals and businesses to choose appropriate actions to reduce their environmental impact. However, most people do not understand the extent to which their daily actions and corporate activities affect the environment. There is also a lack of information and advice on how to find environmentally friendly options. This makes it difficult to achieve a sustainable society.

[0898] 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.

[0899] In this invention, the server includes means for a user to input behavioral data, means for a terminal to transmit the behavioral data to the server, means for the server to perform error checking on the behavioral data, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, and means for the server to generate a diagram for visualizing the evaluation results and sustainable options and provide it to the user. This enables individuals and companies to understand their own environmental impacts and reduce environmental loads through specific sustainable options.

[0900] "User" refers to an individual or company that uses the system to input data about their daily activities or business activities.

[0901] "Behavioral data" refers to information about a user's daily behavior and business activities, including electricity consumption, waste output, and commuting methods.

[0902] A "terminal" is a device used by a user to input behavioral data and transmit the data to a server, and includes a smartphone or computer.

[0903] "Server" refers to the central computer system that receives, analyzes, and generates sustainable options based on behavioral data sent by users.

[0904] "Error checking" refers to the process by which the server examines the format and content of the behavioral data it receives to detect errors.

[0905] "Analysis" refers to the process by which the server uses an AI engine to evaluate behavioral data and generate indicators related to environmental impact.

[0906] "Environmental impact" refers to the impact that a user's actions have on the environment, and specifically includes CO2 emissions, water consumption, waste volume, etc.

[0907] "Sustainable options" refers to specific actions and options for reducing environmental impact that are generated by the server based on the analysis results.

[0908] "Graphs" refers to graphs and charts that visually display analysis results and sustainable options.

[0909] A "report" refers to a document that compiles detailed analysis results and recommendations generated for a company.

[0910] A "generative AI model" is a type of artificial intelligence technology used for analysis, and refers to an algorithm that evaluates environmental impacts based on behavioral data.

[0911] The present invention is a system in which a user inputs behavioral data, which is analyzed by a server to evaluate the environmental impact, and which provides sustainable options. Specific embodiments of the system are described below.

[0912] System Overview

[0913] This system mainly consists of a terminal where users input behavioral data, a server that receives and analyzes the input data, and a means for presenting sustainable options based on the analysis results.

[0914] Data Entry

[0915] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste output, and commuting method (private car, public transport). This data is then converted into a standard data format (e.g., JSON) by the device.

[0916] Data transmission

[0917] The terminal sends the generated data to the server as an HTTP request. At this time, the data is sent using a secure protocol (e.g., HTTPS) to maintain the integrity and security of the data.

[0918] Data reception and error checking

[0919] The server receives the data sent from the device and checks its format and content for errors. Specifically, it checks whether the data is in JSON format and whether all required fields are included. If the data is invalid, it returns an error message to the user.

[0920] Data analysis

[0921] Once the check is complete, the data is transferred to an AI engine on the server. The AI ​​engine evaluates the environmental impact based on the behavioral data, calculating CO2 emissions from electricity consumption, for example. The analysis results are then sent back to the server.

[0922] Generating sustainable options

[0923] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, if electricity usage is high, it will suggest the introduction of LED lighting and energy-efficient home appliances. These suggestions are presented as specific action items.

[0924] Data visualization and presentation

[0925] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to understand their environmental impact at a glance. The generated graphs and charts are displayed on a dashboard in a web portal or smartphone app. It also generates detailed environmental impact reports for businesses and provides data to educators and policymakers via an API.

[0926] User practices

[0927] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[0928] Specific examples

[0929] As a concrete example, let's assume that a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks the format and content for errors. Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis result is calculated as "100 kg CO2 / month." Based on this, the server makes suggestions such as "using LED lighting" and "introducing energy-saving home appliances." These suggestions are displayed on the user's dashboard with graphs and charts. The user checks them and actually purchases and installs LED lighting in their home.

[0930] Prompt Sentence Examples

[0931] "Enter your monthly household electricity usage of 200 kWh to view your environmental impact analysis and sustainable options."

[0932] keyword

[0933] Generative AI model, prompt sentence

[0934] As described above, this system allows individuals and businesses to make environmentally friendly choices and contribute to the realization of a sustainable society.

[0935] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0936] Step 1: Data entry

[0937] Users: Use a smartphone app or web portal to enter data about their daily activities and business activities.

[0938] Specific operation: The user opens the app, enters the amount of electricity usage as "200kWh per month," and clicks the submit button.

[0939] Input: Data related to users' daily activities and business activities (e.g., electricity usage)

[0940] Output: Behavioral data in standard data format (JSON format)

[0941] Step 2: Data conversion

[0942] Terminal: Receives data entered by the user and converts it into a standard data format (e.g., JSON format).

[0943] Specific operation: The terminal receives the input data "Power usage: 200kWh" and converts it into JSON format "{"Power usage": "200kWh"}".

[0944] Input: Raw data entered by the user

[0945] Output: Behavioral data in JSON format

[0946] Step 3: Send data

[0947] Terminal: Sends the generated data to the server as an HTTP request.

[0948] Specific operation: The device uses the HTTPS protocol to send the data "{"power usage": "200kWh"}" to the server.

[0949] Input: JSON formatted behavior data

[0950] Output: HTTP request to the server

[0951] Step 4: Receiving Data

[0952] Server: Receives data sent from the terminal.

[0953] Specific operation: The server listens for HTTP requests on the receiving port and receives the data that arrives.

[0954] Input: HTTP request sent from the terminal

[0955] Output: Received behavior data in JSON format

[0956] Step 5: Error checking

[0957] Server: Checks the format and content of the received data for errors.

[0958] What happens: The server uses a JSON schema to check that "{"Power Usage": "200kWh"}" is in the correct format and contains all required fields.

[0959] Input: JSON formatted behavior data

[0960] Output: Normal data or error message

[0961] Step 6: Data analysis

[0962] Server: Transfers the checked data to the AI ​​engine and evaluates the environmental impact.

[0963] Specific operation: The server calls the AI ​​engine's analysis API and inputs behavioral data. The AI ​​engine analyzes the behavioral data and calculates CO2 emissions.

[0964] Input: Normal data

[0965] Output: Analysis results of environmental impact (e.g., "100 kg CO2 / month")

[0966] Step 7: Generate sustainable options

[0967] Server: Based on the analysis results, it generates optimal sustainable options for users and companies.

[0968] Specific actions: The server evaluates the analysis results and generates specific action items such as "use LED lighting" or "introduce energy-saving home appliances."

[0969] Input: Analysis results

[0970] Output: Sustainable Choice

[0971] Step 8: Visualize the data

[0972] Server: Generates graphs and charts to visually display analysis results and sustainable options.

[0973] Specific operation: The server creates graphs and charts based on the analysis results and inserts them into the user's dashboard in HTML format.

[0974] Input: Analysis results and sustainable options

[0975] Output: Visualized data (graphs and charts)

[0976] Step 9: Provide data

[0977] Server: Generates environmental impact reports for companies and provides data via APIs as needed.

[0978] Specific operation: The server compiles the analysis data and proposals into a PDF report and provides a download link to companies. It also prepares an API to provide the analysis data to educators and policymakers.

[0979] Input: Analytical data and sustainable options

[0980] Output: Environmental Impact Report and API endpoints

[0981] Step 10: User Practice

[0982] Users: Review the analysis results and sustainable options provided to improve their own lives and business activities.

[0983] Specific actions: A user views the dashboard, purchases and installs LED lighting in their home, and a business implements suggested energy efficiency improvements.

[0984] Input: Analysis results and sustainable options

[0985] Output: Reduction of environmental impact (results of concrete actions)

[0986] Through these steps, the system provides concrete support for users and businesses to make sustainable choices and reduce their environmental impact.

[0987] (Application example 1)

[0988] 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."

[0989] To achieve a sustainable society, individuals and businesses are required to make environmentally friendly choices. However, currently, there is a lack of systems to assess environmental impacts and provide sustainable options. Another problem is that there is no established method to collect real-time data on energy consumption and resource use in industrial environments and provide immediate improvement recommendations. This makes it difficult to achieve efficient energy use and reduce waste.

[0990] 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.

[0991] In this invention, the server includes a means for workers to input data on energy consumption and resource utilization in industrial environments in real time through smart glasses, a means for the smart glasses to visually display assessment results and sustainable proposals in real time, and a means for the server to perform analysis using a generative AI model based on the environmental data provided through the smart glasses, thereby enabling workers to understand environmental impacts in real time and immediately adopt sustainable options.

[0992] "User" refers to an individual or company that uses the system.

[0993] "Behavioral data" refers to information about users' daily behavior and business activities, and specifically includes energy consumption and resource usage.

[0994] "Terminal" refers to an electronic device that allows a user to input behavioral data and transmit it to a server.

[0995] "Server" refers to a central computer system that receives and analyzes behavioral data and generates and provides sustainable options based on the evaluation results.

[0996] "Smart glasses" refer to wearable devices worn by workers that allow them to input and visualize data in real time.

[0997] "Energy consumption" refers to the consumption of electricity and fuel used in industrial environments.

[0998] "Resource use" refers to the amount of raw materials and resources used in an industrial environment.

[0999] "Real-time" refers to data being entered, analyzed, and displayed immediately, without delay.

[1000] "Evaluation results" refer to the indicators and numerical values ​​of environmental impact obtained by the server through analysis of behavioral data.

[1001] "Sustainable proposals" refer to specific actions and improvement plans to reduce environmental impact based on the analysis results.

[1002] "Means for visual display" refers to a display function that allows analysis results and proposals to be presented to workers in an easy-to-understand manner.

[1003] "Generative AI models" refer to artificial intelligence algorithms and models used to analyze behavioral data and assess environmental impacts.

[1004] This invention is a system that uses smart glasses to collect data on energy consumption and resource usage in industrial environments in real time, analyzes the data, and evaluates the environmental impact and provides sustainable solutions. This system enables efficient energy use and waste reduction, improving sustainability in industrial environments.

[1005] Specifically, the system consists of the following components:

[1006] 1. Data input method: Workers wear smart glasses and input data on energy consumption and resource utilization in industrial environments in real time.

[1007] 2. Data transmission method: The smart glasses convert the collected data into a standard data format and send it to the server via an HTTP request.

[1008] 3. Data analysis method: The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[1009] 4. Method for generating evaluation results: The AI ​​engine evaluates the environmental impact based on behavioral data, specifically calculating indicators such as CO2 emissions and waste volume from energy consumption.

[1010] 5. Proposal generation method: The server generates sustainable options based on the analysis results. For example, for a factory with high energy consumption, it proposes switching to LED lighting and other measures to improve energy efficiency.

[1011] 6. Result display means: The server displays the analysis results and sustainable options on the smart glasses display in real time, allowing workers to immediately check the evaluation results and specific suggestions.

[1012] Hardware and Software

[1013] This system uses the following hardware and software:

[1014] Hardware: Smart glasses (e.g., SmartGlasses), servers

[1015] Software: AI engine for data analysis, HTTP communication protocol, data visualization tool

[1016] Specific examples

[1017] For example, if a part of a factory uses 500 kWh of electricity per month and generates 20 kg of waste, workers can input this data in real time through the smart glasses. The AI ​​engine analyzes the data and displays the result: "CO2 emissions: 250 kg / month." Based on this result, suggestions such as "switching to LED lighting" and "using recyclable materials" are made. These suggestions are displayed in real time on the smart glasses' display, allowing workers to immediately implement improvement measures.

[1018] Prompt Sentence Examples

[1019] Use the data below to conduct an environmental impact assessment of your factory and propose sustainable options.

[1020] data:

[1021] Energy usage: 500kWh

[1022] Waste generated: 20kg

[1023] As described above, the present invention allows workers to understand the environmental impact within a factory and receive sustainable improvement proposals in real time, thereby contributing to the realization of a sustainable society.

[1024] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1025] Step 1:

[1026] Workers wear smart glasses that input real-time data on energy consumption and resource use within industrial environments.

[1027] Input: Energy consumption and resource utilization data within an industrial environment.

[1028] Output: Data is stored in the smart glasses.

[1029] Specific operation: Workers operate the interface of the smart glasses to input data such as power consumption and waste generation.

[1030] Step 2:

[1031] The device (smart glasses) converts the collected data into a standard data format and sends it to the server via an HTTP request.

[1032] Input: Data stored on smart glasses.

[1033] Output: The data sent to the server.

[1034] Specific operation: The smart glasses software converts the input data into JSON format, generates an HTTP POST request, and sends it to the server.

[1035] Step 3:

[1036] The server performs an error check on the received behavioral data to ensure that there are no errors in the data format or content.

[1037] Input: Behavioral data sent to the server.

[1038] Output: Data verified to be error-free.

[1039] Specific operation: A program on the server runs routines that validate data format, numeric range, etc., to check for inconsistencies.

[1040] Step 4:

[1041] The server transfers the data to an AI engine, which analyzes environmental impacts based on behavioral data.

[1042] Input: Behavioral data that has been verified to be error-free.

[1043] Output: An indicator of the environmental impact (e.g. CO2 emissions) as a result of the analysis.

[1044] Specific operation: The server inputs behavioral data into an AI engine and runs an algorithm to calculate environmental impact indicators such as CO2 emissions and waste generation.

[1045] Step 5:

[1046] The server generates sustainable options based on the analysis results.

[1047] Input: Analysis results by the AI ​​engine.

[1048] Output: Proposal of sustainable options.

[1049] Specific actions: Based on the analysis results, the server executes logic to generate specific suggestions for improving energy efficiency (e.g., switching to LED lighting, using recyclable materials).

[1050] Step 6:

[1051] The server displays the assessment results and sustainable options in real time on the smart glasses' display.

[1052] Input: Proposal of sustainable options.

[1053] Output: Evaluation results and suggestions displayed on the smart glasses display.

[1054] How it works: The server sends the evaluation results and suggestions to the smart glasses, which use a rendering engine to visually display them on the display. This information is updated in real time so that the worker can check it at any time.

[1055] Through these processing steps, workers can understand the environmental impact within the factory in real time and immediately incorporate sustainable options. As a specific example, in a factory that uses 500 kWh of electricity per month and generates 20 kg of waste, CO2 emissions are analyzed and suggestions such as switching to LED lighting are displayed in real time.

[1056] 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.

[1057] This invention relates to a system that inputs behavioral data related to users' daily activities and corporate activities, analyzes it on a server, evaluates the environmental impact, and generates and provides sustainable options. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide appropriate sustainable options accordingly. This system enables individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[1058] Program Overview

[1059] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results. In addition, an emotion engine analyzes the user's emotions and adjusts the suggestions accordingly.

[1060] Data entry and submission

[1061] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is converted into a standard data format (e.g., JSON format) by the device. In addition, the user's emotions can be collected, for example, through emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app.

[1062] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[1063] Data analysis

[1064] The server performs an error check on the received behavioral and emotional data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and emotion engine to begin analysis.

[1065] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from power consumption. The emotion engine analyzes the emotion data and evaluates the user's current emotional state. The analysis results are sent back to the server, which then moves on to the next step.

[1066] Generate results and suggestions

[1067] The server generates specific environmental improvement proposals for users based on the analysis results returned by the AI ​​engine and emotion engine. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. The presentation and content of the proposals are also adjusted based on the emotion data from the emotion engine. Specific improvement measures for increasing the energy efficiency of manufacturing processes are also presented to companies.

[1068] Data visualization and presentation

[1069] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[1070] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[1071] User practices

[1072] Users review the analysis results and sustainable options provided, and use them to improve their own lives and business activities. For example, at home, they might purchase LED lighting and choose energy-efficient home appliances. At work, they might improve energy efficiency and reduce waste. By taking into account the emotion analysis results from the emotion engine, users can receive suggestions that match their own psychological state, reducing resistance to putting ideas into practice and making it easier for them to take sustainable actions.

[1073] Specific examples

[1074] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[1075] At the same time, while the user is using the app, the emotion recognition function analyzes the user's face and voice to collect emotional data such as "stress level," which is also sent to the server.

[1076] Once the error check is complete, the server transfers the behavioral data to the AI ​​engine, which analyzes the CO2 emissions. The emotion engine also analyzes the emotional data and evaluates the user's emotional state. For example, the analysis result "100kg CO2 / month" corresponding to 200kWh of electricity usage and the emotion analysis result "stress state" are returned.

[1077] Based on these analysis results, the server generates environmental improvement suggestions such as "using LED lighting" or "introducing energy-saving home appliances," and presents the suggestions in a concise and easy-to-implement format, taking into account the user's stress level. These suggestions are then displayed on the user's dashboard with graphs and charts.

[1078] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[1079] As described above, this system contributes to solving environmental problems by allowing users and businesses to make sustainable choices, and by adjusting the content of suggestions using an emotion engine, it reduces the psychological burden on users and promotes sustainable behavior.

[1080] The processing flow will be explained below.

[1081] Step 1:

[1082] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user enters "200 kWh per month" in the "Electricity Usage" field.

[1083] Step 2:

[1084] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[1085] json

[1086] {

[1087] "userId": "12345",

[1088] "data": {

[1089] "electricity": "200kWh"

[1090] }

[1091] }

[1092] Step 3:

[1093] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[1094] Step 4:

[1095] Collecting user emotional data. For example, an emotion recognition function embedded in a smartphone app uses a camera and microphone to analyze the user's facial expressions and voice to generate emotional data.

[1096] Step 5:

[1097] The device converts the emotion data into JSON format and sends it to the server. The emotion data will be in the following format:

[1098] json

[1099] {

[1100] "userId": "12345",

[1101] "emotion": "stressed"

[1102] }

[1103] Step 6:

[1104] The server receives the HTTP request and analyzes the behavioral and emotional data sent. First, it checks whether the data format is correct.

[1105] Step 7:

[1106] The server transfers the data that passes the error check to the AI ​​engine and emotion engine. Specifically, behavioral data is input into the AI ​​model, and emotion data is input into the emotion analysis model.

[1107] Step 8:

[1108] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[1109] Step 9:

[1110] The emotion engine analyzes the emotion data and evaluates the user's current emotional state, for example recognizing the state "stressed."

[1111] Step 10:

[1112] The AI ​​engine and emotion engine send their analysis results back to the server, which then generates specific suggestions for improving the user's environment based on these results.

[1113] Step 11:

[1114] The server generates sustainable options based on the evaluation results and adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it will present suggestions such as "using LED lighting" or "introducing energy-saving appliances" in a concise and easy-to-implement format.

[1115] Step 12:

[1116] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[1117] Step 13:

[1118] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[1119] Step 14:

[1120] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[1121] Step 15:

[1122] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[1123] Example 2

[1124] 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."

[1125] In modern society, the impact of everyday user and corporate activities on the environment has become a major issue. However, making sustainable choices requires specific assessments of environmental impacts and appropriate guidelines for action based on those assessments. Furthermore, users' emotional states can be important factors in taking sustainable actions on an ongoing basis, but current systems lack the means to comprehensively assess and adjust these. Therefore, there is a need to provide an environment that makes it easier for users to make sustainable choices based on appropriate information.

[1126] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing behavioral data and emotional data to evaluate environmental influences and the emotional state, means for generating sustainable options based on the evaluation results and adjusting the options according to the emotional state, and means for providing the adjusted options to the user. This makes it possible for the user to understand environmental influences, receive sustainable options that match their own emotional state, and easily implement them.

[1127] "Behavioral data" refers to information about users' daily activities and corporate activities, and refers to specific data such as electricity consumption, waste output, and transportation usage.

[1128] "Emotional data" refers to information that indicates the user's emotional state, such as "stress level" and "happiness" collected through facial recognition and voiceprint analysis.

[1129] A "terminal" is a device or software, such as a smartphone app or web portal, that allows a user to input data and send it to a server.

[1130] "Server" means a digital processing system for receiving and analyzing behavioral and emotional data, generating and providing sustainable options to users.

[1131] An "AI engine" is a computational system that includes algorithms and programs for analyzing behavioral data and assessing environmental impacts.

[1132] An "emotion engine" is a computational system that includes algorithms and programs for analyzing emotion data and assessing a user's emotional state.

[1133] "Sustainable options" are specific recommended actions and proposals for reducing environmental impact and promoting the realization of a sustainable society.

[1134] "Adjustment" refers to changing the content and presentation of sustainable options depending on the user's emotional state.

[1135] "Visualization" refers to converting analytical results into a visual format such as a graph or chart in order to display them in an easy-to-understand manner.

[1136] A "dashboard" is an interface that visually displays analysis results and sustainable options, allowing users to easily check them.

[1137] This system inputs behavioral and emotional data related to users' daily activities and business activities, analyzes the data on a server to evaluate the environmental impact, and uses an emotion engine to recognize the user's emotional state and provide appropriate sustainable options. This system aims to contribute to the realization of a sustainable society.

[1138] This system has a terminal where users input behavioral data and emotional data, a server that receives and analyzes this data, a means for presenting sustainable options based on the analysis results, and a means for the emotion engine to analyze the user's emotions and adjust the content of the suggestions.

[1139] Data entry and submission

[1140] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). Users also provide their own emotional data through facial recognition and voiceprint analysis via the app's emotion recognition feature. This collected data is stored on the device and converted to JSON format.

[1141] The terminal sends this converted data to the server as an HTTP request, which the server receives and proceeds to the next analysis step.

[1142] Data analysis

[1143] The server receives the JSON data sent from the device and first performs an error check. Behavioral data that passes the error check is transferred to the AI ​​engine, and emotional data is transferred to the emotion engine. The AI ​​engine calculates environmental indicators such as CO2 emissions, water consumption, and waste volume based on the behavioral data. The emotion engine analyzes the emotional data and evaluates the user's emotional state (e.g., "stress state").

[1144] Generate results and suggestions

[1145] The server integrates the analysis results returned by the AI ​​engine and emotion engine to generate specific suggestions for improving the environment. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. Furthermore, based on the results of the emotion engine, it makes simple and easy-to-implement suggestions to users who are under stress.

[1146] Data visualization and presentation

[1147] The server converts the analysis results into graphs and charts for easy visual understanding. Users can view these visualized data on a smartphone app or web portal dashboard. The system also generates detailed environmental impact reports for businesses and provides analysis data via an API for educators and policymakers.

[1148] User practices

[1149] Users can check the analysis results and environmental improvement suggestions provided on the dashboard and take action based on them. For example, at home, they can purchase and install LED lighting or choose energy-efficient home appliances. At business, they can improve energy efficiency or implement waste reduction measures. In this way, users can reduce their environmental impact through concrete actions.

[1150] Specific examples

[1151] For example, suppose a user uses 200 kWh of electricity per month. The user enters this data into a smartphone app and presses the send button. The device converts the data into JSON format and uploads it to the server. At the same time, the emotion recognition function analyzes the user's face and voice, collects emotional data on "stress state," and sends it to the server.

[1152] After receiving this data, the server performs an error check. After the error check, the behavioral data is transferred to the AI ​​engine, and the emotional data is transferred to the emotion engine. The AI ​​engine returns the result that "200 kWh of electricity usage per month will result in 100 kg of CO2 emissions," and the emotion engine returns the analysis result that "the user's emotional state is stressed."

[1153] Based on these analysis results, the server generates specific environmental improvement suggestions, such as "using LED lighting" or "introducing energy-saving home appliances," and adjusts the suggestions to be simple and easy to implement, taking into account the user's stress level. These suggestions are displayed on the user's dashboard with graphs and charts.

[1154] An example of a prompt is, "If a user uses 200 kWh of electricity per month and is in a stressed state, please explain in detail how the program will display the analysis results and suggestions on a dashboard along with graphs." In this way, users can take specific actions based on the analysis results and suggestions to reduce their environmental impact.

[1155] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1156] Step 1: Data entry

[1157] Users use a smartphone app or web portal to input data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste generation, and commuting method (private car, public transportation). This data is entered into the device. The input data is collected based on the user's behavior.

[1158] Step 2: Collecting emotion data

[1159] Users use the smartphone app's emotion recognition function to provide emotional data through facial recognition and voiceprint analysis. Specifically, the app uses the camera and microphone to analyze the user's facial expressions and voice, detecting emotions such as "stress" and "happiness." This emotional data is also input into the device.

[1160] Step 3: Transform the data

[1161] The device converts the input behavioral and emotional data into JSON format. For example, it converts data on 200 kWh of electricity usage and stress level into JSON format and creates a single data package. This conversion allows the data to be sent to the server in a standard format.

[1162] Step 4: Sending data

[1163] The device generates an HTTP request with the data converted into JSON format and sends it to the server. The data sent includes behavioral data and emotion data. Once the data is sent from the device to the server, the next analysis step begins.

[1164] Step 5: Error checking

[1165] The server parses the received JSON data and performs an error check. This checks whether there are any errors in the data format or content. For example, it verifies whether the amount of electricity used is a number, and whether the emotion data is in the correct format. Once the error check is complete, the data can proceed to analysis.

[1166] Step 6: Analyze behavioral data

[1167] The server transfers the behavioral data that has passed the error check to the AI ​​engine. The AI ​​engine evaluates the environmental impact based on the behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity consumption. For example, the evaluation result may be, "100 kg of CO2 will be emitted from 200 kWh of electricity consumption per month."

[1168] Step 7: Analyze the sentiment data

[1169] The server transfers the emotion data to the emotion engine. The emotion engine analyzes the emotion data and evaluates the user's emotional state. For example, the result may be "The user's emotional state is stressed." Based on this analysis, the suggestions are adjusted according to the user's emotional state.

[1170] Step 8: Generate results and recommendations

[1171] The server combines the analysis results of the AI ​​engine and the emotion engine to generate specific suggestions for improving the environment. For example, they might suggest switching to LED lighting to reduce power consumption or recommend installing energy-efficient home appliances. Based on the results of the emotion engine, the server also provides simple, easy-to-implement suggestions to users who are under stress.

[1172] Step 9: Visualize the data

[1173] The server generates graphs and charts to make the analysis results visually easy to understand. For example, CO2 emissions are displayed in a pie chart, and specific values ​​are displayed in a bar graph. This visualization allows users to intuitively understand their own environmental impact.

[1174] Step 10: Provide data

[1175] The server then sends the generated graphs and charts to the device, where they are displayed on a dashboard in a web portal or smartphone app. Users can view the analysis results and sustainable choices on the dashboard. The system also provides detailed environmental impact reports for businesses and analytical data via an API for educators and policymakers.

[1176] Step 11: User Practice

[1177] Users can review the analysis results and sustainable choices provided by the dashboard and use them to improve their own lives and business activities. For example, households can purchase LED lighting and choose energy-efficient home appliances, while businesses can implement energy efficiency improvements and waste reduction measures. This allows users to reduce their environmental impact through concrete actions.

[1178] Through these specific processing steps, users and businesses can make environmentally sustainable choices and contribute to the realization of a sustainable society.

[1179] (Application example 2)

[1180] 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."

[1181] This invention relates to a system that evaluates the environmental impact of users' daily activities and corporate activities and provides sustainable options. However, conventional systems do not consider the user's emotional state when proposing sustainable options, making it difficult to translate these options into concrete action. Furthermore, there has been a lack of proposals for simultaneously reducing environmental impact and improving customer satisfaction in certain industries, particularly brick-and-mortar stores.

[1182] 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 the user to input behavioral data, means for the terminal to transmit the behavioral data to the server, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, means for the server to provide the options to the user, means for an emotion recognition engine to analyze the user's emotion data, and means for the server to suggest options adapted to the emotions based on the analysis results. This makes it possible to provide specific, emotion-sensitive sustainable options for users' daily behavior and corporate activities, and in particular, in physical stores, it becomes possible to monitor energy consumption, waste volume, and customer satisfaction and generate management proposals based on the results.

[1183] "User" refers to an individual or company that uses the system.

[1184] "Behavioral data" refers to information such as electricity consumption, waste volume, and transportation methods related to users' daily behavior and corporate activities.

[1185] "Terminal" refers to a device that allows a user to input behavioral data and transmit it to a server. Examples include smartphones and computers.

[1186] "Server" refers to a computer system that receives behavioral data sent from a terminal, analyzes it, and provides the results to the user.

[1187] "Analysis" refers to the process by which the server processes the received behavioral data and evaluates the environmental impact.

[1188] "Environmental impact" refers to the impact that a user's actions have on the environment, and is evaluated using indicators such as CO2 emissions and waste volume.

[1189] "Sustainable options" refers to specific suggestions that encourage users to adopt environmentally friendly actions and choices, such as using energy-efficient appliances or taking public transport.

[1190] An "emotion recognition engine" refers to software technology that analyzes a user's emotional state and uses the results to make appropriate suggestions.

[1191] "Emotional data" refers to data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, etc.

[1192] "Monitoring" refers to the process of regularly observing and recording specific indicators (e.g., energy use, waste volume, customer satisfaction).

[1193] "Operation proposals" refer to specific improvement measures and guidelines for action that the server provides to physical store operators based on the analysis results.

[1194] "Customer satisfaction" refers to an indicator of the level of satisfaction that customers who visit a physical store feel toward the service.

[1195] "Visualization" refers to the process of visually displaying analysis results in graphs, charts, etc., and presenting them to users in an easy-to-understand manner.

[1196] The present invention relates to a system that inputs behavioral data and emotional data related to users' daily activities and business activities, analyzes the data on a server, evaluates the environmental impact, and provides sustainable options. Specifically, the system includes a terminal, a server, an emotion recognition engine, and a data visualization means.

[1197] Data entry and submission

[1198] Users input behavioral data using a device (e.g., a smartphone). This behavioral data includes energy consumption, waste volume, transportation methods, etc. This data is converted into a standard data format (e.g., JSON format). In addition, emotion data of the user is collected using emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app. This data is sent to the server via an HTTP request.

[1199] Data reception and analysis

[1200] The server receives the data sent from the device and first checks for errors. After checking, the behavioral data is transferred to an AI engine to evaluate the environmental impact (e.g., CO2 emissions). The emotion data is analyzed by an emotion recognition engine to evaluate the user's emotional state. This provides information such as whether the user is stressed or relaxed.

[1201] Generate results and suggestions

[1202] The server generates sustainable options based on the analysis results obtained from the AI ​​engine and emotion recognition engine. For example, if energy usage is high, it will suggest installing energy-efficient appliances or switching to LED lighting. It also adjusts the content and presentation of the suggestions based on emotion data. For users in a stressed state, it provides simple, easy-to-implement suggestions.

[1203] Data visualization and presentation

[1204] The server visually displays the analysis results in graphs and charts, allowing users to intuitively understand their own environmental impact. For example, it may provide information such as the CO2 emissions corresponding to 200 kWh of energy consumption (e.g., 100 kg CO2 / month). This data is displayed on the device's dashboard. This display is generated using the matplotlib library.

[1205] User practices

[1206] Users can review the sustainable options presented and take specific actions. Store managers can use the information provided by the app to implement specific measures to reduce energy consumption and waste. For example, introducing LED lighting can reduce environmental impact.

[1207] Prompt Sentence Examples

[1208] "Enter your store's monthly energy use and waste volume, as well as customer reviews. The results assess your environmental impact and provide sustainable options."

[1209] As described above, the system of the present invention can promote environmentally conscious behavior by integrating and analyzing behavioral data and emotional data, and providing optimal sustainable options to users.

[1210] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1211] Step 1:

[1212] The user inputs behavioral data using a device. The user inputs data related to daily activities and store operations, such as energy consumption, waste volume, and transportation methods, into the application. At this time, the emotion recognition function analyzes the user's facial expressions and voice to collect emotional data. The input data and emotional data are converted into JSON format.

[1213] Step 2:

[1214] The device sends input data and emotion data to the server. The device generates an HTTP request and uploads the input behavioral data and emotion data to the server. The server receives the data and performs error checks to ensure it is in the correct format. Here, the input is behavioral data and emotion data, and the output is error-checked data.

[1215] Step 3:

[1216] The server analyzes the behavioral data. After error checking, the server transfers the behavioral data to an AI engine, which calculates environmental impacts such as CO2 emissions, energy consumption, and waste volume. The input is the error-checked behavioral data, and the output is the analyzed environmental impact data.

[1217] Step 4:

[1218] The server analyzes the emotion data. The server sends the error-checked emotion data to an emotion recognition engine to evaluate the user's current emotional state, where the input is the error-checked emotion data and the output is the user's emotional state data.

[1219] Step 5:

[1220] The server generates sustainable options based on the analysis results. The server combines the analysis results obtained from the AI ​​engine and emotion recognition engine to generate specific environmental improvement measures. For example, for users with high energy usage, it suggests introducing energy-saving home appliances and switching to LED lighting. The input is environmental impact data and emotional state data, and the output is sustainable options.

[1221] Step 6:

[1222] The server visualizes the proposals. The server displays the sustainable options generated in the previous step in a visual format such as graphs and charts. This makes it easier for users to intuitively understand their own environmental impacts. The input is the sustainable options, and the output is the visualized data.

[1223] Step 7:

[1224] The server provides the user with the proposed solutions. The server sends the visualized analysis results and sustainable options to the user's device. The user can then review the results in the application and take specific actions. The input is the visualized data, and the output is a notification to the user.

[1225] Through these processing steps, users can obtain specific, emotionally sensitive, and sustainable choices based on their own behavioral data. This system promotes environmentally friendly behavior and supports users' sustainable behavior.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] [Fourth embodiment]

[1230] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1231] 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.

[1232] 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).

[1233] 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.

[1234] 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.

[1235] 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).

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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.

[1242] 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."

[1243] This invention relates to a system that inputs data on users' daily activities and corporate activities, analyzes the data on a server, evaluates the environmental impact, and generates and provides sustainable options. This system allows individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[1244] Program Overview

[1245] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results.

[1246] Data entry and submission

[1247] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is then converted into a standard data format (e.g., JSON) by the device.

[1248] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[1249] Data analysis

[1250] The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[1251] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity usage. The analysis results are sent back to the server, which then moves on to the next step.

[1252] Generate results and suggestions

[1253] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, it suggests switching to LED lighting and choosing more energy-efficient home appliances for households with high electricity consumption. For businesses, it suggests specific improvement measures to increase the energy efficiency of their manufacturing processes.

[1254] Data visualization and presentation

[1255] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[1256] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[1257] User practices

[1258] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[1259] Specific examples

[1260] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[1261] Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis results in "100 kg CO2 / month." Based on this, the server generates specific recommendations, such as "using LED lighting" and "introducing energy-efficient home appliances." These recommendations are displayed on the user's dashboard with graphs and charts.

[1262] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[1263] As described above, this system contributes to solving environmental problems by helping users and businesses make sustainable choices.

[1264] The processing flow will be explained below.

[1265] Step 1:

[1266] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user might enter "200 kWh per month" in the "Electricity Usage" field.

[1267] Step 2:

[1268] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[1269] json

[1270] {

[1271] "userId": "12345",

[1272] "data": {

[1273] "electricity": "200kWh"

[1274] }

[1275] }

[1276] Step 3:

[1277] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[1278] Step 4:

[1279] The server receives the HTTP request and parses the JSON data sent. First, it checks whether the data format is correct or not.

[1280] Step 5:

[1281] The server transfers data that passes the error check to the AI ​​engine, which then inputs the data into the AI ​​model running on the server and begins analysis.

[1282] Step 6:

[1283] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[1284] Step 7:

[1285] The AI ​​engine sends the analysis results back to the server, which receives them and proceeds to the next step.

[1286] Step 8:

[1287] Based on the analysis results received by the server, it generates specific environmental improvement proposals for the user, such as a list of proposals such as "using LED lighting" or "introducing energy-efficient home appliances."

[1288] Step 9:

[1289] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[1290] Step 10:

[1291] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[1292] Step 11:

[1293] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[1294] Step 12:

[1295] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[1296] Through these steps, the ImpactScope system assesses environmental impacts, generates sustainable options, and supports users and companies in making concrete improvements to their behavior.

[1297] Example 1

[1298] 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."

[1299] In modern society, it is important for individuals and businesses to choose appropriate actions to reduce their environmental impact. However, most people do not understand the extent to which their daily actions and corporate activities affect the environment. There is also a lack of information and advice on how to find environmentally friendly options. This makes it difficult to achieve a sustainable society.

[1300] 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.

[1301] In this invention, the server includes means for a user to input behavioral data, means for a terminal to transmit the behavioral data to the server, means for the server to perform error checking on the behavioral data, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, and means for the server to generate a diagram for visualizing the evaluation results and sustainable options and provide it to the user. This enables individuals and companies to understand their own environmental impacts and reduce environmental loads through specific sustainable options.

[1302] "User" refers to an individual or company that uses the system to input data about their daily activities or business activities.

[1303] "Behavioral data" refers to information about a user's daily behavior and business activities, including electricity consumption, waste output, and commuting methods.

[1304] A "terminal" is a device used by a user to input behavioral data and transmit the data to a server, and includes a smartphone or computer.

[1305] "Server" refers to the central computer system that receives, analyzes, and generates sustainable options based on behavioral data sent by users.

[1306] "Error checking" refers to the process by which the server examines the format and content of the behavioral data it receives to detect errors.

[1307] "Analysis" refers to the process by which the server uses an AI engine to evaluate behavioral data and generate indicators related to environmental impact.

[1308] "Environmental impact" refers to the impact that a user's actions have on the environment, and specifically includes CO2 emissions, water consumption, waste volume, etc.

[1309] "Sustainable options" refers to specific actions and options for reducing environmental impact that are generated by the server based on the analysis results.

[1310] "Graphs" refers to graphs and charts that visually display analysis results and sustainable options.

[1311] A "report" refers to a document that compiles detailed analysis results and recommendations generated for a company.

[1312] A "generative AI model" is a type of artificial intelligence technology used for analysis, and refers to an algorithm that evaluates environmental impacts based on behavioral data.

[1313] The present invention is a system in which a user inputs behavioral data, which is analyzed by a server to evaluate the environmental impact, and which provides sustainable options. Specific embodiments of the system are described below.

[1314] System Overview

[1315] This system mainly consists of a terminal where users input behavioral data, a server that receives and analyzes the input data, and a means for presenting sustainable options based on the analysis results.

[1316] Data Entry

[1317] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste output, and commuting method (private car, public transport). This data is then converted into a standard data format (e.g., JSON) by the device.

[1318] Data transmission

[1319] The terminal sends the generated data to the server as an HTTP request. At this time, the data is sent using a secure protocol (e.g., HTTPS) to maintain the integrity and security of the data.

[1320] Data reception and error checking

[1321] The server receives the data sent from the device and checks its format and content for errors. Specifically, it checks whether the data is in JSON format and whether all required fields are included. If the data is invalid, it returns an error message to the user.

[1322] Data analysis

[1323] Once the check is complete, the data is transferred to an AI engine on the server. The AI ​​engine evaluates the environmental impact based on the behavioral data, calculating CO2 emissions from electricity consumption, for example. The analysis results are then sent back to the server.

[1324] Generating sustainable options

[1325] Based on the analysis results returned by the AI ​​engine, the server generates optimal sustainable options for users and businesses. For example, if electricity usage is high, it will suggest the introduction of LED lighting and energy-efficient home appliances. These suggestions are presented as specific action items.

[1326] Data visualization and presentation

[1327] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to understand their environmental impact at a glance. The generated graphs and charts are displayed on a dashboard in a web portal or smartphone app. It also generates detailed environmental impact reports for businesses and provides data to educators and policymakers via an API.

[1328] User practices

[1329] Users can review the analysis results and sustainable choices provided and use them to improve their own lives and business activities, for example by purchasing LED lighting and choosing energy-efficient appliances at home, or by improving energy efficiency and reducing waste at work.

[1330] Specific examples

[1331] As a concrete example, let's assume that a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks the format and content for errors. Once the check is complete, the server transfers the data to an AI engine, which analyzes the CO2 emissions. The analysis result is calculated as "100 kg CO2 / month." Based on this, the server makes suggestions such as "using LED lighting" and "introducing energy-saving home appliances." These suggestions are displayed on the user's dashboard with graphs and charts. The user checks them and actually purchases and installs LED lighting in their home.

[1332] Prompt Sentence Examples

[1333] "Enter your monthly household electricity usage of 200 kWh to view your environmental impact analysis and sustainable options."

[1334] keyword

[1335] Generative AI model, prompt sentence

[1336] As described above, this system allows individuals and businesses to make environmentally friendly choices and contribute to the realization of a sustainable society.

[1337] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1338] Step 1: Data entry

[1339] Users: Use a smartphone app or web portal to enter data about their daily activities and business activities.

[1340] Specific operation: The user opens the app, enters the amount of electricity usage as "200kWh per month," and clicks the submit button.

[1341] Input: Data related to users' daily activities and business activities (e.g., electricity usage)

[1342] Output: Behavioral data in standard data format (JSON format)

[1343] Step 2: Data conversion

[1344] Terminal: Receives data entered by the user and converts it into a standard data format (e.g., JSON format).

[1345] Specific operation: The terminal receives the input data "Power usage: 200kWh" and converts it into JSON format "{"Power usage": "200kWh"}".

[1346] Input: Raw data entered by the user

[1347] Output: Behavioral data in JSON format

[1348] Step 3: Send data

[1349] Terminal: Sends the generated data to the server as an HTTP request.

[1350] Specific operation: The device uses the HTTPS protocol to send the data "{"power usage": "200kWh"}" to the server.

[1351] Input: JSON formatted behavior data

[1352] Output: HTTP request to the server

[1353] Step 4: Receiving Data

[1354] Server: Receives data sent from the terminal.

[1355] Specific operation: The server listens for HTTP requests on the receiving port and receives the data that arrives.

[1356] Input: HTTP request sent from the terminal

[1357] Output: Received behavior data in JSON format

[1358] Step 5: Error checking

[1359] Server: Checks the format and content of the received data for errors.

[1360] What happens: The server uses a JSON schema to check that "{"Power Usage": "200kWh"}" is in the correct format and contains all required fields.

[1361] Input: JSON formatted behavior data

[1362] Output: Normal data or error message

[1363] Step 6: Data analysis

[1364] Server: Transfers the checked data to the AI ​​engine and evaluates the environmental impact.

[1365] Specific operation: The server calls the AI ​​engine's analysis API and inputs behavioral data. The AI ​​engine analyzes the behavioral data and calculates CO2 emissions.

[1366] Input: Normal data

[1367] Output: Analysis results of environmental impact (e.g., "100 kg CO2 / month")

[1368] Step 7: Generate sustainable options

[1369] Server: Based on the analysis results, it generates optimal sustainable options for users and companies.

[1370] Specific actions: The server evaluates the analysis results and generates specific action items such as "use LED lighting" or "introduce energy-saving home appliances."

[1371] Input: Analysis results

[1372] Output: Sustainable Choice

[1373] Step 8: Visualize the data

[1374] Server: Generates graphs and charts to visually display analysis results and sustainable options.

[1375] Specific operation: The server creates graphs and charts based on the analysis results and inserts them into the user's dashboard in HTML format.

[1376] Input: Analysis results and sustainable options

[1377] Output: Visualized data (graphs and charts)

[1378] Step 9: Provide data

[1379] Server: Generates environmental impact reports for companies and provides data via APIs as needed.

[1380] Specific operation: The server compiles the analysis data and proposals into a PDF report and provides a download link to companies. It also prepares an API to provide the analysis data to educators and policymakers.

[1381] Input: Analytical data and sustainable options

[1382] Output: Environmental Impact Report and API endpoints

[1383] Step 10: User Practice

[1384] Users: Review the analysis results and sustainable options provided to improve their own lives and business activities.

[1385] Specific actions: A user views the dashboard, purchases and installs LED lighting in their home, and a business implements suggested energy efficiency improvements.

[1386] Input: Analysis results and sustainable options

[1387] Output: Reduction of environmental impact (results of concrete actions)

[1388] Through these steps, the system provides concrete support for users and businesses to make sustainable choices and reduce their environmental impact.

[1389] (Application example 1)

[1390] 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."

[1391] To achieve a sustainable society, individuals and businesses are required to make environmentally friendly choices. However, currently, there is a lack of systems to assess environmental impacts and provide sustainable options. Another problem is that there is no established method to collect real-time data on energy consumption and resource use in industrial environments and provide immediate improvement recommendations. This makes it difficult to achieve efficient energy use and reduce waste.

[1392] 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.

[1393] In this invention, the server includes a means for workers to input data on energy consumption and resource utilization in industrial environments in real time through smart glasses, a means for the smart glasses to visually display assessment results and sustainable proposals in real time, and a means for the server to perform analysis using a generative AI model based on the environmental data provided through the smart glasses, thereby enabling workers to understand environmental impacts in real time and immediately adopt sustainable options.

[1394] "User" refers to an individual or company that uses the system.

[1395] "Behavioral data" refers to information about users' daily behavior and business activities, and specifically includes energy consumption and resource usage.

[1396] "Terminal" refers to an electronic device that allows a user to input behavioral data and transmit it to a server.

[1397] "Server" refers to a central computer system that receives and analyzes behavioral data and generates and provides sustainable options based on the evaluation results.

[1398] "Smart glasses" refer to wearable devices worn by workers that allow them to input and visualize data in real time.

[1399] "Energy consumption" refers to the consumption of electricity and fuel used in industrial environments.

[1400] "Resource use" refers to the amount of raw materials and resources used in an industrial environment.

[1401] "Real-time" refers to data being entered, analyzed, and displayed immediately, without delay.

[1402] "Evaluation results" refer to the indicators and numerical values ​​of environmental impact obtained by the server through analysis of behavioral data.

[1403] "Sustainable proposals" refer to specific actions and improvement plans to reduce environmental impact based on the analysis results.

[1404] "Means for visual display" refers to a display function that allows analysis results and proposals to be presented to workers in an easy-to-understand manner.

[1405] "Generative AI models" refer to artificial intelligence algorithms and models used to analyze behavioral data and assess environmental impacts.

[1406] This invention is a system that uses smart glasses to collect data on energy consumption and resource usage in industrial environments in real time, analyzes the data, and evaluates the environmental impact and provides sustainable solutions. This system enables efficient energy use and waste reduction, improving sustainability in industrial environments.

[1407] Specifically, the system consists of the following components:

[1408] 1. Data input method: Workers wear smart glasses and input data on energy consumption and resource utilization in industrial environments in real time.

[1409] 2. Data transmission method: The smart glasses convert the collected data into a standard data format and send it to the server via an HTTP request.

[1410] 3. Data analysis method: The server performs an error check on the received behavioral data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and begins analysis.

[1411] 4. Method for generating evaluation results: The AI ​​engine evaluates the environmental impact based on behavioral data, specifically calculating indicators such as CO2 emissions and waste volume from energy consumption.

[1412] 5. Proposal generation method: The server generates sustainable options based on the analysis results. For example, for a factory with high energy consumption, it proposes switching to LED lighting and other measures to improve energy efficiency.

[1413] 6. Result display means: The server displays the analysis results and sustainable options on the smart glasses display in real time, allowing workers to immediately check the evaluation results and specific suggestions.

[1414] Hardware and Software

[1415] This system uses the following hardware and software:

[1416] Hardware: Smart glasses (e.g., SmartGlasses), servers

[1417] Software: AI engine for data analysis, HTTP communication protocol, data visualization tool

[1418] Specific examples

[1419] For example, if a part of a factory uses 500 kWh of electricity per month and generates 20 kg of waste, workers can input this data in real time through the smart glasses. The AI ​​engine analyzes the data and displays the result: "CO2 emissions: 250 kg / month." Based on this result, suggestions such as "switching to LED lighting" and "using recyclable materials" are made. These suggestions are displayed in real time on the smart glasses' display, allowing workers to immediately implement improvement measures.

[1420] Prompt Sentence Examples

[1421] Use the data below to conduct an environmental impact assessment of your factory and propose sustainable options.

[1422] data:

[1423] Energy usage: 500kWh

[1424] Waste generated: 20kg

[1425] As described above, the present invention allows workers to understand the environmental impact within a factory and receive sustainable improvement proposals in real time, thereby contributing to the realization of a sustainable society.

[1426] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1427] Step 1:

[1428] Workers wear smart glasses that input real-time data on energy consumption and resource use within industrial environments.

[1429] Input: Energy consumption and resource utilization data within an industrial environment.

[1430] Output: Data is stored in the smart glasses.

[1431] Specific operation: Workers operate the interface of the smart glasses to input data such as power consumption and waste generation.

[1432] Step 2:

[1433] The device (smart glasses) converts the collected data into a standard data format and sends it to the server via an HTTP request.

[1434] Input: Data stored on smart glasses.

[1435] Output: The data sent to the server.

[1436] Specific operation: The smart glasses software converts the input data into JSON format, generates an HTTP POST request, and sends it to the server.

[1437] Step 3:

[1438] The server performs an error check on the received behavioral data to ensure that there are no errors in the data format or content.

[1439] Input: Behavioral data sent to the server.

[1440] Output: Data verified to be error-free.

[1441] Specific operation: A program on the server runs routines that validate data format, numeric range, etc., to check for inconsistencies.

[1442] Step 4:

[1443] The server transfers the data to an AI engine, which analyzes environmental impacts based on behavioral data.

[1444] Input: Behavioral data that has been verified to be error-free.

[1445] Output: An indicator of the environmental impact (e.g. CO2 emissions) as a result of the analysis.

[1446] Specific operation: The server inputs behavioral data into an AI engine and runs an algorithm to calculate environmental impact indicators such as CO2 emissions and waste generation.

[1447] Step 5:

[1448] The server generates sustainable options based on the analysis results.

[1449] Input: Analysis results by the AI ​​engine.

[1450] Output: Proposal of sustainable options.

[1451] Specific actions: Based on the analysis results, the server executes logic to generate specific suggestions for improving energy efficiency (e.g., switching to LED lighting, using recyclable materials).

[1452] Step 6:

[1453] The server displays the assessment results and sustainable options in real time on the smart glasses' display.

[1454] Input: Proposal of sustainable options.

[1455] Output: Evaluation results and suggestions displayed on the smart glasses display.

[1456] How it works: The server sends the evaluation results and suggestions to the smart glasses, which use a rendering engine to visually display them on the display. This information is updated in real time so that the worker can check it at any time.

[1457] Through these processing steps, workers can understand the environmental impact within the factory in real time and immediately incorporate sustainable options. As a specific example, in a factory that uses 500 kWh of electricity per month and generates 20 kg of waste, CO2 emissions are analyzed and suggestions such as switching to LED lighting are displayed in real time.

[1458] 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.

[1459] This invention relates to a system that inputs behavioral data related to users' daily activities and corporate activities, analyzes it on a server, evaluates the environmental impact, and generates and provides sustainable options. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide appropriate sustainable options accordingly. This system enables individuals and companies to make environmentally friendly choices, contributing to the realization of a sustainable society.

[1460] Program Overview

[1461] The core of the system is a device where users input their behavioral data, a server that receives and analyzes this data, and a means of presenting sustainable options based on the analysis results. In addition, an emotion engine analyzes the user's emotions and adjusts the suggestions accordingly.

[1462] Data entry and submission

[1463] Users enter data about their daily activities and business activities using a smartphone app or web portal. Examples include household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). This data is converted into a standard data format (e.g., JSON format) by the device. In addition, the user's emotions can be collected, for example, through emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app.

[1464] The terminal generates an HTTP request to transmit this data to the server and uploads the data to the server.

[1465] Data analysis

[1466] The server performs an error check on the received behavioral and emotional data to ensure there are no errors in the data format or content. Once this initial check is complete, the server transfers the data to the AI ​​engine and emotion engine to begin analysis.

[1467] The AI ​​engine evaluates environmental impact based on behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from power consumption. The emotion engine analyzes the emotion data and evaluates the user's current emotional state. The analysis results are sent back to the server, which then moves on to the next step.

[1468] Generate results and suggestions

[1469] The server generates specific environmental improvement proposals for users based on the analysis results returned by the AI ​​engine and emotion engine. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. The presentation and content of the proposals are also adjusted based on the emotion data from the emotion engine. Specific improvement measures for increasing the energy efficiency of manufacturing processes are also presented to companies.

[1470] Data visualization and presentation

[1471] The server generates graphs and charts to visualize the analysis results, allowing users and businesses to see their environmental impact at a glance. The generated graphs and charts are displayed on the dashboard of the web portal or smartphone app.

[1472] The server then generates reports containing analysis results and recommendations and provides them to businesses. Analysis data can also be provided to educators and policymakers via API.

[1473] User practices

[1474] Users review the analysis results and sustainable options provided, and use them to improve their own lives and business activities. For example, at home, they might purchase LED lighting and choose energy-efficient home appliances. At work, they might improve energy efficiency and reduce waste. By taking into account the emotion analysis results from the emotion engine, users can receive suggestions that match their own psychological state, reducing resistance to putting ideas into practice and making it easier for them to take sustainable actions.

[1475] Specific examples

[1476] Let's say a user uses 200 kWh of electricity per month. When the user enters this data into the app and presses the send button, the device converts the data into JSON format and uploads it to the server. The server receives the data and checks for format and content errors.

[1477] At the same time, while the user is using the app, the emotion recognition function analyzes the user's face and voice to collect emotional data such as "stress level," which is also sent to the server.

[1478] Once the error check is complete, the server transfers the behavioral data to the AI ​​engine, which analyzes the CO2 emissions. The emotion engine also analyzes the emotional data and evaluates the user's emotional state. For example, the analysis result "100kg CO2 / month" corresponding to 200kWh of electricity usage and the emotion analysis result "stress state" are returned.

[1479] Based on these analysis results, the server generates environmental improvement suggestions such as "using LED lighting" or "introducing energy-saving home appliances," and presents the suggestions in a concise and easy-to-implement format, taking into account the user's stress level. These suggestions are then displayed on the user's dashboard with graphs and charts.

[1480] Users can then check this information, purchase LED lighting, and install it in their homes, thereby reducing their environmental impact through concrete actions.

[1481] As described above, this system contributes to solving environmental problems by allowing users and businesses to make sustainable choices, and by adjusting the content of suggestions using an emotion engine, it reduces the psychological burden on users and promotes sustainable behavior.

[1482] The processing flow will be explained below.

[1483] Step 1:

[1484] A user logs into a smartphone app or web portal and enters data about their daily activities or business activities. For example, a user enters "200 kWh per month" in the "Electricity Usage" field.

[1485] Step 2:

[1486] The terminal converts the user input data into a standard data format (e.g., JSON format). Specifically, the following JSON data is generated.

[1487] json

[1488] {

[1489] "userId": "12345",

[1490] "data": {

[1491] "electricity": "200kWh"

[1492] }

[1493] }

[1494] Step 3:

[1495] The device sends the generated JSON data to the server as an HTTP request, making a POST request to the server's API endpoint.

[1496] Step 4:

[1497] Collecting user emotional data. For example, an emotion recognition function embedded in a smartphone app uses a camera and microphone to analyze the user's facial expressions and voice to generate emotional data.

[1498] Step 5:

[1499] The device converts the emotion data into JSON format and sends it to the server. The emotion data will be in the following format:

[1500] json

[1501] {

[1502] "userId": "12345",

[1503] "emotion": "stressed"

[1504] }

[1505] Step 6:

[1506] The server receives the HTTP request and analyzes the behavioral and emotional data sent. First, it checks whether the data format is correct.

[1507] Step 7:

[1508] The server transfers the data that passes the error check to the AI ​​engine and emotion engine. Specifically, behavioral data is input into the AI ​​model, and emotion data is input into the emotion analysis model.

[1509] Step 8:

[1510] The AI ​​engine evaluates the environmental impact based on electricity usage data. For example, it calculates the CO2 emissions corresponding to 200 kWh of electricity usage (e.g., 100 kg CO2 / month).

[1511] Step 9:

[1512] The emotion engine analyzes the emotion data and evaluates the user's current emotional state, for example recognizing the state "stressed."

[1513] Step 10:

[1514] The AI ​​engine and emotion engine send their analysis results back to the server, which then generates specific suggestions for improving the user's environment based on these results.

[1515] Step 11:

[1516] The server generates sustainable options based on the evaluation results and adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it will present suggestions such as "using LED lighting" or "introducing energy-saving appliances" in a concise and easy-to-implement format.

[1517] Step 12:

[1518] The server visualizes the generated proposals in graphs and charts. The server uses visualization tools to convert the analysis results and proposals into graphs and charts.

[1519] Step 13:

[1520] The server sends the visualized data to the device for display on the user's dashboard, and then calls an API that reflects the data in the UI of the web portal or smartphone app.

[1521] Step 14:

[1522] Users access the smartphone app or web portal, check the analysis results and recommendations presented on the dashboard, and take specific actions to improve the environment.

[1523] Step 15:

[1524] The server generates environmental impact reports for businesses and provides analytical data for educators and policymakers through an API, which makes the data available in a format that can be used by third parties.

[1525] Example 2

[1526] 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."

[1527] In modern society, the impact of everyday user and corporate activities on the environment has become a major issue. However, making sustainable choices requires specific assessments of environmental impacts and appropriate guidelines for action based on those assessments. Furthermore, users' emotional states can be important factors in taking sustainable actions on an ongoing basis, but current systems lack the means to comprehensively assess and adjust these. Therefore, there is a need to provide an environment that makes it easier for users to make sustainable choices based on appropriate information.

[1528] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing behavioral data and emotional data to evaluate environmental influences and the emotional state, means for generating sustainable options based on the evaluation results and adjusting the options according to the emotional state, and means for providing the adjusted options to the user. This makes it possible for the user to understand environmental influences, receive sustainable options that match their own emotional state, and easily implement them.

[1529] "Behavioral data" refers to information about users' daily activities and corporate activities, and refers to specific data such as electricity consumption, waste output, and transportation usage.

[1530] "Emotional data" refers to information that indicates the user's emotional state, such as "stress level" and "happiness" collected through facial recognition and voiceprint analysis.

[1531] A "terminal" is a device or software, such as a smartphone app or web portal, that allows a user to input data and send it to a server.

[1532] "Server" means a digital processing system for receiving and analyzing behavioral and emotional data, generating and providing sustainable options to users.

[1533] An "AI engine" is a computational system that includes algorithms and programs for analyzing behavioral data and assessing environmental impacts.

[1534] An "emotion engine" is a computational system that includes algorithms and programs for analyzing emotion data and assessing a user's emotional state.

[1535] "Sustainable options" are specific recommended actions and proposals for reducing environmental impact and promoting the realization of a sustainable society.

[1536] "Adjustment" refers to changing the content and presentation of sustainable options depending on the user's emotional state.

[1537] "Visualization" refers to converting analytical results into a visual format such as a graph or chart in order to display them in an easy-to-understand manner.

[1538] A "dashboard" is an interface that visually displays analysis results and sustainable options, allowing users to easily check them.

[1539] This system inputs behavioral and emotional data related to users' daily activities and business activities, analyzes the data on a server to evaluate the environmental impact, and uses an emotion engine to recognize the user's emotional state and provide appropriate sustainable options. This system aims to contribute to the realization of a sustainable society.

[1540] This system has a terminal where users input behavioral data and emotional data, a server that receives and analyzes this data, a means for presenting sustainable options based on the analysis results, and a means for the emotion engine to analyze the user's emotions and adjust the content of the suggestions.

[1541] Data entry and submission

[1542] Users use a smartphone app or web portal to enter data about their daily activities and business activities, such as household electricity consumption (e.g., 200 kWh per month), daily waste output, and the mode of transportation used for commuting (e.g., personal car or public transportation). Users also provide their own emotional data through facial recognition and voiceprint analysis via the app's emotion recognition feature. This collected data is stored on the device and converted to JSON format.

[1543] The terminal sends this converted data to the server as an HTTP request, which the server receives and proceeds to the next analysis step.

[1544] Data analysis

[1545] The server receives the JSON data sent from the device and first performs an error check. Behavioral data that passes the error check is transferred to the AI ​​engine, and emotional data is transferred to the emotion engine. The AI ​​engine calculates environmental indicators such as CO2 emissions, water consumption, and waste volume based on the behavioral data. The emotion engine analyzes the emotional data and evaluates the user's emotional state (e.g., "stress state").

[1546] Generate results and suggestions

[1547] The server integrates the analysis results returned by the AI ​​engine and emotion engine to generate specific suggestions for improving the environment. For example, for households with high electricity usage, it suggests switching to LED lighting or selecting more energy-efficient home appliances. Furthermore, based on the results of the emotion engine, it makes simple and easy-to-implement suggestions to users who are under stress.

[1548] Data visualization and presentation

[1549] The server converts the analysis results into graphs and charts for easy visual understanding. Users can view these visualized data on a smartphone app or web portal dashboard. The system also generates detailed environmental impact reports for businesses and provides analysis data via an API for educators and policymakers.

[1550] User practices

[1551] Users can check the analysis results and environmental improvement suggestions provided on the dashboard and take action based on them. For example, at home, they can purchase and install LED lighting or choose energy-efficient home appliances. At business, they can improve energy efficiency or implement waste reduction measures. In this way, users can reduce their environmental impact through concrete actions.

[1552] Specific examples

[1553] For example, suppose a user uses 200 kWh of electricity per month. The user enters this data into a smartphone app and presses the send button. The device converts the data into JSON format and uploads it to the server. At the same time, the emotion recognition function analyzes the user's face and voice, collects emotional data on "stress state," and sends it to the server.

[1554] After receiving this data, the server performs an error check. After the error check, the behavioral data is transferred to the AI ​​engine, and the emotional data is transferred to the emotion engine. The AI ​​engine returns the result that "200 kWh of electricity usage per month will result in 100 kg of CO2 emissions," and the emotion engine returns the analysis result that "the user's emotional state is stressed."

[1555] Based on these analysis results, the server generates specific environmental improvement suggestions, such as "using LED lighting" or "introducing energy-saving home appliances," and adjusts the suggestions to be simple and easy to implement, taking into account the user's stress level. These suggestions are displayed on the user's dashboard with graphs and charts.

[1556] An example of a prompt is, "If a user uses 200 kWh of electricity per month and is in a stressed state, please explain in detail how the program will display the analysis results and suggestions on a dashboard along with graphs." In this way, users can take specific actions based on the analysis results and suggestions to reduce their environmental impact.

[1557] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1558] Step 1: Data entry

[1559] Users use a smartphone app or web portal to input data about their daily activities and business activities, such as household electricity consumption (200 kWh per month), waste generation, and commuting method (private car, public transportation). This data is entered into the device. The input data is collected based on the user's behavior.

[1560] Step 2: Collecting emotion data

[1561] Users use the smartphone app's emotion recognition function to provide emotional data through facial recognition and voiceprint analysis. Specifically, the app uses the camera and microphone to analyze the user's facial expressions and voice, detecting emotions such as "stress" and "happiness." This emotional data is also input into the device.

[1562] Step 3: Transform the data

[1563] The device converts the input behavioral and emotional data into JSON format. For example, it converts data on 200 kWh of electricity usage and stress level into JSON format and creates a single data package. This conversion allows the data to be sent to the server in a standard format.

[1564] Step 4: Sending data

[1565] The device generates an HTTP request with the data converted into JSON format and sends it to the server. The data sent includes behavioral data and emotion data. Once the data is sent from the device to the server, the next analysis step begins.

[1566] Step 5: Error checking

[1567] The server parses the received JSON data and performs an error check. This checks whether there are any errors in the data format or content. For example, it verifies whether the amount of electricity used is a number, and whether the emotion data is in the correct format. Once the error check is complete, the data can proceed to analysis.

[1568] Step 6: Analyze behavioral data

[1569] The server transfers the behavioral data that has passed the error check to the AI ​​engine. The AI ​​engine evaluates the environmental impact based on the behavioral data. Specifically, it calculates indicators such as CO2 emissions, water consumption, and waste volume from electricity consumption. For example, the evaluation result may be, "100 kg of CO2 will be emitted from 200 kWh of electricity consumption per month."

[1570] Step 7: Analyze the sentiment data

[1571] The server transfers the emotion data to the emotion engine. The emotion engine analyzes the emotion data and evaluates the user's emotional state. For example, the result may be "The user's emotional state is stressed." Based on this analysis, the suggestions are adjusted according to the user's emotional state.

[1572] Step 8: Generate results and recommendations

[1573] The server combines the analysis results of the AI ​​engine and the emotion engine to generate specific suggestions for improving the environment. For example, they might suggest switching to LED lighting to reduce power consumption or recommend installing energy-efficient home appliances. Based on the results of the emotion engine, the server also provides simple, easy-to-implement suggestions to users who are under stress.

[1574] Step 9: Visualize the data

[1575] The server generates graphs and charts to make the analysis results visually easy to understand. For example, CO2 emissions are displayed in a pie chart, and specific values ​​are displayed in a bar graph. This visualization allows users to intuitively understand their own environmental impact.

[1576] Step 10: Provide data

[1577] The server then sends the generated graphs and charts to the device, where they are displayed on a dashboard in a web portal or smartphone app. Users can view the analysis results and sustainable choices on the dashboard. The system also provides detailed environmental impact reports for businesses and analytical data via an API for educators and policymakers.

[1578] Step 11: User Practice

[1579] Users can review the analysis results and sustainable choices provided by the dashboard and use them to improve their own lives and business activities. For example, households can purchase LED lighting and choose energy-efficient home appliances, while businesses can implement energy efficiency improvements and waste reduction measures. This allows users to reduce their environmental impact through concrete actions.

[1580] Through these specific processing steps, users and businesses can make environmentally sustainable choices and contribute to the realization of a sustainable society.

[1581] (Application example 2)

[1582] 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."

[1583] This invention relates to a system that evaluates the environmental impact of users' daily activities and corporate activities and provides sustainable options. However, conventional systems do not consider the user's emotional state when proposing sustainable options, making it difficult to translate these options into concrete action. Furthermore, there has been a lack of proposals for simultaneously reducing environmental impact and improving customer satisfaction in certain industries, particularly brick-and-mortar stores.

[1584] 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 the user to input behavioral data, means for the terminal to transmit the behavioral data to the server, means for the server to analyze the behavioral data and evaluate environmental impacts, means for the server to generate sustainable options based on the evaluation results, means for the server to provide the options to the user, means for an emotion recognition engine to analyze the user's emotion data, and means for the server to suggest options adapted to the emotions based on the analysis results. This makes it possible to provide specific, emotion-sensitive sustainable options for users' daily behavior and corporate activities, and in particular, in physical stores, it becomes possible to monitor energy consumption, waste volume, and customer satisfaction and generate management proposals based on the results.

[1585] "User" refers to an individual or company that uses the system.

[1586] "Behavioral data" refers to information such as electricity consumption, waste volume, and transportation methods related to users' daily behavior and corporate activities.

[1587] "Terminal" refers to a device that allows a user to input behavioral data and transmit it to a server. Examples include smartphones and computers.

[1588] "Server" refers to a computer system that receives behavioral data sent from a terminal, analyzes it, and provides the results to the user.

[1589] "Analysis" refers to the process by which the server processes the received behavioral data and evaluates the environmental impact.

[1590] "Environmental impact" refers to the impact that a user's actions have on the environment, and is evaluated using indicators such as CO2 emissions and waste volume.

[1591] "Sustainable options" refers to specific suggestions that encourage users to adopt environmentally friendly actions and choices, such as using energy-efficient appliances or taking public transport.

[1592] An "emotion recognition engine" refers to software technology that analyzes a user's emotional state and uses the results to make appropriate suggestions.

[1593] "Emotional data" refers to data that indicates the user's emotional state, and is obtained from facial expressions, tone of voice, etc.

[1594] "Monitoring" refers to the process of regularly observing and recording specific indicators (e.g., energy use, waste volume, customer satisfaction).

[1595] "Operation proposals" refer to specific improvement measures and guidelines for action that the server provides to physical store operators based on the analysis results.

[1596] "Customer satisfaction" refers to an indicator of the level of satisfaction that customers who visit a physical store feel toward the service.

[1597] "Visualization" refers to the process of visually displaying analysis results in graphs, charts, etc., and presenting them to users in an easy-to-understand manner.

[1598] The present invention relates to a system that inputs behavioral data and emotional data related to users' daily activities and business activities, analyzes the data on a server, evaluates the environmental impact, and provides sustainable options. Specifically, the system includes a terminal, a server, an emotion recognition engine, and a data visualization means.

[1599] Data entry and submission

[1600] Users input behavioral data using a device (e.g., a smartphone). This behavioral data includes energy consumption, waste volume, transportation methods, etc. This data is converted into a standard data format (e.g., JSON format). In addition, emotion data of the user is collected using emotion recognition functions (e.g., facial recognition, voiceprint analysis) embedded in the app. This data is sent to the server via an HTTP request.

[1601] Data reception and analysis

[1602] The server receives the data sent from the device and first checks for errors. After checking, the behavioral data is transferred to an AI engine to evaluate the environmental impact (e.g., CO2 emissions). The emotion data is analyzed by an emotion recognition engine to evaluate the user's emotional state. This provides information such as whether the user is stressed or relaxed.

[1603] Generate results and suggestions

[1604] The server generates sustainable options based on the analysis results obtained from the AI ​​engine and emotion recognition engine. For example, if energy usage is high, it will suggest installing energy-efficient appliances or switching to LED lighting. It also adjusts the content and presentation of the suggestions based on emotion data. For users in a stressed state, it provides simple, easy-to-implement suggestions.

[1605] Data visualization and presentation

[1606] The server visually displays the analysis results in graphs and charts, allowing users to intuitively understand their own environmental impact. For example, it may provide information such as the CO2 emissions corresponding to 200 kWh of energy consumption (e.g., 100 kg CO2 / month). This data is displayed on the device's dashboard. This display is generated using the matplotlib library.

[1607] User practices

[1608] Users can review the sustainable options presented and take specific actions. Store managers can use the information provided by the app to implement specific measures to reduce energy consumption and waste. For example, introducing LED lighting can reduce environmental impact.

[1609] Prompt Sentence Examples

[1610] "Enter your store's monthly energy use and waste volume, as well as customer reviews. The results assess your environmental impact and provide sustainable options."

[1611] As described above, the system of the present invention can promote environmentally conscious behavior by integrating and analyzing behavioral data and emotional data, and providing optimal sustainable options to users.

[1612] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1613] Step 1:

[1614] The user inputs behavioral data using a device. The user inputs data related to daily activities and store operations, such as energy consumption, waste volume, and transportation methods, into the application. At this time, the emotion recognition function analyzes the user's facial expressions and voice to collect emotional data. The input data and emotional data are converted into JSON format.

[1615] Step 2:

[1616] The device sends input data and emotion data to the server. The device generates an HTTP request and uploads the input behavioral data and emotion data to the server. The server receives the data and performs error checks to ensure it is in the correct format. Here, the input is behavioral data and emotion data, and the output is error-checked data.

[1617] Step 3:

[1618] The server analyzes the behavioral data. After error checking, the server transfers the behavioral data to an AI engine, which calculates environmental impacts such as CO2 emissions, energy consumption, and waste volume. The input is the error-checked behavioral data, and the output is the analyzed environmental impact data.

[1619] Step 4:

[1620] The server analyzes the emotion data. The server sends the error-checked emotion data to an emotion recognition engine to evaluate the user's current emotional state, where the input is the error-checked emotion data and the output is the user's emotional state data.

[1621] Step 5:

[1622] The server generates sustainable options based on the analysis results. The server combines the analysis results obtained from the AI ​​engine and emotion recognition engine to generate specific environmental improvement measures. For example, for users with high energy usage, it suggests introducing energy-saving home appliances and switching to LED lighting. The input is environmental impact data and emotional state data, and the output is sustainable options.

[1623] Step 6:

[1624] The server visualizes the proposals. The server displays the sustainable options generated in the previous step in a visual format such as graphs and charts. This makes it easier for users to intuitively understand their own environmental impacts. The input is the sustainable options, and the output is the visualized data.

[1625] Step 7:

[1626] The server provides the user with the proposed solutions. The server sends the visualized analysis results and sustainable options to the user's device. The user can then review the results in the application and take specific actions. The input is the visualized data, and the output is a notification to the user.

[1627] Through these processing steps, users can obtain specific, emotionally sensitive, and sustainable choices based on their own behavioral data. This system promotes environmentally friendly behavior and supports users' sustainable behavior.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1633] 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.

[1634] 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).

[1635] 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.

[1636] 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."

[1637] 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.

[1638] 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).

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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 example of a 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] The following is further disclosed regarding the above embodiment.

[1650] (Claim 1)

[1651] a means for a user to input behavioral data;

[1652] A means for the terminal to transmit the behavioral data to a server;

[1653] A server analyzes the behavioral data to evaluate environmental impacts;

[1654] a means for the server to generate sustainable options based on the evaluation results;

[1655] means for the server to provide said options to a user;

[1656] A system including:

[1657] (Claim 2)

[1658] means for the server to check the behavioral data for errors;

[1659] A means for the server to visualize the analysis results;

[1660] The system of claim 1 further comprising:

[1661] (Claim 3)

[1662] a means for the server to generate an environmental impact report for the enterprise;

[1663] The server provides analytical data for educators and policymakers;

[1664] The system of claim 1 further comprising:

[1665] (Claim 4)

[1666] The system according to claim 1, further comprising means for the server to provide the analysis data to an external party through an API.

[1667] (Claim 5)

[1668] 2. The system of claim 1, wherein the terminal includes means for converting input data into a standard data format.

[1669] (Claim 6)

[1670] 10. The system of claim 1, further comprising means for a user to implement the environmental improvement suggestions.

[1671] "Example 1"

[1672] (Claim 1)

[1673] a means for a user to input behavioral data;

[1674] A means for the terminal to transmit the behavioral data to a server;

[1675] means for the server to check the behavioral data for errors;

[1676] A server analyzes the behavioral data to evaluate environmental impacts;

[1677] a means for the server to generate sustainable options based on the evaluation results;

[1678] A means for the server to generate a chart for visualizing the evaluation results and sustainable options and provide the chart to a user;

[1679] A system including:

[1680] (Claim 2)

[1681] a means for the server to generate an environmental impact report for the enterprise;

[1682] The server provides analytical data for educators and policymakers;

[1683] The system of claim 1 further comprising:

[1684] (Claim 3)

[1685] means for the server to use a generative AI model to generate the options;

[1686] a means by which a user provides input data via an application or web portal;

[1687] The system of claim 1 further comprising:

[1688] "Application Example 1"

[1689] (Claim 1)

[1690] a means for a user to input behavioral data;

[1691] A means for the terminal to transmit the behavioral data to a server;

[1692] A server analyzes the behavioral data to evaluate environmental impacts;

[1693] a means for the server to generate sustainable options based on the evaluation results;

[1694] means for the server to provide said options to a user;

[1695] a means for workers to input data on energy consumption and resource utilization in industrial environments in real time through smart glasses;

[1696] means for the smart glasses to visually display the assessment results and sustainable suggestions in real time;

[1697] A system including:

[1698] (Claim 2)

[1699] means for the server to check the behavioral data for errors;

[1700] A means for the server to visualize the analysis results;

[1701] The system of claim 1 further comprising:

[1702] (Claim 3)

[1703] a means for the server to generate an environmental impact report for the enterprise;

[1704] The server provides analytical data for educators and policymakers;

[1705] A means for the server to perform analysis using a generative AI model based on the environmental data provided through the smart glasses;

[1706] The system of claim 1 further comprising:

[1707] "Example 2: Combining Emotion Engines"

[1708] (Claim 1)

[1709] A means for a user to input behavioral data and emotional data;

[1710] a means for transmitting the behavioral data and emotion data to a server by the terminal;

[1711] A server analyzes the behavioral data and evaluates environmental impacts;

[1712] a means for the server to analyze the emotion data and evaluate the user's emotion state;

[1713] A means for the server to generate sustainable options based on the evaluation results and adjust the options according to the user's emotional state;

[1714] means for the server to provide the adjusted options to a user;

[1715] A system including:

[1716] (Claim 2)

[1717] means for the server to check the behavioral data for errors;

[1718] A server has means for visualizing the analysis results;

[1719] a means for displaying the visualized results on a dashboard by the terminal;

[1720] The system of claim 1 further comprising:

[1721] (Claim 3)

[1722] a means for the server to generate an environmental impact report for the enterprise;

[1723] The server provides analytical data for educators and policymakers;

[1724] The system of claim 1 further comprising:

[1725] "Application example 2 when combining emotion engines"

[1726] (Claim 1)

[1727] a means for a user to input behavioral data;

[1728] A means for the terminal to transmit the behavioral data to a server;

[1729] A server analyzes the behavioral data to evaluate environmental impacts;

[1730] a means for the server to generate sustainable options based on the evaluation results;

[1731] means for the server to provide said options to a user;

[1732] A means for the emotion recognition engine to analyze the emotion data of the user;

[1733] A means for the server to propose options adapted to emotions based on the analysis results

[1734] A system including:

[1735] (Claim 2)

[1736] means for the server to check the behavioral data for errors;

[1737] A means for the server to visualize the analysis results;

[1738] a means for the server to visually display the suggestions;

[1739] a means of collecting user feedback;

[1740] The system of claim 1 further comprising:

[1741] (Claim 3)

[1742] a means for the server to generate an environmental impact report for the enterprise;

[1743] The server provides analytical data for educators and policymakers;

[1744] A means for brick-and-mortar store operators to monitor energy use, waste volume, and customer satisfaction;

[1745] means for generating sustainable management proposals based on said monitoring results;

[1746] The system of claim 1 further comprising: [Explanation of symbols]

[1747] 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 means for a user to input behavioral data; A means for the terminal to transmit the behavioral data to a server; A server analyzes the behavioral data to evaluate environmental impacts; a means for the server to generate sustainable options based on the evaluation results; means for the server to provide said options to a user; A system including:

2. means for the server to check the behavioral data for errors; A means for the server to visualize the analysis results; The system of claim 1 further comprising:

3. a means for the server to generate an environmental impact report for the enterprise; The server provides analytical data for educators and policymakers; The system of claim 1 further comprising:

4. The system according to claim 1, wherein the server includes means for providing the analysis data to an external party through an API.

5. 2. The system of claim 1, wherein the terminal includes means for converting input data into a standard data format.

6. 2. The system of claim 1, further comprising means for a user to implement the environmental improvement suggestions.

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