System

Abstract

A system is provided.SOLUTION: The system includes a means for storing an inputted environmental objective, a means for generating an annual plan based on the environmental objective, a means for predicting a simulation result of a designated reduction measure, a means for generating a proposal required for environmental improvement, a means for supporting a carbon credit transaction, and a means for collecting subsidy information and providing it to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, companies and public institutions have been under pressure to take measures against environmental issues, but they face difficulties in formulating and implementing effective plans to do so. Furthermore, there is no system for integrated management of a wide range of tasks, including setting environmental goals, creating annual plans, simulating reductions, proposing specific improvements, trading carbon credits, and utilizing subsidies. This creates the problem that various environmental measures are implemented separately, reducing the overall efficiency and effectiveness. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including a means for saving input environmental targets, a means for generating an annual plan based on the environmental targets, a means for predicting simulation results of specified reduction measures, a means for generating proposals necessary for environmental improvement, a means for supporting carbon credit trading, and a means for collecting and providing subsidy information to users. The system also includes a means for utilizing AI to propose an annual plan based on target data stored in a database, and a means for using AI to generate multiple simulation results based on input data and presenting the results to users, thereby providing a system that realizes more comprehensive and efficient environmental measures.

[0006] Understood. Below are definitions of important words.

[0007] The "means for saving input environmental goals" refers to a system having a function for temporarily or permanently recording environmental goal data input by a user in a storage device.

[0008] The "means for generating an annual plan based on environmental targets" is a system that has the function of automatically creating a specific action plan for one year using stored environmental target data.

[0009] The "means for predicting the simulation results of specified reduction measures" is a system that has the function of predicting the effects of energy efficiency improvement measures or other environmental reduction measures selected by the user and generating simulation results.

[0010] The "means for generating proposals necessary for environmental improvement" is a system that has the function of automatically proposing effective environmental improvement measures to the user based on input data and simulation results.

[0011] A "means for supporting carbon credit trading" is a system that has the function of suggesting optimal trading timing and trading partners when a user purchases or sells carbon credits, and supporting the smooth conduct of transactions.

[0012] The "means for collecting and providing subsidy information to users" is a system that has the function of collecting information on the latest subsidies and providing users with details of available subsidies.

[0013] "Goal data stored in a database" refers to information stored in a database for recording environmental goals set by a user.

[0014] "Means for proposing annual plans using AI" refers to a system that uses artificial intelligence technology to automatically create annual plans based on stored goal data and propose them to users.

[0015] The "means for generating a plurality of simulation results" is a system having the function of executing simulations for a plurality of different scenarios based on data provided by the user and generating the results.

[0016] The "means for presenting results to a user" is a system that has the function of displaying the generated simulation results and proposals to a user in a visual or text format. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0038] The present invention provides an integrated system for taking effective measures against environmental problems. The following describes in detail the embodiments of the present invention. This system functions through the cooperation of a server, terminals, and users.

[0039] ---

[0040] 1. Goal Setting

[0041] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[0042] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[0043] ---

[0044] 2. Create an annual plan

[0045] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[0046] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[0047] ---

[0048] 3. Prediction and reduction simulation

[0049] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[0050] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[0051] ---

[0052] 4. Suggestion function

[0053] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[0054] The user can review these suggestions on the system and decide whether to incorporate them as appropriate.

[0055] ---

[0056] 5. Support for purchasing and selling carbon credits

[0057] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[0058] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[0059] ---

[0060] 6. Support for using subsidies

[0061] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[0062] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[0063] ---

[0064] This allows companies and public institutions to receive comprehensive support to efficiently and effectively achieve their environmental goals. The present invention is an extremely effective tool for comprehensively managing environmental measures.

[0065] The processing flow will be explained below.

[0066] ---

[0067] goal setting

[0068] Step 1:

[0069] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[0070] Step 2:

[0071] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[0072] Step 3:

[0073] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[0074] Step 4:

[0075] The server receives the input environmental goals and stores them in a database.

[0076] ---

[0077] Creating an annual plan

[0078] Step 1:

[0079] User selects the "Create Annual Plan" option from the dashboard.

[0080] Step 2:

[0081] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[0082] Step 3:

[0083] The user enters the necessary data according to the template and presses the send button.

[0084] Step 4:

[0085] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[0086] Step 5:

[0087] The user reviews the proposed annual plan and makes any necessary adjustments.

[0088] Step 6:

[0089] The server saves the revised plan as the final version in the database.

[0090] ---

[0091] Prediction and reduction simulation

[0092] Step 1:

[0093] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[0094] Step 2:

[0095] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[0096] Step 3:

[0097] The server runs a simulation based on the entered items and data.

[0098] Step 4:

[0099] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[0100] ---

[0101] Suggestion feature

[0102] Step 1:

[0103] The server continuously analyzes user-entered data, periodically analyzing it to detect patterns and trends.

[0104] Step 2:

[0105] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[0106] Step 3:

[0107] Users view the offers from their dashboard and view details about the offers that interest them.

[0108] Step 4:

[0109] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[0110] ---

[0111] Support for purchasing and selling carbon credits

[0112] Step 1:

[0113] User selects the "Carbon Credit Trading" option from the dashboard.

[0114] Step 2:

[0115] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[0116] Step 3:

[0117] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[0118] Step 4:

[0119] The user reviews the proposal and, if satisfied, executes the transaction.

[0120] ---

[0121] Support for using subsidies

[0122] Step 1:

[0123] The server collects the latest grant information and updates the database.

[0124] Step 2:

[0125] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[0126] Step 3:

[0127] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[0128] Step 4:

[0129] The user initiates the application process and uploads the required documents into the system.

[0130] Step 5:

[0131] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[0132] ---

[0133] This allows the system to assist companies and public institutions in achieving their environmental goals effectively and efficiently.

[0134] Example 1

[0135] 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."

[0136] Many current environmental management systems have separate functions, such as goal setting, annual plan creation, reduction simulation, transaction support, and provision of subsidy information, making it difficult to achieve overall efficiency and implement effective measures. Furthermore, due to a lack of automatic generation functions utilizing data analysis and AI technology, manual input and planning by users takes time and effort. The purpose of this invention is to solve these problems and provide a comprehensive system for achieving environmental goals effectively and efficiently.

[0137] 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.

[0138] In this invention, the server includes: means for saving input environmental goals; means for generating an annual plan based on the environmental goals; means for predicting simulation results of specified reduction measures; means for generating proposals necessary for environmental improvement; means for supporting carbon credit trading; means for collecting subsidy information and supporting application procedures; means for analyzing data input by a user and automatically generating an optimal annual plan using AI technology; means for acquiring real-time market data based on conditions specified by the user and proposing transaction timing; means for saving subsidy information in a database and acquiring necessary information based on search conditions to support applications; and means for analyzing the user's environmental data at regular intervals and generating new proposals. This allows for integrated management of the process from setting to achieving environmental goals, enabling users to take actions toward achieving their goals effectively and efficiently.

[0139] The "means for saving input environmental targets" is a function for receiving environmental target data input by the user and saving it in a database within the server.

[0140] The "means for generating annual plans based on environmental targets" is a function that uses AI technology to automatically generate optimal annual plans based on input environmental target data.

[0141] The "means for predicting the simulation results of the specified reduction measures" is a function that executes a simulation based on the reduction measures input by the user, predicts the results, and presents them.

[0142] The "means for generating proposals necessary for environmental improvement" is a function that analyzes data at regular intervals and automatically generates proposals for new environmental improvement measures and energy efficiency improvement measures.

[0143] "Means to support carbon credit trading" is a function that suggests optimal trading timing and trading partners to users who wish to buy or sell carbon credits.

[0144] The "means for collecting subsidy information and supporting application procedures" is a function that collects the latest subsidy information and supports users in smoothly proceeding with the subsidy application procedures.

[0145] "Means for analyzing data entered by the user and automatically generating an optimal annual plan using AI technology" refers to a function that analyzes data entered by the user and automatically generates an optimal annual plan based on that data using AI technology.

[0146] "Means for obtaining real-time market data based on conditions specified by the user and proposing trading timing" is a function for obtaining market data in real time based on conditions specified by the user and proposing optimal trading timing.

[0147] "Means for saving subsidy information in a database and for users to obtain the necessary information based on search criteria to assist with applications" refers to a function that saves subsidy information in a database, provides the necessary subsidy information based on search criteria entered by the user, and assists with applications.

[0148] The "means for periodically analyzing the user's environmental data and generating new proposals" is a function that periodically analyzes the user's environmental data and automatically generates new proposals based on that data.

[0149] This system invention functions through the cooperation of the server, the terminal, and the user. The program and processing of this system will be specifically described below.

[0150] goal setting

[0151] The user logs into the system using a terminal. On the goal setting screen displayed on the terminal, the user enters an environmental goal, such as "reducing CO2 emissions by 30% by 2025." When the user enters the goal and presses the send button, the server receives the data. The server stores this goal data in a database and uses it as the base data for future annual plans and simulations.

[0152] Creating an annual plan

[0153] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters this data and presses the submit button, the server receives the entered data, analyzes it using AI technology (for example, Scikit-learn or TENSORFLOW (registered trademark)), and automatically generates an optimal annual plan. The proposed annual plan is presented to the user for approval.

[0154] Prediction and reduction simulation

[0155] When a user wants to simulate a reduction measure, they select the target item on the simulation screen. For example, if they want to check the effect of introducing new energy-saving equipment, they enter the detailed data. When the user enters the detailed data and presses the send button, the server runs a simulation using an AI model (for example, a Keras neural network) based on this data. The resulting predicted reduction amount and cost effect are presented to the user.

[0156] Suggestion feature

[0157] The server periodically analyzes the user's environmental data and generates new proposals. For example, it may propose an environmental awareness program for employees or new energy efficiency improvements based on the data analysis. These proposals are presented to the user on the system, and the user can decide whether to implement the proposals as needed.

[0158] Support for purchasing and selling carbon credits

[0159] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen. The server obtains market data in real time and proposes the optimal trading timing and trading partner for the user's needs. The user can then proceed with the specific transaction based on this proposal.

[0160] Support for using subsidies

[0161] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen. The server provides subsidy information that matches the user's criteria and assists the user in applying for a subsidy. The user can upload the necessary documents through the system and apply for a subsidy.

[0162] Specific examples and examples of prompts to input to the generative AI model

[0163] Examples:

[0164] Goal setting: "The user enters their goal, such as 'reduce CO2 emissions by 30% by 2025,' into a form on their device and saves it on the server."

[0165] Creating an annual plan: "The user inputs energy consumption data, and the server analyzes the data and generates an optimal annual plan."

[0166] Energy Reduction Simulation: "Users can simulate the introduction of new energy-saving equipment, enter detailed data to confirm the effects, and then press the submit button. The server then uses AI to present the simulation results."

[0167] Example prompt for a generative AI model:

[0168] "Please explain the process flow of your system to support goal setting."

[0169] "Please tell me the detailed steps for the system that generates an annual plan based on the environmental goals entered by the user."

[0170] "Please detail the operation of the system to simulate the CO2 reduction effect of introducing new energy-saving equipment."

[0171] This system is highly effective as an integrated support tool for companies and public institutions to efficiently achieve their environmental goals. By utilizing specific data processing and AI technology, users can quickly and accurately obtain the information and support they need.

[0172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0173] goal setting

[0174] Step 1:

[0175] The user starts up the terminal and logs in to the system. They enter their user ID and password on the login screen and press the submit button. Based on this input, the server retrieves the user information from the database and performs authentication processing. If authentication is successful, the user's dashboard is displayed.

[0176] Step 2:

[0177] The user accesses the goal setting screen from the dashboard, enters an environmental goal such as "reduce CO2 emissions by 30% by 2025," and presses the submit button. The server receives this input data and executes an SQL query to save it in the database.

[0178] Creating an annual plan

[0179] Step 3:

[0180] The user accesses the annual plan creation screen. The annual plan creation screen displays a template for entering energy consumption and current CO2 emission data. The user enters this data and presses the submit button. The server receives the input data and saves it in the database.

[0181] Step 4:

[0182] The server invokes an AI model (such as Scikit-learn or TensorFlow) based on the stored data and performs data analysis. This analysis generates an optimal annual plan. The generated annual plan is converted into an appropriate format for presentation to the user and displayed on a web page.

[0183] Step 5:

[0184] The user checks the presented annual plan and gives feedback on approval or correction. When the user clicks the approval button, the server records the approval and proceeds to the next step.

[0185] Prediction and reduction simulation

[0186] Step 6:

[0187] The user accesses the simulation screen, selects the item to be simulated, enters data to confirm the effect of introducing new energy-saving equipment, for example, and presses the send button. The server receives this data and runs the simulation using an AI model (e.g., Keras).

[0188] Step 7:

[0189] The server generates simulation results, calculates predicted savings and cost benefits, and formats and displays these results on a web page for presentation to the user.

[0190] Suggestion feature

[0191] Step 8:

[0192] The server periodically retrieves and analyzes environmental data from the database. It then applies machine learning algorithms such as clustering and regression analysis to generate new proposals. These proposals include specific content, such as "new energy efficiency improvements to reduce energy consumption by 10%."

[0193] Step 9:

[0194] The generated suggestions are displayed to the user, who can then review them and decide whether to implement them. Once the user clicks the "Implement" button, the server records the information and uses it in the next improvement cycle.

[0195] Support for purchasing and selling carbon credits

[0196] Step 10:

[0197] A user accesses the trading screen and enters the quantity and price of carbon credits they wish to buy or sell. The server receives the input data and calls an API to obtain market data in real time.

[0198] Step 11:

[0199] The server calculates the optimal trading timing and counterparty based on market data and makes a proposal to the user. When this proposal is displayed and the user clicks the trade button, the server records the trading information and proceeds with the actual trade.

[0200] Support for using subsidies

[0201] Step 12:

[0202] The server periodically collects the latest subsidy information from the subsidy information API and stores it in a database. Based on this database information, the user enters the conditions on the subsidy search screen and presses the submit button.

[0203] Step 13:

[0204] The server extracts and presents subsidy information that matches the user's search criteria. Once the user finds the subsidy information they need, they upload their application documents and click the apply button. The server records this information and sends it to the grant agency.

[0205] Through the above processing steps, the system is able to provide comprehensive support for users to achieve their environmental goals efficiently and effectively.

[0206] (Application example 1)

[0207] 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."

[0208] Currently, many factories are setting environmental goals and trying to manage energy consumption based on them, but they face difficulties in generating annual plans, predicting reduction measures, running simulations, and proposing new energy-saving measures. Furthermore, trading carbon credits and utilizing subsidies require real-time data analysis and quick decision-making, which requires specialized knowledge and skills, and many factories are slow to adopt these approaches. Therefore, an integrated system is needed to improve the energy efficiency of the entire factory and effectively achieve environmental goals.

[0209] 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.

[0210] In this invention, the server includes means for saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing subsidy information to users, means for controlling robots to optimize energy efficiency in the factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time, thereby making it possible to improve the energy efficiency of the entire factory and effectively achieve the environmental targets.

[0211] "Environmental goals" are targets set by users for reducing energy consumption and CO2 emissions at facilities such as factories.

[0212] An "annual plan" is a detailed schedule for implementing specific action plans throughout the year to achieve set environmental targets.

[0213] "Abatement measures" are specific measures and methods implemented to reduce energy consumption and mitigate environmental impact.

[0214] "Simulation results" are the results of predicting and analyzing the effects of the set reduction measures if they are actually implemented.

[0215] "Proposals" are specific action plans and measures generated by the server to help achieve environmental goals.

[0216] "Carbon credit trading" is a market transaction that aims to reduce environmental impact by buying and selling reduced CO2 emissions.

[0217] "Subsidy information" refers to information on fundraising provided to support environmental improvement and energy efficiency improvements.

[0218] "Factory environment" refers to the overall situation and conditions regarding production equipment and energy consumption within a factory.

[0219] "Energy efficiency" refers to the ability to effectively utilize energy resources, and means reducing wasteful energy consumption and operating efficiently.

[0220] "Robot control" refers to the operation and supervision of robots deployed within a factory to manage and optimize their movements and operations.

[0221] "Energy-saving equipment" refers to facilities and devices that consume less energy than conventional equipment and can reduce the burden on the environment.

[0222] A "generative AI model" is an algorithm or program that allows artificial intelligence to generate optimal plans and proposals on its own based on data entered by the user.

[0223] "Real-time data analysis" is a technology that instantly analyzes ongoing data and outputs results immediately.

[0224] "Energy saving measures" refer to specific methods and measures for reducing energy consumption.

[0225] The present invention provides an integrated system for optimizing energy efficiency and achieving environmental goals within a factory, which functions through the cooperation of a server, terminals, and users.

[0226] The server includes means for storing input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting the simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting subsidy information and providing it to users, means for controlling robots to optimize energy efficiency in a factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time.

[0227] An embodiment of this system will now be described in detail.

[0228] First, the user logs into the system using a terminal and inputs their environmental goals. For example, they can set a specific goal such as "reducing CO2 emissions by 30% by 2025." The server receives these goals and stores them in a database. The saved goal data serves as the basis for future annual plans and simulations.

[0229] Next, the user accesses the system again and opens the annual plan creation screen. A template for inputting energy consumption and current CO2 emission data is displayed, and the user enters this data. The server analyzes the input data using a generative AI model (e.g., TensorFlow or PyTorch) and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[0230] Furthermore, if a user wants to simulate the effects of introducing new energy-saving equipment, they can enter details on the simulation screen, such as the annual energy savings of the new energy-saving equipment. Based on this input data, the server uses a generative AI model to run the simulation and presents the results, such as predicted energy savings and cost effectiveness, to the user.

[0231] The server also continuously analyzes the user's environmental data and generates new proposals. For example, based on periodic data analysis, it makes effective proposals such as environmental awareness programs for employees and new energy efficiency improvement measures. The user can review these proposals on the system and decide whether to incorporate them as appropriate.

[0232] The system also supports the use of carbon credit trading and subsidy information. When a user wishes to buy or sell carbon credits, they enter the quantity and price on the system's trading screen. The server obtains market data in real time and suggests the optimal trading timing and trading partner. For subsidy information, the server also collects the latest information, provides information that matches the search criteria, and supports the application process.

[0233] In this way, it is possible to increase the energy efficiency of the entire factory and help to effectively achieve environmental goals.

[0234] As a concrete example, consider a factory manager who has set a goal of reducing CO2 emissions by 30% by 2025. The manager enters the following:

[0235] Example prompt sentence:

[0236] To reduce your factory's CO2 emissions by 30%, use AI to generate an annual plan based on the following data:

[0237] 1. Target year: 2025

[0238] 2. Current energy consumption: 1000 kWh / month

[0239] 3. Current CO2 emissions: 300 kg / month

[0240] 4. Data for new energy-saving devices: 200 kWh saved per year

[0241] Based on these, we will provide you with the best annual plan.

[0242] Based on this prompt, the generative AI model generates an annual plan and presents suggestions to the user.

[0243] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0244] Step 1:

[0245] Users log in to the system using a terminal and input their environmental goals. For example, they can set a goal such as "reducing CO2 emissions by 30% by 2025." The input environmental goals are sent to the server, which receives this goal data and stores it in a database.

[0246] Step 2:

[0247] The user accesses the system again from their terminal and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters and submits this data, the server receives it. The received data is stored in a database.

[0248] Step 3:

[0249] The server analyzes the stored environmental targets and energy consumption data using a generative AI model (e.g., TensorFlow or PyTorch). Based on the analysis results, an annual plan is automatically generated. The generated annual plan is sent to the user's device as a proposal. The user reviews the presented annual plan and decides whether to approve it.

[0250] Step 4:

[0251] When a user wants to simulate the effects of introducing new energy-saving equipment, they enter detailed data on the simulation screen and submit it. For example, they can enter the annual energy savings of the new energy-saving equipment. The server receives this data and runs the simulation using a generative AI model. The simulation results, such as predicted energy savings and cost effects, are then sent to the user's device.

[0252] Step 5:

[0253] The server continuously analyzes the user's environmental data and generates new energy-saving suggestions. Based on the results of periodic data analysis, the server creates useful suggestions, such as environmental awareness programs for employees or new energy efficiency improvements. The generated suggestions are sent to the user's device. The user reviews the suggestions and decides whether to implement them.

[0254] Step 6:

[0255] When a user wishes to buy or sell carbon credits, they enter the desired quantity and price on the system's trading screen and submit it. The server receives the entered data and obtains market data in real time. It then proposes the optimal trading timing and trading partner. The proposal is sent to the user's device, and the user proceeds with the transaction based on it.

[0256] Step 7:

[0257] When a user wants to search for subsidy information, they enter their search criteria on the system's search screen and submit it. The server searches the database for the latest subsidy information based on the entered criteria. Subsidy information that matches the criteria is sent to the user's device, and the user can use that information to apply for a subsidy.

[0258] 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.

[0259] The present invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the present invention. This system functions by combining a server, a terminal, and an emotion engine.

[0260] ---

[0261] goal setting

[0262] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[0263] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[0264] ---

[0265] Creating an annual plan

[0266] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[0267] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[0268] ---

[0269] Prediction and reduction simulation

[0270] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[0271] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[0272] ---

[0273] Suggestion feature

[0274] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[0275] Furthermore, the emotion engine analyzes the user's input and behavioral data to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is losing motivation for the plan, the server generates additional suggestions and encouraging messages to improve the user's motivation.

[0276] ---

[0277] Support for purchasing and selling carbon credits

[0278] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[0279] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[0280] ---

[0281] Support for using subsidies

[0282] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[0283] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[0284] ---

[0285] Combining Emotion Engines

[0286] As users go through steps such as goal setting, annual planning, simulations, proposals, carbon credit trading, and subsidy information viewing, the emotion engine analyzes their input in real time to recognize emotions such as stress, excitement, or anxiety.

[0287] Based on the results of the emotion engine, the server automatically responds appropriately to the user's situation. For example, if it determines that the user's motivation is declining, it displays positive feedback and encouraging messages to improve motivation.

[0288] ---

[0289] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby enabling companies and public institutions to achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[0290] The processing flow will be explained below.

[0291] ---

[0292] goal setting

[0293] Step 1:

[0294] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[0295] Step 2:

[0296] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[0297] Step 3:

[0298] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[0299] Step 4:

[0300] The emotion engine analyzes the user's behavioral patterns and typing speed while they are typing to determine their emotional state. For example, if they type smoothly, it will sense "normal," but if their tempo is too fast, it will sense "impatience."

[0301] Step 5:

[0302] The server receives the user's emotion data and goal data and stores them in a database.

[0303] ---

[0304] Creating an annual plan

[0305] Step 1:

[0306] User selects the "Create Annual Plan" option from the dashboard.

[0307] Step 2:

[0308] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[0309] Step 3:

[0310] The user enters the necessary data according to the template and presses the send button.

[0311] Step 4:

[0312] The emotion engine captures the user's emotional state while they are typing, and if stress or anxiety is detected, the server displays a comforting or encouraging message.

[0313] Step 5:

[0314] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[0315] Step 6:

[0316] The user reviews the proposed annual plan and makes any necessary adjustments.

[0317] Step 7:

[0318] The server saves the revised plan as the final version in the database.

[0319] ---

[0320] Prediction and reduction simulation

[0321] Step 1:

[0322] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[0323] Step 2:

[0324] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[0325] Step 3:

[0326] The emotion engine captures the user's emotional state and displays reminder messages if they are slow to type.

[0327] Step 4:

[0328] The server runs a simulation based on the entered items and data.

[0329] Step 5:

[0330] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[0331] Step 6:

[0332] The emotion engine monitors the user's emotions regarding the simulation results and displays additional suggestions if satisfaction is low.

[0333] ---

[0334] Suggestion feature

[0335] Step 1:

[0336] The server continuously analyzes your environment data, periodically analyzing it to detect patterns and trends.

[0337] Step 2:

[0338] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[0339] Step 3:

[0340] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly. For example, if the user is feeling stressed, simple and effective suggestions will be prioritized.

[0341] Step 4:

[0342] Users view the offers from their dashboard and view details about the offers that interest them.

[0343] Step 5:

[0344] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[0345] ---

[0346] Support for purchasing and selling carbon credits

[0347] Step 1:

[0348] User selects the "Carbon Credit Trading" option from the dashboard.

[0349] Step 2:

[0350] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[0351] Step 3:

[0352] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[0353] Step 4:

[0354] The emotion engine analyzes the user's emotional state before trading, and if the user feels unsure, it displays information to build confidence and past success stories.

[0355] Step 5:

[0356] The user reviews the proposal and, if satisfied, executes the transaction.

[0357] ---

[0358] Support for using subsidies

[0359] Step 1:

[0360] The server collects the latest grant information and updates the database.

[0361] Step 2:

[0362] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[0363] Step 3:

[0364] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[0365] Step 4:

[0366] The user initiates the application process and uploads the required documents into the system.

[0367] Step 5:

[0368] The emotion engine analyzes the user's emotional state during the application process and displays a guide to simplify the process if the user is confused.

[0369] Step 6:

[0370] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[0371] ---

[0372] This processing flow enables the system, combined with the emotion engine, to grasp the user's emotional state in real time and provide appropriate support and suggestions, helping companies and public institutions achieve their environmental goals more efficiently and effectively.

[0373] Example 2

[0374] 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."

[0375] The purpose of this invention is to improve user motivation throughout the process, from setting environmental goals to managing their achievement, and to realize more effective and sustainable environmental measures. Conventional systems lack the means to respond to fluctuations in users' emotions and motivation, which leads to a high likelihood of users giving up midway through the plan. Furthermore, functions such as setting environmental goals, creating annual plans, simulating reductions, trading carbon credits, and providing subsidy information are not integrated, which makes it difficult to provide consistent support.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0377] In this invention, the server includes means for saving environmental goals entered by the user, means for automatically generating an annual plan based on the saved environmental goals, means for predicting the results of a simulation of reduction measures specified by the user, means for continuously generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing the latest subsidy information to the user, means for analyzing the user's emotional state in real time, and means for providing feedback to improve the user's motivation based on the analysis results, thereby enabling effective support for achieving environmental goals that takes the user's emotional state into consideration.

[0378] "Environmental goals" are specific targets set by the user for environmental protection and improvement.

[0379] An "annual plan" is a plan that indicates specific measures and actions to be implemented within a year based on the environmental goals that have been saved.

[0380] "Reduction measures" are specific measures to reduce environmental impact, such as reducing energy consumption and CO2 emissions.

[0381] A "simulation" is a mock experiment to predict the results of implementing specified reduction measures.

[0382] "Proposals" are measures and actions that are necessary to improve the environment that are presented to the user.

[0383] A "carbon credit" is a tradable unit that represents the amount of greenhouse gas emissions reduction, such as CO2.

[0384] "Subsidy information" refers to information for receiving financial support to implement environmental measures.

[0385] "Saving" is the act of writing data or information entered by a user into a database.

[0386] "Analysis" refers to the act of analyzing and evaluating data based on user input and environmental data.

[0387] "Continuous generation" is the process of periodically analyzing data and generating new suggestions and information.

[0388] "Real time" means that processing is executed the instant the user performs an operation or input.

[0389] "Emotional state" refers to the user's mental and psychological state.

[0390] "Motivation" refers to the user's willingness and motivation to achieve environmental goals.

[0391] "Feedback" refers to information or actions given in response to a user's actions or state.

[0392] The present invention provides a system that recognizes a user's emotions and supports the process from setting environmental goals to managing their achievement accordingly. The system functions by combining a server, a terminal, and an emotion engine. Hereinafter, embodiments of the present invention will be described in detail.

[0393] Hardware and software used

[0394] Server: A physical or virtual computer with high-performance data processing capabilities.

[0395] Terminal: User input device such as a personal computer, smartphone, or tablet.

[0396] Database: Data management system such as MySQL (registered trademark), PostgreSQL, etc.

[0397] Emotion Engine: Uses AI technology to analyze user emotions

[0398] Generative AI models: Artificial intelligence techniques used to analyze environmental data and generate recommendations

[0399] Detailed Description

[0400] 1. Goal Setting

[0401] User action: The user logs in to the system using a terminal. After logging in, they enter a specific environmental goal into the form on the screen, such as "reducing CO2 emissions by 30% by 2025."

[0402] Server operation: The server receives this goal and stores it in a database. The saved goal data will be used as the basis for future annual planning and simulations.

[0403] 2. Create an annual plan

[0404] User action: The user opens the annual planning screen and enters data such as energy consumption of 500 kWh and current CO2 emissions of 100 tonnes.

[0405] Server operation: The server analyzes the input data using a generative AI model and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[0406] 3. Prediction and reduction simulation

[0407] User action: If the user wants to check the effect of introducing new energy-saving equipment on the simulation screen, he or she enters the details.

[0408] Server operation: The server runs a simulation using a generative AI model based on the input data, and presents the simulation results, such as predicted reductions and cost effectiveness, to the user.

[0409] 4. Suggestion function

[0410] Server operation: The server continuously analyzes the user's environmental data and generates new suggestions, such as environmental awareness programs for employees or new energy efficiency improvements based on data analysis.

[0411] How the emotion engine works: It analyzes user input and behavioral data to recognize the user's emotional state. For example, if the server recognizes that the user is losing motivation for the plan, it will generate additional suggestions or encouraging messages.

[0412] 5. Support for purchasing and selling carbon credits

[0413] User action: The user enters the desired amount and price on the carbon credit trading screen.

[0414] Server operation: The server obtains market data in real time and proposes optimal trading timing and trading partners according to the user's preferences.

[0415] 6. Support for using subsidies

[0416] Server operation: The server collects the latest grant information and stores it in a database.

[0417] User action: The user enters criteria on the grant information search screen.

[0418] Server operation: The server provides information on grants that match the user's requirements and assists with the application process.

[0419] Examples and prompts

[0420] Example 1: Setting environmental goals

[0421] Input: "Reduce CO2 emissions by 30% by 2025"

[0422] Process description: The user inputs a goal into the terminal, and the server stores the goal in the database.

[0423] Example 2: Creating an annual plan

[0424] Input: "Energy consumption: 500kWh, Current CO2 emissions: 100 tons"

[0425] Process description: The user enters data into the annual plan creation screen, and the server uses AI to generate and propose the optimal annual plan.

[0426] Example 3: Using an Emotion Engine

[0427] Input volume: "I get stressed when things don't go as planned."

[0428] Process description: The emotion engine analyzes the input data and generates encouraging messages and new suggestions to reduce the user's stress.

[0429] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby helping companies and public institutions achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[0430] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0431] Goal setting process flow

[0432] Step 1: User Login

[0433] Input: Username and Password

[0434] Specific behavior:

[0435] 1. A user accesses the system at a terminal and enters their username and password on the login screen.

[0436] 2. The server receives the entered username and password.

[0437] Data processing and calculation:

[0438] The server compares the input password with the hashed password in its database to see if they match.

[0439] Output: If authentication is successful, transition to the main screen. If authentication fails, display an error message.

[0440] Step 2: Enter your goals

[0441] Enter: "Cut CO2 emissions by 30% by 2025."

[0442] Specific behavior:

[0443] 1. After the user logs in, they will be taken to the goal setting screen.

[0444] 2. A user types "Reduce CO2 emissions by 30% by 2025" into a text box.

[0445] 3. The user clicks the "Submit" button.

[0446] Data processing and calculation:

[0447] The server receives the input, checks the format of the data, and converts it if necessary, for example checking the date format or numbers.

[0448] Output: The converted target data is sent to the server.

[0449] Step 3: Save your goal

[0450] Input: Transformed target data

[0451] Specific behavior:

[0452] 1. The server saves the goal data in a database.

[0453] 2. Metadata such as goal ID, user ID, and setting date and time are also stored in the database.

[0454] Data processing and calculation:

[0455] Convert the data format to SQL and insert it into the database.

[0456] Output: Goal setting completion message

[0457] Annual plan creation process flow

[0458] Step 1: Access the user's annual plan screen

[0459] Input: None (accessed by screen transition)

[0460] Specific behavior:

[0461] 1. The user opens the annual plan creation screen on their device.

[0462] 2. The planning template will be displayed.

[0463] Step 2: Enter your data

[0464] Input: "Energy consumption: 500kWh" "Current CO2 emissions: 100 tons"

[0465] Specific behavior:

[0466] 1. The user enters energy consumption of 500 kWh or current CO2 emissions of 100 tons into the template.

[0467] 2. After entering the information, the user clicks the "Submit" button.

[0468] Data processing and calculation:

[0469] The server receives the data, checks the format, and checks whether it is a numeric format.

[0470] Output: Validated data is passed to the server.

[0471] Step 3: Automatically generate annual plans

[0472] Input: Validated Data

[0473] Specific behavior:

[0474] 1. The server analyzes the verified data using an AI model.

[0475] 2. The model generates an optimal annual plan.

[0476] Data processing and calculation:

[0477] A generative AI model analyzes input data and derives recommended measures and action plans.

[0478] Output: Generated annual planning data

[0479] Step 4: Annual plan presentation and approval

[0480] Input: Generated annual plan data

[0481] Specific behavior:

[0482] 1. The server presents the generated annual plan to the user.

[0483] 2. The user reviews the plan and, if satisfied, clicks the "Approve" button.

[0484] Data processing and calculation:

[0485] Add approval data and finalize the annual plan.

[0486] Output: Confirmed annual plan

[0487] Processing flow for prediction and reduction simulation

[0488] Step 1: Open the simulation screen

[0489] Input: None (accessed by screen transition)

[0490] Specific behavior:

[0491] 1. The user opens the simulation screen on their device.

[0492] 2. A menu will appear for selecting the items to be simulated.

[0493] Step 2: Selecting simulation items and entering data

[0494] Input: "Installation of new energy-efficient equipment"

[0495] Specific behavior:

[0496] 1. The user selects the items to be simulated and enters the necessary data, such as "power consumption of new equipment," "installation cost," and "estimated lifespan."

[0497] 2. The user clicks the "Simulate" button.

[0498] Data processing and calculation:

[0499] The server receives the input data and performs format validation and format conversion.

[0500] Output: The transformed input data

[0501] Step 3: Run the simulation and present the results

[0502] Input: The converted input data

[0503] Specific behavior:

[0504] 1. The server runs a simulation using the AI ​​model.

[0505] 2. A generative AI model calculates the projected savings and cost benefits.

[0506] 3. The server presents the calculation results to the user.

[0507] Data processing and calculation:

[0508] Calculate the predicted reductions, cost effectiveness, etc. and compile the results.

[0509] Output: Simulation result data (e.g., "Predicted reduction: 20 tons, cost effect: 50,000 yen")

[0510] The above are the typical processing steps and their specific operations of the present invention, and show in detail how the system functions to help users achieve their environmental goals.

[0511] (Application example 2)

[0512] 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."

[0513] Setting environmental goals and creating annual plans based on them takes time and effort for many users, making it particularly difficult to maintain motivation. Furthermore, systems that ignore user emotions make it difficult to implement sustainable environmental measures, and their effectiveness is often limited. In particular, in the case of eco-driving in autonomous vehicles, the lack of feedback and assistance that takes user emotions into account makes it difficult to achieve goals such as improving fuel efficiency.

[0514] 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 saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for recognizing the user's emotions and providing feedback in real time, means for inputting driving data and generating an eco-driving plan, and means for providing feedback to improve motivation according to the user's emotional state. This makes it possible to implement effective environmental measures and eco-driving while maintaining the user's motivation.

[0515] "Environmental Goals" refers to specific targets or outcomes regarding environmental protection and sustainability set by a user.

[0516] "Annual Plan" refers to a plan that is implemented in stages throughout the year to achieve set environmental targets.

[0517] "Simulation results of reduction measures" refers to the predicted results obtained when simulating specific environmental measures or reduction measures.

[0518] "Carbon credit trading" refers to the transaction of buying and selling rights to reduce CO2 emissions.

[0519] "Subsidy Information" refers to detailed information about financial support provided for environmental and energy conservation projects.

[0520] An "emotion engine" refers to a system that analyzes a user's emotions and provides dynamic feedback based on those emotions.

[0521] "Real-time feedback" refers to feedback provided instantly in response to a user's input or actions.

[0522] "Driving data" refers to data related to the user's driving behavior and the vehicle's operating conditions.

[0523] An "eco-driving plan" refers to a driving method or plan that improves fuel efficiency.

[0524] "Motivational feedback" refers to encouragement or suggestions provided to motivate users.

[0525] This invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the invention. This system functions by combining a server, a terminal, and an emotion engine.

[0526] goal setting

[0527] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "improving fuel efficiency by 15% annually." The server receives this goal and stores it in a database. This saved goal data becomes the basis for future annual plans and simulations.

[0528] Creating an annual plan

[0529] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering driving data and current fuel consumption data. Once the user enters this data, the server uses AI technology to analyze the entered data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[0530] Prediction and reduction simulation

[0531] When a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to confirm the effect of introducing a new energy-saving driving method, they enter the details. The server uses AI to run the simulation based on this input data. The server then presents the simulation results, such as predicted fuel efficiency improvements and cost effects, to the user.

[0532] Sentiment Analysis and Feedback

[0533] The emotion engine analyzes the user's emotions in real time while the user is driving the vehicle. For example, if the user is feeling high stress, the server will send suggestions for relaxation methods and advice on safe driving. Conversely, if the user is maintaining motivation, the server will send encouraging messages. This helps keep the user highly motivated and helps them achieve their environmental goals.

[0534] Providing feedback

[0535] The server periodically analyzes the user's driving data and emotional data to evaluate their eco-driving achievement rate and areas for improvement. Based on this information, the server generates feedback and presents it to the user. For example, it may provide a message such as, "Your driving efficiency has improved by 10%. Keep it up next month!"

[0536] Examples and prompts

[0537] For example, if a user operates the app after a long drive and is judged to be "stressed," the server will suggest "5 minutes of deep breathing exercises." Also, if the user achieves their eco-driving goal, the server will display rewards such as points or coupons.

[0538] Example prompt sentence:

[0539] "When users set eco-driving goals, they input their daily driving data and the AI ​​will suggest the optimal driving plan. The emotion engine analyzes emotions in real time and provides appropriate feedback and suggestions."

[0540] This system is implemented using smartphones and vehicle infotainment systems. It uses the emotion engine for emotion analysis, the driving_data_analysis library for driving data analysis, and the feedback_system library for feedback provision. This allows users to maintain their motivation while driving efficiently and eco-friendly.

[0541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0542] Step 1:

[0543] A user logs into the system using a terminal and inputs their environmental goals. This input includes specific targets such as "improving fuel efficiency by 15% annually." The server receives this input data and stores it in a database. The input data is saved as target data and is used as the basis for subsequent annual plans and simulations.

[0544] Step 2:

[0545] The user accesses the system again and opens the annual plan creation screen. On this screen, they are prompted to enter driving data (mileage, fuel economy, etc.) according to a template. The server uses AI technology to analyze the entered driving data. Specifically, it uses the driving_data_analysis library to analyze the data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal.

[0546] Step 3:

[0547] When a user wants to simulate a specific reduction measure, they select the specific measure (e.g., introducing energy-saving driving methods) on the simulation screen and enter the necessary data. The server uses AI to run the simulation based on this input data. It predicts the energy-saving and cost-effectiveness effects from the input data and presents the results to the user.

[0548] Step 4:

[0549] While the user is driving the vehicle, the emotion engine analyzes the user's emotions in real time. For example, emotion analysis is performed using data collected by cameras and sensors. If the analysis shows that the user is feeling high stress, the server sends advice on relaxation methods and safe driving through a feedback system. Emotional state data is input in real time and output as the emotion analysis results.

[0550] Step 5:

[0551] The server periodically analyzes the user's driving data and emotional data. This analysis includes past driving records and emotional states. Based on this data, the server evaluates the user's eco-driving achievement rate and areas for improvement. The server analyzes the driving data using the driving_data_analysis library and the emotional data using the emotion_engine, and provides the results as feedback to the user.

[0552] Step 6:

[0553] The feedback system generates messages and suggestions to maintain or improve the user's motivation. For example, it sends a message such as, "Your driving efficiency has improved by 10%. Let's keep it up next month!" This increases the user's motivation and supports sustainable eco-driving.

[0554] Through the above steps, users can maintain their motivation while taking effective environmental measures and practicing eco-driving.

[0555] 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.

[0556] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0557] 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.

[0558] [Second embodiment]

[0559] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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).

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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."

[0571] The present invention provides an integrated system for taking effective measures against environmental problems. The following describes in detail the embodiments of the present invention. This system functions through the cooperation of a server, terminals, and users.

[0572] ---

[0573] 1. Goal Setting

[0574] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[0575] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[0576] ---

[0577] 2. Create an annual plan

[0578] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[0579] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[0580] ---

[0581] 3. Prediction and reduction simulation

[0582] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[0583] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[0584] ---

[0585] 4. Suggestion function

[0586] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[0587] The user can review these suggestions on the system and decide whether to incorporate them as appropriate.

[0588] ---

[0589] 5. Support for purchasing and selling carbon credits

[0590] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[0591] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[0592] ---

[0593] 6. Support for using subsidies

[0594] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[0595] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[0596] ---

[0597] This allows companies and public institutions to receive comprehensive support to efficiently and effectively achieve their environmental goals. The present invention is an extremely effective tool for comprehensively managing environmental measures.

[0598] The processing flow will be explained below.

[0599] ---

[0600] goal setting

[0601] Step 1:

[0602] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[0603] Step 2:

[0604] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[0605] Step 3:

[0606] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[0607] Step 4:

[0608] The server receives the input environmental goals and stores them in a database.

[0609] ---

[0610] Creating an annual plan

[0611] Step 1:

[0612] User selects the "Create Annual Plan" option from the dashboard.

[0613] Step 2:

[0614] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[0615] Step 3:

[0616] The user enters the necessary data according to the template and presses the send button.

[0617] Step 4:

[0618] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[0619] Step 5:

[0620] The user reviews the proposed annual plan and makes any necessary adjustments.

[0621] Step 6:

[0622] The server saves the revised plan as the final version in the database.

[0623] ---

[0624] Prediction and reduction simulation

[0625] Step 1:

[0626] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[0627] Step 2:

[0628] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[0629] Step 3:

[0630] The server runs a simulation based on the entered items and data.

[0631] Step 4:

[0632] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[0633] ---

[0634] Suggestion feature

[0635] Step 1:

[0636] The server continuously analyzes user-entered data, periodically analyzing it to detect patterns and trends.

[0637] Step 2:

[0638] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[0639] Step 3:

[0640] Users view the offers from their dashboard and view details about the offers that interest them.

[0641] Step 4:

[0642] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[0643] ---

[0644] Support for purchasing and selling carbon credits

[0645] Step 1:

[0646] User selects the "Carbon Credit Trading" option from the dashboard.

[0647] Step 2:

[0648] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[0649] Step 3:

[0650] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[0651] Step 4:

[0652] The user reviews the proposal and, if satisfied, executes the transaction.

[0653] ---

[0654] Support for using subsidies

[0655] Step 1:

[0656] The server collects the latest grant information and updates the database.

[0657] Step 2:

[0658] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[0659] Step 3:

[0660] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[0661] Step 4:

[0662] The user initiates the application process and uploads the required documents into the system.

[0663] Step 5:

[0664] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[0665] ---

[0666] This allows the system to assist companies and public institutions in achieving their environmental goals effectively and efficiently.

[0667] Example 1

[0668] 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."

[0669] Many current environmental management systems have separate functions, such as goal setting, annual plan creation, reduction simulation, transaction support, and provision of subsidy information, making it difficult to achieve overall efficiency and implement effective measures. Furthermore, due to a lack of automatic generation functions utilizing data analysis and AI technology, manual input and planning by users takes time and effort. The purpose of this invention is to solve these problems and provide a comprehensive system for achieving environmental goals effectively and efficiently.

[0670] 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.

[0671] In this invention, the server includes: means for saving input environmental goals; means for generating an annual plan based on the environmental goals; means for predicting simulation results of specified reduction measures; means for generating proposals necessary for environmental improvement; means for supporting carbon credit trading; means for collecting subsidy information and supporting application procedures; means for analyzing data input by a user and automatically generating an optimal annual plan using AI technology; means for acquiring real-time market data based on conditions specified by the user and proposing transaction timing; means for saving subsidy information in a database and acquiring necessary information based on search conditions to support applications; and means for analyzing the user's environmental data at regular intervals and generating new proposals. This allows for integrated management of the process from setting to achieving environmental goals, enabling users to take actions toward achieving their goals effectively and efficiently.

[0672] The "means for saving input environmental targets" is a function for receiving environmental target data input by the user and saving it in a database within the server.

[0673] The "means for generating annual plans based on environmental targets" is a function that uses AI technology to automatically generate optimal annual plans based on input environmental target data.

[0674] The "means for predicting the simulation results of the specified reduction measures" is a function that executes a simulation based on the reduction measures input by the user, predicts the results, and presents them.

[0675] The "means for generating proposals necessary for environmental improvement" is a function that analyzes data at regular intervals and automatically generates proposals for new environmental improvement measures and energy efficiency improvement measures.

[0676] "Means to support carbon credit trading" is a function that suggests optimal trading timing and trading partners to users who wish to buy or sell carbon credits.

[0677] The "means for collecting subsidy information and supporting application procedures" is a function that collects the latest subsidy information and supports users in smoothly proceeding with the subsidy application procedures.

[0678] "Means for analyzing data entered by the user and automatically generating an optimal annual plan using AI technology" refers to a function that analyzes data entered by the user and automatically generates an optimal annual plan based on that data using AI technology.

[0679] "Means for obtaining real-time market data based on conditions specified by the user and proposing trading timing" is a function for obtaining market data in real time based on conditions specified by the user and proposing optimal trading timing.

[0680] "Means for saving subsidy information in a database and for users to obtain the necessary information based on search criteria to assist with applications" refers to a function that saves subsidy information in a database, provides the necessary subsidy information based on search criteria entered by the user, and assists with applications.

[0681] The "means for periodically analyzing the user's environmental data and generating new proposals" is a function that periodically analyzes the user's environmental data and automatically generates new proposals based on that data.

[0682] This system invention functions through the cooperation of the server, the terminal, and the user. The program and processing of this system will be specifically described below.

[0683] goal setting

[0684] The user logs into the system using a terminal. On the goal setting screen displayed on the terminal, the user enters an environmental goal, such as "reducing CO2 emissions by 30% by 2025." When the user enters the goal and presses the send button, the server receives the data. The server stores this goal data in a database and uses it as the base data for future annual plans and simulations.

[0685] Creating an annual plan

[0686] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters this data and presses the submit button, the server receives the entered data, analyzes it using AI technology (for example, Scikit-learn or TensorFlow), and automatically generates an optimal annual plan. The proposed annual plan is presented to the user for approval.

[0687] Prediction and reduction simulation

[0688] When a user wants to simulate a reduction measure, they select the target item on the simulation screen. For example, if they want to check the effect of introducing new energy-saving equipment, they enter the detailed data. When the user enters the detailed data and presses the send button, the server runs a simulation using an AI model (for example, a Keras neural network) based on this data. The resulting predicted reduction amount and cost effect are presented to the user.

[0689] Suggestion feature

[0690] The server periodically analyzes the user's environmental data and generates new proposals. For example, it may propose an environmental awareness program for employees or new energy efficiency improvements based on the data analysis. These proposals are presented to the user on the system, and the user can decide whether to implement the proposals as needed.

[0691] Support for purchasing and selling carbon credits

[0692] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen. The server obtains market data in real time and proposes the optimal trading timing and trading partner for the user's needs. The user can then proceed with the specific transaction based on this proposal.

[0693] Support for using subsidies

[0694] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen. The server provides subsidy information that matches the user's criteria and assists the user in applying for a subsidy. The user can upload the necessary documents through the system and apply for a subsidy.

[0695] Specific examples and examples of prompts to input to the generative AI model

[0696] Examples:

[0697] Goal setting: "The user enters their goal, such as 'reduce CO2 emissions by 30% by 2025,' into a form on their device and saves it on the server."

[0698] Creating an annual plan: "The user inputs energy consumption data, and the server analyzes the data and generates an optimal annual plan."

[0699] Energy Reduction Simulation: "Users can simulate the introduction of new energy-saving equipment, enter detailed data to confirm the effects, and then press the submit button. The server then uses AI to present the simulation results."

[0700] Example prompt for a generative AI model:

[0701] "Please explain the process flow of your system to support goal setting."

[0702] "Please tell me the detailed steps for the system that generates an annual plan based on the environmental goals entered by the user."

[0703] "Please detail the operation of the system to simulate the CO2 reduction effect of introducing new energy-saving equipment."

[0704] This system is highly effective as an integrated support tool for companies and public institutions to efficiently achieve their environmental goals. By utilizing specific data processing and AI technology, users can quickly and accurately obtain the information and support they need.

[0705] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0706] goal setting

[0707] Step 1:

[0708] The user starts up the terminal and logs in to the system. They enter their user ID and password on the login screen and press the submit button. Based on this input, the server retrieves the user information from the database and performs authentication processing. If authentication is successful, the user's dashboard is displayed.

[0709] Step 2:

[0710] The user accesses the goal setting screen from the dashboard, enters an environmental goal such as "reduce CO2 emissions by 30% by 2025," and presses the submit button. The server receives this input data and executes an SQL query to save it in the database.

[0711] Creating an annual plan

[0712] Step 3:

[0713] The user accesses the annual plan creation screen. The annual plan creation screen displays a template for entering energy consumption and current CO2 emission data. The user enters this data and presses the submit button. The server receives the input data and saves it in the database.

[0714] Step 4:

[0715] The server invokes an AI model (such as Scikit-learn or TensorFlow) based on the stored data and performs data analysis. This analysis generates an optimal annual plan. The generated annual plan is converted into an appropriate format for presentation to the user and displayed on a web page.

[0716] Step 5:

[0717] The user checks the presented annual plan and gives feedback on approval or correction. When the user clicks the approval button, the server records the approval and proceeds to the next step.

[0718] Prediction and reduction simulation

[0719] Step 6:

[0720] The user accesses the simulation screen, selects the item to be simulated, enters data to confirm the effect of introducing new energy-saving equipment, for example, and presses the send button. The server receives this data and runs the simulation using an AI model (e.g., Keras).

[0721] Step 7:

[0722] The server generates simulation results, calculates predicted savings and cost benefits, and formats and displays these results on a web page for presentation to the user.

[0723] Suggestion feature

[0724] Step 8:

[0725] The server periodically retrieves and analyzes environmental data from the database. It then applies machine learning algorithms such as clustering and regression analysis to generate new proposals. These proposals include specific content, such as "new energy efficiency improvements to reduce energy consumption by 10%."

[0726] Step 9:

[0727] The generated suggestions are displayed to the user, who can then review them and decide whether to implement them. Once the user clicks the "Implement" button, the server records the information and uses it in the next improvement cycle.

[0728] Support for purchasing and selling carbon credits

[0729] Step 10:

[0730] A user accesses the trading screen and enters the quantity and price of carbon credits they wish to buy or sell. The server receives the input data and calls an API to obtain market data in real time.

[0731] Step 11:

[0732] The server calculates the optimal trading timing and counterparty based on market data and makes a proposal to the user. When this proposal is displayed and the user clicks the trade button, the server records the trading information and proceeds with the actual trade.

[0733] Support for using subsidies

[0734] Step 12:

[0735] The server periodically collects the latest subsidy information from the subsidy information API and stores it in a database. Based on this database information, the user enters the conditions on the subsidy search screen and presses the submit button.

[0736] Step 13:

[0737] The server extracts and presents subsidy information that matches the user's search criteria. Once the user finds the subsidy information they need, they upload their application documents and click the apply button. The server records this information and sends it to the grant agency.

[0738] Through the above processing steps, the system is able to provide comprehensive support for users to achieve their environmental goals efficiently and effectively.

[0739] (Application example 1)

[0740] 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."

[0741] Currently, many factories are setting environmental goals and trying to manage energy consumption based on them, but they face difficulties in generating annual plans, predicting reduction measures, running simulations, and proposing new energy-saving measures. Furthermore, trading carbon credits and utilizing subsidies require real-time data analysis and quick decision-making, which requires specialized knowledge and skills, and many factories are slow to adopt these approaches. Therefore, an integrated system is needed to improve the energy efficiency of the entire factory and effectively achieve environmental goals.

[0742] 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.

[0743] In this invention, the server includes means for saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing subsidy information to users, means for controlling robots to optimize energy efficiency in the factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time, thereby making it possible to improve the energy efficiency of the entire factory and effectively achieve the environmental targets.

[0744] "Environmental goals" are targets set by users for reducing energy consumption and CO2 emissions at facilities such as factories.

[0745] An "annual plan" is a detailed schedule for implementing specific action plans throughout the year to achieve set environmental targets.

[0746] "Abatement measures" are specific measures and methods implemented to reduce energy consumption and mitigate environmental impact.

[0747] "Simulation results" are the results of predicting and analyzing the effects of the set reduction measures if they are actually implemented.

[0748] "Proposals" are specific action plans and measures generated by the server to help achieve environmental goals.

[0749] "Carbon credit trading" is a market transaction that aims to reduce environmental impact by buying and selling reduced CO2 emissions.

[0750] "Subsidy information" refers to information on fundraising provided to support environmental improvement and energy efficiency improvements.

[0751] "Factory environment" refers to the overall situation and conditions regarding production equipment and energy consumption within a factory.

[0752] "Energy efficiency" refers to the ability to effectively utilize energy resources, and means reducing wasteful energy consumption and operating efficiently.

[0753] "Robot control" refers to the operation and supervision of robots deployed within a factory to manage and optimize their movements and operations.

[0754] "Energy-saving equipment" refers to facilities and devices that consume less energy than conventional equipment and can reduce the burden on the environment.

[0755] A "generative AI model" is an algorithm or program that allows artificial intelligence to generate optimal plans and proposals on its own based on data entered by the user.

[0756] "Real-time data analysis" is a technology that instantly analyzes ongoing data and outputs results immediately.

[0757] "Energy saving measures" refer to specific methods and measures for reducing energy consumption.

[0758] The present invention provides an integrated system for optimizing energy efficiency and achieving environmental goals within a factory, which functions through the cooperation of a server, terminals, and users.

[0759] The server includes means for storing input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting the simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting subsidy information and providing it to users, means for controlling robots to optimize energy efficiency in a factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time.

[0760] An embodiment of this system will now be described in detail.

[0761] First, the user logs into the system using a terminal and inputs their environmental goals. For example, they can set a specific goal such as "reducing CO2 emissions by 30% by 2025." The server receives these goals and stores them in a database. The saved goal data serves as the basis for future annual plans and simulations.

[0762] Next, the user accesses the system again and opens the annual plan creation screen. A template for inputting energy consumption and current CO2 emission data is displayed, and the user enters this data. The server analyzes the input data using a generative AI model (e.g., TensorFlow or PyTorch) and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[0763] Furthermore, if a user wants to simulate the effects of introducing new energy-saving equipment, they can enter details on the simulation screen, such as the annual energy savings of the new energy-saving equipment. Based on this input data, the server uses a generative AI model to run the simulation and presents the results, such as predicted energy savings and cost effectiveness, to the user.

[0764] The server also continuously analyzes the user's environmental data and generates new proposals. For example, based on periodic data analysis, it makes effective proposals such as environmental awareness programs for employees and new energy efficiency improvement measures. The user can review these proposals on the system and decide whether to incorporate them as appropriate.

[0765] The system also supports the use of carbon credit trading and subsidy information. When a user wishes to buy or sell carbon credits, they enter the quantity and price on the system's trading screen. The server obtains market data in real time and suggests the optimal trading timing and trading partner. For subsidy information, the server also collects the latest information, provides information that matches the search criteria, and supports the application process.

[0766] In this way, it is possible to increase the energy efficiency of the entire factory and help to effectively achieve environmental goals.

[0767] As a concrete example, consider a factory manager who has set a goal of reducing CO2 emissions by 30% by 2025. The manager enters the following:

[0768] Example prompt sentence:

[0769] To reduce your factory's CO2 emissions by 30%, use AI to generate an annual plan based on the following data:

[0770] 1. Target year: 2025

[0771] 2. Current energy consumption: 1000 kWh / month

[0772] 3. Current CO2 emissions: 300 kg / month

[0773] 4. Data for new energy-saving devices: 200 kWh saved per year

[0774] Based on these, we will provide you with the best annual plan.

[0775] Based on this prompt, the generative AI model generates an annual plan and presents suggestions to the user.

[0776] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0777] Step 1:

[0778] Users log in to the system using a terminal and input their environmental goals. For example, they can set a goal such as "reducing CO2 emissions by 30% by 2025." The input environmental goals are sent to the server, which receives this goal data and stores it in a database.

[0779] Step 2:

[0780] The user accesses the system again from their terminal and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters and submits this data, the server receives it. The received data is stored in a database.

[0781] Step 3:

[0782] The server analyzes the stored environmental targets and energy consumption data using a generative AI model (e.g., TensorFlow or PyTorch). Based on the analysis results, an annual plan is automatically generated. The generated annual plan is sent to the user's device as a proposal. The user reviews the presented annual plan and decides whether to approve it.

[0783] Step 4:

[0784] When a user wants to simulate the effects of introducing new energy-saving equipment, they enter detailed data on the simulation screen and submit it. For example, they can enter the annual energy savings of the new energy-saving equipment. The server receives this data and runs the simulation using a generative AI model. The simulation results, such as predicted energy savings and cost effects, are then sent to the user's device.

[0785] Step 5:

[0786] The server continuously analyzes the user's environmental data and generates new energy-saving suggestions. Based on the results of periodic data analysis, the server creates useful suggestions, such as environmental awareness programs for employees or new energy efficiency improvements. The generated suggestions are sent to the user's device. The user reviews the suggestions and decides whether to implement them.

[0787] Step 6:

[0788] When a user wishes to buy or sell carbon credits, they enter the desired quantity and price on the system's trading screen and submit it. The server receives the entered data and obtains market data in real time. It then proposes the optimal trading timing and trading partner. The proposal is sent to the user's device, and the user proceeds with the transaction based on it.

[0789] Step 7:

[0790] When a user wants to search for subsidy information, they enter their search criteria on the system's search screen and submit it. The server searches the database for the latest subsidy information based on the entered criteria. Subsidy information that matches the criteria is sent to the user's device, and the user can use that information to apply for a subsidy.

[0791] 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.

[0792] The present invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the present invention. This system functions by combining a server, a terminal, and an emotion engine.

[0793] ---

[0794] goal setting

[0795] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[0796] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[0797] ---

[0798] Creating an annual plan

[0799] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[0800] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[0801] ---

[0802] Prediction and reduction simulation

[0803] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[0804] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[0805] ---

[0806] Suggestion feature

[0807] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[0808] Furthermore, the emotion engine analyzes the user's input and behavioral data to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is losing motivation for the plan, the server generates additional suggestions and encouraging messages to improve the user's motivation.

[0809] ---

[0810] Support for purchasing and selling carbon credits

[0811] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[0812] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[0813] ---

[0814] Support for using subsidies

[0815] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[0816] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[0817] ---

[0818] Combining Emotion Engines

[0819] As users go through steps such as goal setting, annual planning, simulations, proposals, carbon credit trading, and subsidy information viewing, the emotion engine analyzes their input in real time to recognize emotions such as stress, excitement, or anxiety.

[0820] Based on the results of the emotion engine, the server automatically responds appropriately to the user's situation. For example, if it determines that the user's motivation is declining, it displays positive feedback and encouraging messages to improve motivation.

[0821] ---

[0822] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby enabling companies and public institutions to achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[0823] The processing flow will be explained below.

[0824] ---

[0825] goal setting

[0826] Step 1:

[0827] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[0828] Step 2:

[0829] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[0830] Step 3:

[0831] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[0832] Step 4:

[0833] The emotion engine analyzes the user's behavioral patterns and typing speed while they are typing to determine their emotional state. For example, if they type smoothly, it will sense "normal," but if their tempo is too fast, it will sense "impatience."

[0834] Step 5:

[0835] The server receives the user's emotion data and goal data and stores them in a database.

[0836] ---

[0837] Creating an annual plan

[0838] Step 1:

[0839] User selects the "Create Annual Plan" option from the dashboard.

[0840] Step 2:

[0841] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[0842] Step 3:

[0843] The user enters the necessary data according to the template and presses the send button.

[0844] Step 4:

[0845] The emotion engine captures the user's emotional state while they are typing, and if stress or anxiety is detected, the server displays a comforting or encouraging message.

[0846] Step 5:

[0847] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[0848] Step 6:

[0849] The user reviews the proposed annual plan and makes any necessary adjustments.

[0850] Step 7:

[0851] The server saves the revised plan as the final version in the database.

[0852] ---

[0853] Prediction and reduction simulation

[0854] Step 1:

[0855] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[0856] Step 2:

[0857] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[0858] Step 3:

[0859] The emotion engine captures the user's emotional state and displays reminder messages if they are slow to type.

[0860] Step 4:

[0861] The server runs a simulation based on the entered items and data.

[0862] Step 5:

[0863] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[0864] Step 6:

[0865] The emotion engine monitors the user's emotions regarding the simulation results and displays additional suggestions if satisfaction is low.

[0866] ---

[0867] Suggestion feature

[0868] Step 1:

[0869] The server continuously analyzes your environment data, periodically analyzing it to detect patterns and trends.

[0870] Step 2:

[0871] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[0872] Step 3:

[0873] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly. For example, if the user is feeling stressed, simple and effective suggestions will be prioritized.

[0874] Step 4:

[0875] Users view the offers from their dashboard and view details about the offers that interest them.

[0876] Step 5:

[0877] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[0878] ---

[0879] Support for purchasing and selling carbon credits

[0880] Step 1:

[0881] User selects the "Carbon Credit Trading" option from the dashboard.

[0882] Step 2:

[0883] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[0884] Step 3:

[0885] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[0886] Step 4:

[0887] The emotion engine analyzes the user's emotional state before trading, and if the user feels unsure, it displays information to build confidence and past success stories.

[0888] Step 5:

[0889] The user reviews the proposal and, if satisfied, executes the transaction.

[0890] ---

[0891] Support for using subsidies

[0892] Step 1:

[0893] The server collects the latest grant information and updates the database.

[0894] Step 2:

[0895] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[0896] Step 3:

[0897] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[0898] Step 4:

[0899] The user initiates the application process and uploads the required documents into the system.

[0900] Step 5:

[0901] The emotion engine analyzes the user's emotional state during the application process and displays a guide to simplify the process if the user is confused.

[0902] Step 6:

[0903] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[0904] ---

[0905] This processing flow enables the system, combined with the emotion engine, to grasp the user's emotional state in real time and provide appropriate support and suggestions, helping companies and public institutions achieve their environmental goals more efficiently and effectively.

[0906] Example 2

[0907] 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."

[0908] The purpose of this invention is to improve user motivation throughout the process, from setting environmental goals to managing their achievement, and to realize more effective and sustainable environmental measures. Conventional systems lack the means to respond to fluctuations in users' emotions and motivation, which leads to a high likelihood of users giving up midway through the plan. Furthermore, functions such as setting environmental goals, creating annual plans, simulating reductions, trading carbon credits, and providing subsidy information are not integrated, which makes it difficult to provide consistent support.

[0909] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0910] In this invention, the server includes means for saving environmental goals entered by the user, means for automatically generating an annual plan based on the saved environmental goals, means for predicting the results of a simulation of reduction measures specified by the user, means for continuously generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing the latest subsidy information to the user, means for analyzing the user's emotional state in real time, and means for providing feedback to improve the user's motivation based on the analysis results, thereby enabling effective support for achieving environmental goals that takes the user's emotional state into consideration.

[0911] "Environmental goals" are specific targets set by the user for environmental protection and improvement.

[0912] An "annual plan" is a plan that indicates specific measures and actions to be implemented within a year based on the environmental goals that have been saved.

[0913] "Reduction measures" are specific measures to reduce environmental impact, such as reducing energy consumption and CO2 emissions.

[0914] A "simulation" is a mock experiment to predict the results of implementing specified reduction measures.

[0915] "Proposals" are measures and actions that are necessary to improve the environment that are presented to the user.

[0916] A "carbon credit" is a tradable unit that represents the amount of greenhouse gas emissions reduction, such as CO2.

[0917] "Subsidy information" refers to information for receiving financial support to implement environmental measures.

[0918] "Saving" is the act of writing data or information entered by a user into a database.

[0919] "Analysis" refers to the act of analyzing and evaluating data based on user input and environmental data.

[0920] "Continuous generation" is the process of periodically analyzing data and generating new suggestions and information.

[0921] "Real time" means that processing is executed the instant the user performs an operation or input.

[0922] "Emotional state" refers to the user's mental and psychological state.

[0923] "Motivation" refers to the user's willingness and motivation to achieve environmental goals.

[0924] "Feedback" refers to information or actions given in response to a user's actions or state.

[0925] The present invention provides a system that recognizes a user's emotions and supports the process from setting environmental goals to managing their achievement accordingly. The system functions by combining a server, a terminal, and an emotion engine. Hereinafter, embodiments of the present invention will be described in detail.

[0926] Hardware and software used

[0927] Server: A physical or virtual computer with high-performance data processing capabilities.

[0928] Terminal: User input device such as a personal computer, smartphone, or tablet.

[0929] Database: Data management systems such as MySQL, PostgreSQL, etc.

[0930] Emotion Engine: Uses AI technology to analyze user emotions

[0931] Generative AI models: Artificial intelligence techniques used to analyze environmental data and generate recommendations

[0932] Detailed Description

[0933] 1. Goal Setting

[0934] User action: The user logs in to the system using a terminal. After logging in, they enter a specific environmental goal into the form on the screen, such as "reducing CO2 emissions by 30% by 2025."

[0935] Server operation: The server receives this goal and stores it in a database. The saved goal data will be used as the basis for future annual planning and simulations.

[0936] 2. Create an annual plan

[0937] User action: The user opens the annual planning screen and enters data such as energy consumption of 500 kWh and current CO2 emissions of 100 tonnes.

[0938] Server operation: The server analyzes the input data using a generative AI model and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[0939] 3. Prediction and reduction simulation

[0940] User action: If the user wants to check the effect of introducing new energy-saving equipment on the simulation screen, he or she enters the details.

[0941] Server operation: The server runs a simulation using a generative AI model based on the input data, and presents the simulation results, such as predicted reductions and cost effectiveness, to the user.

[0942] 4. Suggestion function

[0943] Server operation: The server continuously analyzes the user's environmental data and generates new suggestions, such as environmental awareness programs for employees or new energy efficiency improvements based on data analysis.

[0944] How the emotion engine works: It analyzes user input and behavioral data to recognize the user's emotional state. For example, if the server recognizes that the user is losing motivation for the plan, it will generate additional suggestions or encouraging messages.

[0945] 5. Support for purchasing and selling carbon credits

[0946] User action: The user enters the desired amount and price on the carbon credit trading screen.

[0947] Server operation: The server obtains market data in real time and proposes optimal trading timing and trading partners according to the user's preferences.

[0948] 6. Support for using subsidies

[0949] Server operation: The server collects the latest grant information and stores it in a database.

[0950] User action: The user enters criteria on the grant information search screen.

[0951] Server operation: The server provides information on grants that match the user's requirements and assists with the application process.

[0952] Examples and prompts

[0953] Example 1: Setting environmental goals

[0954] Input: "Reduce CO2 emissions by 30% by 2025"

[0955] Process description: The user inputs a goal into the terminal, and the server stores the goal in the database.

[0956] Example 2: Creating an annual plan

[0957] Input: "Energy consumption: 500kWh, Current CO2 emissions: 100 tons"

[0958] Process description: The user enters data into the annual plan creation screen, and the server uses AI to generate and propose the optimal annual plan.

[0959] Example 3: Using an Emotion Engine

[0960] Input volume: "I get stressed when things don't go as planned."

[0961] Process description: The emotion engine analyzes the input data and generates encouraging messages and new suggestions to reduce the user's stress.

[0962] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby helping companies and public institutions achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[0963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0964] Goal setting process flow

[0965] Step 1: User Login

[0966] Input: Username and Password

[0967] Specific behavior:

[0968] 1. A user accesses the system at a terminal and enters their username and password on the login screen.

[0969] 2. The server receives the entered username and password.

[0970] Data processing and calculation:

[0971] The server compares the input password with the hashed password in its database to see if they match.

[0972] Output: If authentication is successful, transition to the main screen. If authentication fails, display an error message.

[0973] Step 2: Enter your goals

[0974] Enter: "Cut CO2 emissions by 30% by 2025."

[0975] Specific behavior:

[0976] 1. After the user logs in, they will be taken to the goal setting screen.

[0977] 2. A user types "Reduce CO2 emissions by 30% by 2025" into a text box.

[0978] 3. The user clicks the "Submit" button.

[0979] Data processing and calculation:

[0980] The server receives the input, checks the format of the data, and converts it if necessary, for example checking the date format or numbers.

[0981] Output: The converted target data is sent to the server.

[0982] Step 3: Save your goal

[0983] Input: Transformed target data

[0984] Specific behavior:

[0985] 1. The server saves the goal data in a database.

[0986] 2. Metadata such as goal ID, user ID, and setting date and time are also stored in the database.

[0987] Data processing and calculation:

[0988] Convert the data format to SQL and insert it into the database.

[0989] Output: Goal setting completion message

[0990] Annual plan creation process flow

[0991] Step 1: Access the user's annual plan screen

[0992] Input: None (accessed by screen transition)

[0993] Specific behavior:

[0994] 1. The user opens the annual plan creation screen on their device.

[0995] 2. The planning template will be displayed.

[0996] Step 2: Enter your data

[0997] Input: "Energy consumption: 500kWh" "Current CO2 emissions: 100 tons"

[0998] Specific behavior:

[0999] 1. The user enters energy consumption of 500 kWh or current CO2 emissions of 100 tons into the template.

[1000] 2. After entering the information, the user clicks the "Submit" button.

[1001] Data processing and calculation:

[1002] The server receives the data, checks the format, and checks whether it is a numeric format.

[1003] Output: Validated data is passed to the server.

[1004] Step 3: Automatically generate annual plans

[1005] Input: Validated Data

[1006] Specific behavior:

[1007] 1. The server analyzes the verified data using an AI model.

[1008] 2. The model generates an optimal annual plan.

[1009] Data processing and calculation:

[1010] A generative AI model analyzes input data and derives recommended measures and action plans.

[1011] Output: Generated annual planning data

[1012] Step 4: Annual plan presentation and approval

[1013] Input: Generated annual plan data

[1014] Specific behavior:

[1015] 1. The server presents the generated annual plan to the user.

[1016] 2. The user reviews the plan and, if satisfied, clicks the "Approve" button.

[1017] Data processing and calculation:

[1018] Add approval data and finalize the annual plan.

[1019] Output: Confirmed annual plan

[1020] Processing flow for prediction and reduction simulation

[1021] Step 1: Open the simulation screen

[1022] Input: None (accessed by screen transition)

[1023] Specific behavior:

[1024] 1. The user opens the simulation screen on their device.

[1025] 2. A menu will appear for selecting the items to be simulated.

[1026] Step 2: Selecting simulation items and entering data

[1027] Input: "Installation of new energy-efficient equipment"

[1028] Specific behavior:

[1029] 1. The user selects the items to be simulated and enters the necessary data, such as "power consumption of new equipment," "installation cost," and "estimated lifespan."

[1030] 2. The user clicks the "Simulate" button.

[1031] Data processing and calculation:

[1032] The server receives the input data and performs format validation and format conversion.

[1033] Output: The transformed input data

[1034] Step 3: Run the simulation and present the results

[1035] Input: The converted input data

[1036] Specific behavior:

[1037] 1. The server runs a simulation using the AI ​​model.

[1038] 2. A generative AI model calculates the projected savings and cost benefits.

[1039] 3. The server presents the calculation results to the user.

[1040] Data processing and calculation:

[1041] Calculate the predicted reductions, cost effectiveness, etc. and compile the results.

[1042] Output: Simulation result data (e.g., "Predicted reduction: 20 tons, cost effect: 50,000 yen")

[1043] The above are the typical processing steps and their specific operations of the present invention, and show in detail how the system functions to help users achieve their environmental goals.

[1044] (Application example 2)

[1045] 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."

[1046] Setting environmental goals and creating annual plans based on them takes time and effort for many users, making it particularly difficult to maintain motivation. Furthermore, systems that ignore user emotions make it difficult to implement sustainable environmental measures, and their effectiveness is often limited. In particular, in the case of eco-driving in autonomous vehicles, the lack of feedback and assistance that takes user emotions into account makes it difficult to achieve goals such as improving fuel efficiency.

[1047] 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 saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for recognizing the user's emotions and providing feedback in real time, means for inputting driving data and generating an eco-driving plan, and means for providing feedback to improve motivation according to the user's emotional state. This makes it possible to implement effective environmental measures and eco-driving while maintaining the user's motivation.

[1048] "Environmental Goals" refers to specific targets or outcomes regarding environmental protection and sustainability set by a user.

[1049] "Annual Plan" refers to a plan that is implemented in stages throughout the year to achieve set environmental targets.

[1050] "Simulation results of reduction measures" refers to the predicted results obtained when simulating specific environmental measures or reduction measures.

[1051] "Carbon credit trading" refers to the transaction of buying and selling rights to reduce CO2 emissions.

[1052] "Subsidy Information" refers to detailed information about financial support provided for environmental and energy conservation projects.

[1053] An "emotion engine" refers to a system that analyzes a user's emotions and provides dynamic feedback based on those emotions.

[1054] "Real-time feedback" refers to feedback provided instantly in response to a user's input or actions.

[1055] "Driving data" refers to data related to the user's driving behavior and the vehicle's operating conditions.

[1056] An "eco-driving plan" refers to a driving method or plan that improves fuel efficiency.

[1057] "Motivational feedback" refers to encouragement or suggestions provided to motivate users.

[1058] This invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the invention. This system functions by combining a server, a terminal, and an emotion engine.

[1059] goal setting

[1060] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "improving fuel efficiency by 15% annually." The server receives this goal and stores it in a database. This saved goal data becomes the basis for future annual plans and simulations.

[1061] Creating an annual plan

[1062] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering driving data and current fuel consumption data. Once the user enters this data, the server uses AI technology to analyze the entered data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[1063] Prediction and reduction simulation

[1064] When a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to confirm the effect of introducing a new energy-saving driving method, they enter the details. The server uses AI to run the simulation based on this input data. The server then presents the simulation results, such as predicted fuel efficiency improvements and cost effects, to the user.

[1065] Sentiment Analysis and Feedback

[1066] The emotion engine analyzes the user's emotions in real time while the user is driving the vehicle. For example, if the user is feeling high stress, the server will send suggestions for relaxation methods and advice on safe driving. Conversely, if the user is maintaining motivation, the server will send encouraging messages. This helps keep the user highly motivated and helps them achieve their environmental goals.

[1067] Providing feedback

[1068] The server periodically analyzes the user's driving data and emotional data to evaluate their eco-driving achievement rate and areas for improvement. Based on this information, the server generates feedback and presents it to the user. For example, it may provide a message such as, "Your driving efficiency has improved by 10%. Keep it up next month!"

[1069] Examples and prompts

[1070] For example, if a user operates the app after a long drive and is judged to be "stressed," the server will suggest "5 minutes of deep breathing exercises." Also, if the user achieves their eco-driving goal, the server will display rewards such as points or coupons.

[1071] Example prompt sentence:

[1072] "When users set eco-driving goals, they input their daily driving data and the AI ​​will suggest the optimal driving plan. The emotion engine analyzes emotions in real time and provides appropriate feedback and suggestions."

[1073] This system is implemented using smartphones and vehicle infotainment systems. It uses the emotion engine for emotion analysis, the driving_data_analysis library for driving data analysis, and the feedback_system library for feedback provision. This allows users to maintain their motivation while driving efficiently and eco-friendly.

[1074] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1075] Step 1:

[1076] A user logs into the system using a terminal and inputs their environmental goals. This input includes specific targets such as "improving fuel efficiency by 15% annually." The server receives this input data and stores it in a database. The input data is saved as target data and is used as the basis for subsequent annual plans and simulations.

[1077] Step 2:

[1078] The user accesses the system again and opens the annual plan creation screen. On this screen, they are prompted to enter driving data (mileage, fuel economy, etc.) according to a template. The server uses AI technology to analyze the entered driving data. Specifically, it uses the driving_data_analysis library to analyze the data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal.

[1079] Step 3:

[1080] When a user wants to simulate a specific reduction measure, they select the specific measure (e.g., introducing energy-saving driving methods) on the simulation screen and enter the necessary data. The server uses AI to run the simulation based on this input data. It predicts the energy-saving and cost-effectiveness effects from the input data and presents the results to the user.

[1081] Step 4:

[1082] While the user is driving the vehicle, the emotion engine analyzes the user's emotions in real time. For example, emotion analysis is performed using data collected by cameras and sensors. If the analysis shows that the user is feeling high stress, the server sends advice on relaxation methods and safe driving through a feedback system. Emotional state data is input in real time and output as the emotion analysis results.

[1083] Step 5:

[1084] The server periodically analyzes the user's driving data and emotional data. This analysis includes past driving records and emotional states. Based on this data, the server evaluates the user's eco-driving achievement rate and areas for improvement. The server analyzes the driving data using the driving_data_analysis library and the emotional data using the emotion_engine, and provides the results as feedback to the user.

[1085] Step 6:

[1086] The feedback system generates messages and suggestions to maintain or improve the user's motivation. For example, it sends a message such as, "Your driving efficiency has improved by 10%. Let's keep it up next month!" This increases the user's motivation and supports sustainable eco-driving.

[1087] Through the above steps, users can maintain their motivation while taking effective environmental measures and practicing eco-driving.

[1088] 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.

[1089] 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.

[1090] 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.

[1091] [Third embodiment]

[1092] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1093] 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.

[1094] 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).

[1095] 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.

[1096] 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.

[1097] 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).

[1098] 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.

[1099] 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.

[1100] 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.

[1101] 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.

[1102] 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.

[1103] 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."

[1104] The present invention provides an integrated system for taking effective measures against environmental problems. The following describes in detail the embodiments of the present invention. This system functions through the cooperation of a server, terminals, and users.

[1105] ---

[1106] 1. Goal Setting

[1107] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[1108] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[1109] ---

[1110] 2. Create an annual plan

[1111] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[1112] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[1113] ---

[1114] 3. Prediction and reduction simulation

[1115] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[1116] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[1117] ---

[1118] 4. Suggestion function

[1119] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[1120] The user can review these suggestions on the system and decide whether to incorporate them as appropriate.

[1121] ---

[1122] 5. Support for purchasing and selling carbon credits

[1123] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[1124] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[1125] ---

[1126] 6. Support for using subsidies

[1127] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[1128] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[1129] ---

[1130] This allows companies and public institutions to receive comprehensive support to efficiently and effectively achieve their environmental goals. The present invention is an extremely effective tool for comprehensively managing environmental measures.

[1131] The processing flow will be explained below.

[1132] ---

[1133] goal setting

[1134] Step 1:

[1135] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[1136] Step 2:

[1137] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[1138] Step 3:

[1139] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[1140] Step 4:

[1141] The server receives the input environmental goals and stores them in a database.

[1142] ---

[1143] Creating an annual plan

[1144] Step 1:

[1145] User selects the "Create Annual Plan" option from the dashboard.

[1146] Step 2:

[1147] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[1148] Step 3:

[1149] The user enters the necessary data according to the template and presses the send button.

[1150] Step 4:

[1151] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[1152] Step 5:

[1153] The user reviews the proposed annual plan and makes any necessary adjustments.

[1154] Step 6:

[1155] The server saves the revised plan as the final version in the database.

[1156] ---

[1157] Prediction and reduction simulation

[1158] Step 1:

[1159] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[1160] Step 2:

[1161] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[1162] Step 3:

[1163] The server runs a simulation based on the entered items and data.

[1164] Step 4:

[1165] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[1166] ---

[1167] Suggestion feature

[1168] Step 1:

[1169] The server continuously analyzes user-entered data, periodically analyzing it to detect patterns and trends.

[1170] Step 2:

[1171] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[1172] Step 3:

[1173] Users view the offers from their dashboard and view details about the offers that interest them.

[1174] Step 4:

[1175] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[1176] ---

[1177] Support for purchasing and selling carbon credits

[1178] Step 1:

[1179] User selects the "Carbon Credit Trading" option from the dashboard.

[1180] Step 2:

[1181] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[1182] Step 3:

[1183] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[1184] Step 4:

[1185] The user reviews the proposal and, if satisfied, executes the transaction.

[1186] ---

[1187] Support for using subsidies

[1188] Step 1:

[1189] The server collects the latest grant information and updates the database.

[1190] Step 2:

[1191] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[1192] Step 3:

[1193] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[1194] Step 4:

[1195] The user initiates the application process and uploads the required documents into the system.

[1196] Step 5:

[1197] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[1198] ---

[1199] This allows the system to assist companies and public institutions in achieving their environmental goals effectively and efficiently.

[1200] Example 1

[1201] 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."

[1202] Many current environmental management systems have separate functions, such as goal setting, annual plan creation, reduction simulation, transaction support, and provision of subsidy information, making it difficult to achieve overall efficiency and implement effective measures. Furthermore, due to a lack of automatic generation functions utilizing data analysis and AI technology, manual input and planning by users takes time and effort. The purpose of this invention is to solve these problems and provide a comprehensive system for achieving environmental goals effectively and efficiently.

[1203] 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.

[1204] In this invention, the server includes: means for saving input environmental goals; means for generating an annual plan based on the environmental goals; means for predicting simulation results of specified reduction measures; means for generating proposals necessary for environmental improvement; means for supporting carbon credit trading; means for collecting subsidy information and supporting application procedures; means for analyzing data input by a user and automatically generating an optimal annual plan using AI technology; means for acquiring real-time market data based on conditions specified by the user and proposing transaction timing; means for saving subsidy information in a database and acquiring necessary information based on search conditions to support applications; and means for analyzing the user's environmental data at regular intervals and generating new proposals. This allows for integrated management of the process from setting to achieving environmental goals, enabling users to take actions toward achieving their goals effectively and efficiently.

[1205] The "means for saving input environmental targets" is a function for receiving environmental target data input by the user and saving it in a database within the server.

[1206] The "means for generating annual plans based on environmental targets" is a function that uses AI technology to automatically generate optimal annual plans based on input environmental target data.

[1207] The "means for predicting the simulation results of the specified reduction measures" is a function that executes a simulation based on the reduction measures input by the user, predicts the results, and presents them.

[1208] The "means for generating proposals necessary for environmental improvement" is a function that analyzes data at regular intervals and automatically generates proposals for new environmental improvement measures and energy efficiency improvement measures.

[1209] "Means to support carbon credit trading" is a function that suggests optimal trading timing and trading partners to users who wish to buy or sell carbon credits.

[1210] The "means for collecting subsidy information and supporting application procedures" is a function that collects the latest subsidy information and supports users in smoothly proceeding with the subsidy application procedures.

[1211] "Means for analyzing data entered by the user and automatically generating an optimal annual plan using AI technology" refers to a function that analyzes data entered by the user and automatically generates an optimal annual plan based on that data using AI technology.

[1212] "Means for obtaining real-time market data based on conditions specified by the user and proposing trading timing" is a function for obtaining market data in real time based on conditions specified by the user and proposing optimal trading timing.

[1213] "Means for saving subsidy information in a database and for users to obtain the necessary information based on search criteria to assist with applications" refers to a function that saves subsidy information in a database, provides the necessary subsidy information based on search criteria entered by the user, and assists with applications.

[1214] The "means for periodically analyzing the user's environmental data and generating new proposals" is a function that periodically analyzes the user's environmental data and automatically generates new proposals based on that data.

[1215] This system invention functions through the cooperation of the server, the terminal, and the user. The program and processing of this system will be specifically described below.

[1216] goal setting

[1217] The user logs into the system using a terminal. On the goal setting screen displayed on the terminal, the user enters an environmental goal, such as "reducing CO2 emissions by 30% by 2025." When the user enters the goal and presses the send button, the server receives the data. The server stores this goal data in a database and uses it as the base data for future annual plans and simulations.

[1218] Creating an annual plan

[1219] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters this data and presses the submit button, the server receives the entered data, analyzes it using AI technology (for example, Scikit-learn or TensorFlow), and automatically generates an optimal annual plan. The proposed annual plan is presented to the user for approval.

[1220] Prediction and reduction simulation

[1221] When a user wants to simulate a reduction measure, they select the target item on the simulation screen. For example, if they want to check the effect of introducing new energy-saving equipment, they enter the detailed data. When the user enters the detailed data and presses the send button, the server runs a simulation using an AI model (for example, a Keras neural network) based on this data. The resulting predicted reduction amount and cost effect are presented to the user.

[1222] Suggestion feature

[1223] The server periodically analyzes the user's environmental data and generates new proposals. For example, it may propose an environmental awareness program for employees or new energy efficiency improvements based on the data analysis. These proposals are presented to the user on the system, and the user can decide whether to implement the proposals as needed.

[1224] Support for purchasing and selling carbon credits

[1225] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen. The server obtains market data in real time and proposes the optimal trading timing and trading partner for the user's needs. The user can then proceed with the specific transaction based on this proposal.

[1226] Support for using subsidies

[1227] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen. The server provides subsidy information that matches the user's criteria and assists the user in applying for a subsidy. The user can upload the necessary documents through the system and apply for a subsidy.

[1228] Specific examples and examples of prompts to input to the generative AI model

[1229] Examples:

[1230] Goal setting: "The user enters their goal, such as 'reduce CO2 emissions by 30% by 2025,' into a form on their device and saves it on the server."

[1231] Creating an annual plan: "The user inputs energy consumption data, and the server analyzes the data and generates an optimal annual plan."

[1232] Energy Reduction Simulation: "Users can simulate the introduction of new energy-saving equipment, enter detailed data to confirm the effects, and then press the submit button. The server then uses AI to present the simulation results."

[1233] Example prompt for a generative AI model:

[1234] "Please explain the process flow of your system to support goal setting."

[1235] "Please tell me the detailed steps for the system that generates an annual plan based on the environmental goals entered by the user."

[1236] "Please detail the operation of the system to simulate the CO2 reduction effect of introducing new energy-saving equipment."

[1237] This system is highly effective as an integrated support tool for companies and public institutions to efficiently achieve their environmental goals. By utilizing specific data processing and AI technology, users can quickly and accurately obtain the information and support they need.

[1238] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1239] goal setting

[1240] Step 1:

[1241] The user starts up the terminal and logs in to the system. They enter their user ID and password on the login screen and press the submit button. Based on this input, the server retrieves the user information from the database and performs authentication processing. If authentication is successful, the user's dashboard is displayed.

[1242] Step 2:

[1243] The user accesses the goal setting screen from the dashboard, enters an environmental goal such as "reduce CO2 emissions by 30% by 2025," and presses the submit button. The server receives this input data and executes an SQL query to save it in the database.

[1244] Creating an annual plan

[1245] Step 3:

[1246] The user accesses the annual plan creation screen. The annual plan creation screen displays a template for entering energy consumption and current CO2 emission data. The user enters this data and presses the submit button. The server receives the input data and saves it in the database.

[1247] Step 4:

[1248] The server invokes an AI model (such as Scikit-learn or TensorFlow) based on the stored data and performs data analysis. This analysis generates an optimal annual plan. The generated annual plan is converted into an appropriate format for presentation to the user and displayed on a web page.

[1249] Step 5:

[1250] The user checks the presented annual plan and gives feedback on approval or correction. When the user clicks the approval button, the server records the approval and proceeds to the next step.

[1251] Prediction and reduction simulation

[1252] Step 6:

[1253] The user accesses the simulation screen, selects the item to be simulated, enters data to confirm the effect of introducing new energy-saving equipment, for example, and presses the send button. The server receives this data and runs the simulation using an AI model (e.g., Keras).

[1254] Step 7:

[1255] The server generates simulation results, calculates predicted savings and cost benefits, and formats and displays these results on a web page for presentation to the user.

[1256] Suggestion feature

[1257] Step 8:

[1258] The server periodically retrieves and analyzes environmental data from the database. It then applies machine learning algorithms such as clustering and regression analysis to generate new proposals. These proposals include specific content, such as "new energy efficiency improvements to reduce energy consumption by 10%."

[1259] Step 9:

[1260] The generated suggestions are displayed to the user, who can then review them and decide whether to implement them. Once the user clicks the "Implement" button, the server records the information and uses it in the next improvement cycle.

[1261] Support for purchasing and selling carbon credits

[1262] Step 10:

[1263] A user accesses the trading screen and enters the quantity and price of carbon credits they wish to buy or sell. The server receives the input data and calls an API to obtain market data in real time.

[1264] Step 11:

[1265] The server calculates the optimal trading timing and counterparty based on market data and makes a proposal to the user. When this proposal is displayed and the user clicks the trade button, the server records the trading information and proceeds with the actual trade.

[1266] Support for using subsidies

[1267] Step 12:

[1268] The server periodically collects the latest subsidy information from the subsidy information API and stores it in a database. Based on this database information, the user enters the conditions on the subsidy search screen and presses the submit button.

[1269] Step 13:

[1270] The server extracts and presents subsidy information that matches the user's search criteria. Once the user finds the subsidy information they need, they upload their application documents and click the apply button. The server records this information and sends it to the grant agency.

[1271] Through the above processing steps, the system is able to provide comprehensive support for users to achieve their environmental goals efficiently and effectively.

[1272] (Application example 1)

[1273] 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."

[1274] Currently, many factories are setting environmental goals and trying to manage energy consumption based on them, but they face difficulties in generating annual plans, predicting reduction measures, running simulations, and proposing new energy-saving measures. Furthermore, trading carbon credits and utilizing subsidies require real-time data analysis and quick decision-making, which requires specialized knowledge and skills, and many factories are slow to adopt these approaches. Therefore, an integrated system is needed to improve the energy efficiency of the entire factory and effectively achieve environmental goals.

[1275] 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.

[1276] In this invention, the server includes means for saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing subsidy information to users, means for controlling robots to optimize energy efficiency in the factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time, thereby making it possible to improve the energy efficiency of the entire factory and effectively achieve the environmental targets.

[1277] "Environmental goals" are targets set by users for reducing energy consumption and CO2 emissions at facilities such as factories.

[1278] An "annual plan" is a detailed schedule for implementing specific action plans throughout the year to achieve set environmental targets.

[1279] "Abatement measures" are specific measures and methods implemented to reduce energy consumption and mitigate environmental impact.

[1280] "Simulation results" are the results of predicting and analyzing the effects of the set reduction measures if they are actually implemented.

[1281] "Proposals" are specific action plans and measures generated by the server to help achieve environmental goals.

[1282] "Carbon credit trading" is a market transaction that aims to reduce environmental impact by buying and selling reduced CO2 emissions.

[1283] "Subsidy information" refers to information on fundraising provided to support environmental improvement and energy efficiency improvements.

[1284] "Factory environment" refers to the overall situation and conditions regarding production equipment and energy consumption within a factory.

[1285] "Energy efficiency" refers to the ability to effectively utilize energy resources, and means reducing wasteful energy consumption and operating efficiently.

[1286] "Robot control" refers to the operation and supervision of robots deployed within a factory to manage and optimize their movements and operations.

[1287] "Energy-saving equipment" refers to facilities and devices that consume less energy than conventional equipment and can reduce the burden on the environment.

[1288] A "generative AI model" is an algorithm or program that allows artificial intelligence to generate optimal plans and proposals on its own based on data entered by the user.

[1289] "Real-time data analysis" is a technology that instantly analyzes ongoing data and outputs results immediately.

[1290] "Energy saving measures" refer to specific methods and measures for reducing energy consumption.

[1291] The present invention provides an integrated system for optimizing energy efficiency and achieving environmental goals within a factory, which functions through the cooperation of a server, terminals, and users.

[1292] The server includes means for storing input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting the simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting subsidy information and providing it to users, means for controlling robots to optimize energy efficiency in a factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time.

[1293] An embodiment of this system will now be described in detail.

[1294] First, the user logs into the system using a terminal and inputs their environmental goals. For example, they can set a specific goal such as "reducing CO2 emissions by 30% by 2025." The server receives these goals and stores them in a database. The saved goal data serves as the basis for future annual plans and simulations.

[1295] Next, the user accesses the system again and opens the annual plan creation screen. A template for inputting energy consumption and current CO2 emission data is displayed, and the user enters this data. The server analyzes the input data using a generative AI model (e.g., TensorFlow or PyTorch) and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[1296] Furthermore, if a user wants to simulate the effects of introducing new energy-saving equipment, they can enter details on the simulation screen, such as the annual energy savings of the new energy-saving equipment. Based on this input data, the server uses a generative AI model to run the simulation and presents the results, such as predicted energy savings and cost effectiveness, to the user.

[1297] The server also continuously analyzes the user's environmental data and generates new proposals. For example, based on periodic data analysis, it makes effective proposals such as environmental awareness programs for employees and new energy efficiency improvement measures. The user can review these proposals on the system and decide whether to incorporate them as appropriate.

[1298] The system also supports the use of carbon credit trading and subsidy information. When a user wishes to buy or sell carbon credits, they enter the quantity and price on the system's trading screen. The server obtains market data in real time and suggests the optimal trading timing and trading partner. For subsidy information, the server also collects the latest information, provides information that matches the search criteria, and supports the application process.

[1299] In this way, it is possible to increase the energy efficiency of the entire factory and help to effectively achieve environmental goals.

[1300] As a concrete example, consider a factory manager who has set a goal of reducing CO2 emissions by 30% by 2025. The manager enters the following:

[1301] Example prompt sentence:

[1302] To reduce your factory's CO2 emissions by 30%, use AI to generate an annual plan based on the following data:

[1303] 1. Target year: 2025

[1304] 2. Current energy consumption: 1000 kWh / month

[1305] 3. Current CO2 emissions: 300 kg / month

[1306] 4. Data for new energy-saving devices: 200 kWh saved per year

[1307] Based on these, we will provide you with the best annual plan.

[1308] Based on this prompt, the generative AI model generates an annual plan and presents suggestions to the user.

[1309] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1310] Step 1:

[1311] Users log in to the system using a terminal and input their environmental goals. For example, they can set a goal such as "reducing CO2 emissions by 30% by 2025." The input environmental goals are sent to the server, which receives this goal data and stores it in a database.

[1312] Step 2:

[1313] The user accesses the system again from their terminal and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters and submits this data, the server receives it. The received data is stored in a database.

[1314] Step 3:

[1315] The server analyzes the stored environmental targets and energy consumption data using a generative AI model (e.g., TensorFlow or PyTorch). Based on the analysis results, an annual plan is automatically generated. The generated annual plan is sent to the user's device as a proposal. The user reviews the presented annual plan and decides whether to approve it.

[1316] Step 4:

[1317] When a user wants to simulate the effects of introducing new energy-saving equipment, they enter detailed data on the simulation screen and submit it. For example, they can enter the annual energy savings of the new energy-saving equipment. The server receives this data and runs the simulation using a generative AI model. The simulation results, such as predicted energy savings and cost effects, are then sent to the user's device.

[1318] Step 5:

[1319] The server continuously analyzes the user's environmental data and generates new energy-saving suggestions. Based on the results of periodic data analysis, the server creates useful suggestions, such as environmental awareness programs for employees or new energy efficiency improvements. The generated suggestions are sent to the user's device. The user reviews the suggestions and decides whether to implement them.

[1320] Step 6:

[1321] When a user wishes to buy or sell carbon credits, they enter the desired quantity and price on the system's trading screen and submit it. The server receives the entered data and obtains market data in real time. It then proposes the optimal trading timing and trading partner. The proposal is sent to the user's device, and the user proceeds with the transaction based on it.

[1322] Step 7:

[1323] When a user wants to search for subsidy information, they enter their search criteria on the system's search screen and submit it. The server searches the database for the latest subsidy information based on the entered criteria. Subsidy information that matches the criteria is sent to the user's device, and the user can use that information to apply for a subsidy.

[1324] 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.

[1325] The present invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the present invention. This system functions by combining a server, a terminal, and an emotion engine.

[1326] ---

[1327] goal setting

[1328] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[1329] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[1330] ---

[1331] Creating an annual plan

[1332] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[1333] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[1334] ---

[1335] Prediction and reduction simulation

[1336] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[1337] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[1338] ---

[1339] Suggestion feature

[1340] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[1341] Furthermore, the emotion engine analyzes the user's input and behavioral data to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is losing motivation for the plan, the server generates additional suggestions and encouraging messages to improve the user's motivation.

[1342] ---

[1343] Support for purchasing and selling carbon credits

[1344] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[1345] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[1346] ---

[1347] Support for using subsidies

[1348] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[1349] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[1350] ---

[1351] Combining Emotion Engines

[1352] As users go through steps such as goal setting, annual planning, simulations, proposals, carbon credit trading, and subsidy information viewing, the emotion engine analyzes their input in real time to recognize emotions such as stress, excitement, or anxiety.

[1353] Based on the results of the emotion engine, the server automatically responds appropriately to the user's situation. For example, if it determines that the user's motivation is declining, it displays positive feedback and encouraging messages to improve motivation.

[1354] ---

[1355] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby enabling companies and public institutions to achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[1356] The processing flow will be explained below.

[1357] ---

[1358] goal setting

[1359] Step 1:

[1360] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[1361] Step 2:

[1362] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[1363] Step 3:

[1364] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[1365] Step 4:

[1366] The emotion engine analyzes the user's behavioral patterns and typing speed while they are typing to determine their emotional state. For example, if they type smoothly, it will sense "normal," but if their tempo is too fast, it will sense "impatience."

[1367] Step 5:

[1368] The server receives the user's emotion data and goal data and stores them in a database.

[1369] ---

[1370] Creating an annual plan

[1371] Step 1:

[1372] User selects the "Create Annual Plan" option from the dashboard.

[1373] Step 2:

[1374] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[1375] Step 3:

[1376] The user enters the necessary data according to the template and presses the send button.

[1377] Step 4:

[1378] The emotion engine captures the user's emotional state while they are typing, and if stress or anxiety is detected, the server displays a comforting or encouraging message.

[1379] Step 5:

[1380] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[1381] Step 6:

[1382] The user reviews the proposed annual plan and makes any necessary adjustments.

[1383] Step 7:

[1384] The server saves the revised plan as the final version in the database.

[1385] ---

[1386] Prediction and reduction simulation

[1387] Step 1:

[1388] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[1389] Step 2:

[1390] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[1391] Step 3:

[1392] The emotion engine captures the user's emotional state and displays reminder messages if they are slow to type.

[1393] Step 4:

[1394] The server runs a simulation based on the entered items and data.

[1395] Step 5:

[1396] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[1397] Step 6:

[1398] The emotion engine monitors the user's emotions regarding the simulation results and displays additional suggestions if satisfaction is low.

[1399] ---

[1400] Suggestion feature

[1401] Step 1:

[1402] The server continuously analyzes your environment data, periodically analyzing it to detect patterns and trends.

[1403] Step 2:

[1404] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[1405] Step 3:

[1406] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly. For example, if the user is feeling stressed, simple and effective suggestions will be prioritized.

[1407] Step 4:

[1408] Users view the offers from their dashboard and view details about the offers that interest them.

[1409] Step 5:

[1410] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[1411] ---

[1412] Support for purchasing and selling carbon credits

[1413] Step 1:

[1414] User selects the "Carbon Credit Trading" option from the dashboard.

[1415] Step 2:

[1416] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[1417] Step 3:

[1418] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[1419] Step 4:

[1420] The emotion engine analyzes the user's emotional state before trading, and if the user feels unsure, it displays information to build confidence and past success stories.

[1421] Step 5:

[1422] The user reviews the proposal and, if satisfied, executes the transaction.

[1423] ---

[1424] Support for using subsidies

[1425] Step 1:

[1426] The server collects the latest grant information and updates the database.

[1427] Step 2:

[1428] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[1429] Step 3:

[1430] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[1431] Step 4:

[1432] The user initiates the application process and uploads the required documents into the system.

[1433] Step 5:

[1434] The emotion engine analyzes the user's emotional state during the application process and displays a guide to simplify the process if the user is confused.

[1435] Step 6:

[1436] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[1437] ---

[1438] This processing flow enables the system, combined with the emotion engine, to grasp the user's emotional state in real time and provide appropriate support and suggestions, helping companies and public institutions achieve their environmental goals more efficiently and effectively.

[1439] Example 2

[1440] 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."

[1441] The purpose of this invention is to improve user motivation throughout the process, from setting environmental goals to managing their achievement, and to realize more effective and sustainable environmental measures. Conventional systems lack the means to respond to fluctuations in users' emotions and motivation, which leads to a high likelihood of users giving up midway through the plan. Furthermore, functions such as setting environmental goals, creating annual plans, simulating reductions, trading carbon credits, and providing subsidy information are not integrated, which makes it difficult to provide consistent support.

[1442] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1443] In this invention, the server includes means for saving environmental goals entered by the user, means for automatically generating an annual plan based on the saved environmental goals, means for predicting the results of a simulation of reduction measures specified by the user, means for continuously generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing the latest subsidy information to the user, means for analyzing the user's emotional state in real time, and means for providing feedback to improve the user's motivation based on the analysis results, thereby enabling effective support for achieving environmental goals that takes the user's emotional state into consideration.

[1444] "Environmental goals" are specific targets set by the user for environmental protection and improvement.

[1445] An "annual plan" is a plan that indicates specific measures and actions to be implemented within a year based on the environmental goals that have been saved.

[1446] "Reduction measures" are specific measures to reduce environmental impact, such as reducing energy consumption and CO2 emissions.

[1447] A "simulation" is a mock experiment to predict the results of implementing specified reduction measures.

[1448] "Proposals" are measures and actions that are necessary to improve the environment that are presented to the user.

[1449] A "carbon credit" is a tradable unit that represents the amount of greenhouse gas emissions reduction, such as CO2.

[1450] "Subsidy information" refers to information for receiving financial support to implement environmental measures.

[1451] "Saving" is the act of writing data or information entered by a user into a database.

[1452] "Analysis" refers to the act of analyzing and evaluating data based on user input and environmental data.

[1453] "Continuous generation" is the process of periodically analyzing data and generating new suggestions and information.

[1454] "Real time" means that processing is executed the instant the user performs an operation or input.

[1455] "Emotional state" refers to the user's mental and psychological state.

[1456] "Motivation" refers to the user's willingness and motivation to achieve environmental goals.

[1457] "Feedback" refers to information or actions given in response to a user's actions or state.

[1458] The present invention provides a system that recognizes a user's emotions and supports the process from setting environmental goals to managing their achievement accordingly. The system functions by combining a server, a terminal, and an emotion engine. Hereinafter, embodiments of the present invention will be described in detail.

[1459] Hardware and software used

[1460] Server: A physical or virtual computer with high-performance data processing capabilities.

[1461] Terminal: User input device such as a personal computer, smartphone, or tablet.

[1462] Database: Data management systems such as MySQL, PostgreSQL, etc.

[1463] Emotion Engine: Uses AI technology to analyze user emotions

[1464] Generative AI models: Artificial intelligence techniques used to analyze environmental data and generate recommendations

[1465] Detailed Description

[1466] 1. Goal Setting

[1467] User action: The user logs in to the system using a terminal. After logging in, they enter a specific environmental goal into the form on the screen, such as "reducing CO2 emissions by 30% by 2025."

[1468] Server operation: The server receives this goal and stores it in a database. The saved goal data will be used as the basis for future annual planning and simulations.

[1469] 2. Create an annual plan

[1470] User action: The user opens the annual planning screen and enters data such as energy consumption of 500 kWh and current CO2 emissions of 100 tonnes.

[1471] Server operation: The server analyzes the input data using a generative AI model and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[1472] 3. Prediction and reduction simulation

[1473] User action: If the user wants to check the effect of introducing new energy-saving equipment on the simulation screen, he or she enters the details.

[1474] Server operation: The server runs a simulation using a generative AI model based on the input data, and presents the simulation results, such as predicted reductions and cost effectiveness, to the user.

[1475] 4. Suggestion function

[1476] Server operation: The server continuously analyzes the user's environmental data and generates new suggestions, such as environmental awareness programs for employees or new energy efficiency improvements based on data analysis.

[1477] How the emotion engine works: It analyzes user input and behavioral data to recognize the user's emotional state. For example, if the server recognizes that the user is losing motivation for the plan, it will generate additional suggestions or encouraging messages.

[1478] 5. Support for purchasing and selling carbon credits

[1479] User action: The user enters the desired amount and price on the carbon credit trading screen.

[1480] Server operation: The server obtains market data in real time and proposes optimal trading timing and trading partners according to the user's preferences.

[1481] 6. Support for using subsidies

[1482] Server operation: The server collects the latest grant information and stores it in a database.

[1483] User action: The user enters criteria on the grant information search screen.

[1484] Server operation: The server provides information on grants that match the user's requirements and assists with the application process.

[1485] Examples and prompts

[1486] Example 1: Setting environmental goals

[1487] Input: "Reduce CO2 emissions by 30% by 2025"

[1488] Process description: The user inputs a goal into the terminal, and the server stores the goal in the database.

[1489] Example 2: Creating an annual plan

[1490] Input: "Energy consumption: 500kWh, Current CO2 emissions: 100 tons"

[1491] Process description: The user enters data into the annual plan creation screen, and the server uses AI to generate and propose the optimal annual plan.

[1492] Example 3: Using an Emotion Engine

[1493] Input volume: "I get stressed when things don't go as planned."

[1494] Process description: The emotion engine analyzes the input data and generates encouraging messages and new suggestions to reduce the user's stress.

[1495] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby helping companies and public institutions achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[1496] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1497] Goal setting process flow

[1498] Step 1: User Login

[1499] Input: Username and Password

[1500] Specific behavior:

[1501] 1. A user accesses the system at a terminal and enters their username and password on the login screen.

[1502] 2. The server receives the entered username and password.

[1503] Data processing and calculation:

[1504] The server compares the input password with the hashed password in its database to see if they match.

[1505] Output: If authentication is successful, transition to the main screen. If authentication fails, display an error message.

[1506] Step 2: Enter your goals

[1507] Enter: "Cut CO2 emissions by 30% by 2025."

[1508] Specific behavior:

[1509] 1. After the user logs in, they will be taken to the goal setting screen.

[1510] 2. A user types "Reduce CO2 emissions by 30% by 2025" into a text box.

[1511] 3. The user clicks the "Submit" button.

[1512] Data processing and calculation:

[1513] The server receives the input, checks the format of the data, and converts it if necessary, for example checking the date format or numbers.

[1514] Output: The converted target data is sent to the server.

[1515] Step 3: Save your goal

[1516] Input: Transformed target data

[1517] Specific behavior:

[1518] 1. The server saves the goal data in a database.

[1519] 2. Metadata such as goal ID, user ID, and setting date and time are also stored in the database.

[1520] Data processing and calculation:

[1521] Convert the data format to SQL and insert it into the database.

[1522] Output: Goal setting completion message

[1523] Annual plan creation process flow

[1524] Step 1: Access the user's annual plan screen

[1525] Input: None (accessed by screen transition)

[1526] Specific behavior:

[1527] 1. The user opens the annual plan creation screen on their device.

[1528] 2. The planning template will be displayed.

[1529] Step 2: Enter your data

[1530] Input: "Energy consumption: 500kWh" "Current CO2 emissions: 100 tons"

[1531] Specific behavior:

[1532] 1. The user enters energy consumption of 500 kWh or current CO2 emissions of 100 tons into the template.

[1533] 2. After entering the information, the user clicks the "Submit" button.

[1534] Data processing and calculation:

[1535] The server receives the data, checks the format, and checks whether it is a numeric format.

[1536] Output: Validated data is passed to the server.

[1537] Step 3: Automatically generate annual plans

[1538] Input: Validated Data

[1539] Specific behavior:

[1540] 1. The server analyzes the verified data using an AI model.

[1541] 2. The model generates an optimal annual plan.

[1542] Data processing and calculation:

[1543] A generative AI model analyzes input data and derives recommended measures and action plans.

[1544] Output: Generated annual planning data

[1545] Step 4: Annual plan presentation and approval

[1546] Input: Generated annual plan data

[1547] Specific behavior:

[1548] 1. The server presents the generated annual plan to the user.

[1549] 2. The user reviews the plan and, if satisfied, clicks the "Approve" button.

[1550] Data processing and calculation:

[1551] Add approval data and finalize the annual plan.

[1552] Output: Confirmed annual plan

[1553] Processing flow for prediction and reduction simulation

[1554] Step 1: Open the simulation screen

[1555] Input: None (accessed by screen transition)

[1556] Specific behavior:

[1557] 1. The user opens the simulation screen on their device.

[1558] 2. A menu will appear for selecting the items to be simulated.

[1559] Step 2: Selecting simulation items and entering data

[1560] Input: "Installation of new energy-efficient equipment"

[1561] Specific behavior:

[1562] 1. The user selects the items to be simulated and enters the necessary data, such as "power consumption of new equipment," "installation cost," and "estimated lifespan."

[1563] 2. The user clicks the "Simulate" button.

[1564] Data processing and calculation:

[1565] The server receives the input data and performs format validation and format conversion.

[1566] Output: The transformed input data

[1567] Step 3: Run the simulation and present the results

[1568] Input: The converted input data

[1569] Specific behavior:

[1570] 1. The server runs a simulation using the AI ​​model.

[1571] 2. A generative AI model calculates the projected savings and cost benefits.

[1572] 3. The server presents the calculation results to the user.

[1573] Data processing and calculation:

[1574] Calculate the predicted reductions, cost effectiveness, etc. and compile the results.

[1575] Output: Simulation result data (e.g., "Predicted reduction: 20 tons, cost effect: 50,000 yen")

[1576] The above are the typical processing steps and their specific operations of the present invention, and show in detail how the system functions to help users achieve their environmental goals.

[1577] (Application example 2)

[1578] 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."

[1579] Setting environmental goals and creating annual plans based on them takes time and effort for many users, making it particularly difficult to maintain motivation. Furthermore, systems that ignore user emotions make it difficult to implement sustainable environmental measures, and their effectiveness is often limited. In particular, in the case of eco-driving in autonomous vehicles, the lack of feedback and assistance that takes user emotions into account makes it difficult to achieve goals such as improving fuel efficiency.

[1580] 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 saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for recognizing the user's emotions and providing feedback in real time, means for inputting driving data and generating an eco-driving plan, and means for providing feedback to improve motivation according to the user's emotional state. This makes it possible to implement effective environmental measures and eco-driving while maintaining the user's motivation.

[1581] "Environmental Goals" refers to specific targets or outcomes regarding environmental protection and sustainability set by a user.

[1582] "Annual Plan" refers to a plan that is implemented in stages throughout the year to achieve set environmental targets.

[1583] "Simulation results of reduction measures" refers to the predicted results obtained when simulating specific environmental measures or reduction measures.

[1584] "Carbon credit trading" refers to the transaction of buying and selling rights to reduce CO2 emissions.

[1585] "Subsidy Information" refers to detailed information about financial support provided for environmental and energy conservation projects.

[1586] An "emotion engine" refers to a system that analyzes a user's emotions and provides dynamic feedback based on those emotions.

[1587] "Real-time feedback" refers to feedback provided instantly in response to a user's input or actions.

[1588] "Driving data" refers to data related to the user's driving behavior and the vehicle's operating conditions.

[1589] An "eco-driving plan" refers to a driving method or plan that improves fuel efficiency.

[1590] "Motivational feedback" refers to encouragement or suggestions provided to motivate users.

[1591] This invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the invention. This system functions by combining a server, a terminal, and an emotion engine.

[1592] goal setting

[1593] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "improving fuel efficiency by 15% annually." The server receives this goal and stores it in a database. This saved goal data becomes the basis for future annual plans and simulations.

[1594] Creating an annual plan

[1595] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering driving data and current fuel consumption data. Once the user enters this data, the server uses AI technology to analyze the entered data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[1596] Prediction and reduction simulation

[1597] When a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to confirm the effect of introducing a new energy-saving driving method, they enter the details. The server uses AI to run the simulation based on this input data. The server then presents the simulation results, such as predicted fuel efficiency improvements and cost effects, to the user.

[1598] Sentiment Analysis and Feedback

[1599] The emotion engine analyzes the user's emotions in real time while the user is driving the vehicle. For example, if the user is feeling high stress, the server will send suggestions for relaxation methods and advice on safe driving. Conversely, if the user is maintaining motivation, the server will send encouraging messages. This helps keep the user highly motivated and helps them achieve their environmental goals.

[1600] Providing feedback

[1601] The server periodically analyzes the user's driving data and emotional data to evaluate their eco-driving achievement rate and areas for improvement. Based on this information, the server generates feedback and presents it to the user. For example, it may provide a message such as, "Your driving efficiency has improved by 10%. Keep it up next month!"

[1602] Examples and prompts

[1603] For example, if a user operates the app after a long drive and is judged to be "stressed," the server will suggest "5 minutes of deep breathing exercises." Also, if the user achieves their eco-driving goal, the server will display rewards such as points or coupons.

[1604] Example prompt sentence:

[1605] "When users set eco-driving goals, they input their daily driving data and the AI ​​will suggest the optimal driving plan. The emotion engine analyzes emotions in real time and provides appropriate feedback and suggestions."

[1606] This system is implemented using smartphones and vehicle infotainment systems. It uses the emotion engine for emotion analysis, the driving_data_analysis library for driving data analysis, and the feedback_system library for feedback provision. This allows users to maintain their motivation while driving efficiently and eco-friendly.

[1607] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1608] Step 1:

[1609] A user logs into the system using a terminal and inputs their environmental goals. This input includes specific targets such as "improving fuel efficiency by 15% annually." The server receives this input data and stores it in a database. The input data is saved as target data and is used as the basis for subsequent annual plans and simulations.

[1610] Step 2:

[1611] The user accesses the system again and opens the annual plan creation screen. On this screen, they are prompted to enter driving data (mileage, fuel economy, etc.) according to a template. The server uses AI technology to analyze the entered driving data. Specifically, it uses the driving_data_analysis library to analyze the data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal.

[1612] Step 3:

[1613] When a user wants to simulate a specific reduction measure, they select the specific measure (e.g., introducing energy-saving driving methods) on the simulation screen and enter the necessary data. The server uses AI to run the simulation based on this input data. It predicts the energy-saving and cost-effectiveness effects from the input data and presents the results to the user.

[1614] Step 4:

[1615] While the user is driving the vehicle, the emotion engine analyzes the user's emotions in real time. For example, emotion analysis is performed using data collected by cameras and sensors. If the analysis shows that the user is feeling high stress, the server sends advice on relaxation methods and safe driving through a feedback system. Emotional state data is input in real time and output as the emotion analysis results.

[1616] Step 5:

[1617] The server periodically analyzes the user's driving data and emotional data. This analysis includes past driving records and emotional states. Based on this data, the server evaluates the user's eco-driving achievement rate and areas for improvement. The server analyzes the driving data using the driving_data_analysis library and the emotional data using the emotion_engine, and provides the results as feedback to the user.

[1618] Step 6:

[1619] The feedback system generates messages and suggestions to maintain or improve the user's motivation. For example, it sends a message such as, "Your driving efficiency has improved by 10%. Let's keep it up next month!" This increases the user's motivation and supports sustainable eco-driving.

[1620] Through the above steps, users can maintain their motivation while taking effective environmental measures and practicing eco-driving.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] [Fourth embodiment]

[1625] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1626] 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.

[1627] 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).

[1628] 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.

[1629] 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.

[1630] 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).

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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."

[1638] The present invention provides an integrated system for taking effective measures against environmental problems. The following describes in detail the embodiments of the present invention. This system functions through the cooperation of a server, terminals, and users.

[1639] ---

[1640] 1. Goal Setting

[1641] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[1642] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[1643] ---

[1644] 2. Create an annual plan

[1645] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[1646] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[1647] ---

[1648] 3. Prediction and reduction simulation

[1649] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[1650] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[1651] ---

[1652] 4. Suggestion function

[1653] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[1654] The user can review these suggestions on the system and decide whether to incorporate them as appropriate.

[1655] ---

[1656] 5. Support for purchasing and selling carbon credits

[1657] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[1658] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[1659] ---

[1660] 6. Support for using subsidies

[1661] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[1662] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[1663] ---

[1664] This allows companies and public institutions to receive comprehensive support to efficiently and effectively achieve their environmental goals. The present invention is an extremely effective tool for comprehensively managing environmental measures.

[1665] The processing flow will be explained below.

[1666] ---

[1667] goal setting

[1668] Step 1:

[1669] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[1670] Step 2:

[1671] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[1672] Step 3:

[1673] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[1674] Step 4:

[1675] The server receives the input environmental goals and stores them in a database.

[1676] ---

[1677] Creating an annual plan

[1678] Step 1:

[1679] User selects the "Create Annual Plan" option from the dashboard.

[1680] Step 2:

[1681] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[1682] Step 3:

[1683] The user enters the necessary data according to the template and presses the send button.

[1684] Step 4:

[1685] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[1686] Step 5:

[1687] The user reviews the proposed annual plan and makes any necessary adjustments.

[1688] Step 6:

[1689] The server saves the revised plan as the final version in the database.

[1690] ---

[1691] Prediction and reduction simulation

[1692] Step 1:

[1693] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[1694] Step 2:

[1695] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[1696] Step 3:

[1697] The server runs a simulation based on the entered items and data.

[1698] Step 4:

[1699] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[1700] ---

[1701] Suggestion feature

[1702] Step 1:

[1703] The server continuously analyzes user-entered data, periodically analyzing it to detect patterns and trends.

[1704] Step 2:

[1705] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[1706] Step 3:

[1707] Users view the offers from their dashboard and view details about the offers that interest them.

[1708] Step 4:

[1709] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[1710] ---

[1711] Support for purchasing and selling carbon credits

[1712] Step 1:

[1713] User selects the "Carbon Credit Trading" option from the dashboard.

[1714] Step 2:

[1715] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[1716] Step 3:

[1717] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[1718] Step 4:

[1719] The user reviews the proposal and, if satisfied, executes the transaction.

[1720] ---

[1721] Support for using subsidies

[1722] Step 1:

[1723] The server collects the latest grant information and updates the database.

[1724] Step 2:

[1725] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[1726] Step 3:

[1727] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[1728] Step 4:

[1729] The user initiates the application process and uploads the required documents into the system.

[1730] Step 5:

[1731] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[1732] ---

[1733] This allows the system to assist companies and public institutions in achieving their environmental goals effectively and efficiently.

[1734] Example 1

[1735] 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."

[1736] Many current environmental management systems have separate functions, such as goal setting, annual plan creation, reduction simulation, transaction support, and provision of subsidy information, making it difficult to achieve overall efficiency and implement effective measures. Furthermore, due to a lack of automatic generation functions utilizing data analysis and AI technology, manual input and planning by users takes time and effort. The purpose of this invention is to solve these problems and provide a comprehensive system for achieving environmental goals effectively and efficiently.

[1737] 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.

[1738] In this invention, the server includes: means for saving input environmental goals; means for generating an annual plan based on the environmental goals; means for predicting simulation results of specified reduction measures; means for generating proposals necessary for environmental improvement; means for supporting carbon credit trading; means for collecting subsidy information and supporting application procedures; means for analyzing data input by a user and automatically generating an optimal annual plan using AI technology; means for acquiring real-time market data based on conditions specified by the user and proposing transaction timing; means for saving subsidy information in a database and acquiring necessary information based on search conditions to support applications; and means for analyzing the user's environmental data at regular intervals and generating new proposals. This allows for integrated management of the process from setting to achieving environmental goals, enabling users to take actions toward achieving their goals effectively and efficiently.

[1739] The "means for saving input environmental targets" is a function for receiving environmental target data input by the user and saving it in a database within the server.

[1740] The "means for generating annual plans based on environmental targets" is a function that uses AI technology to automatically generate optimal annual plans based on input environmental target data.

[1741] The "means for predicting the simulation results of the specified reduction measures" is a function that executes a simulation based on the reduction measures input by the user, predicts the results, and presents them.

[1742] The "means for generating proposals necessary for environmental improvement" is a function that analyzes data at regular intervals and automatically generates proposals for new environmental improvement measures and energy efficiency improvement measures.

[1743] "Means to support carbon credit trading" is a function that suggests optimal trading timing and trading partners to users who wish to buy or sell carbon credits.

[1744] The "means for collecting subsidy information and supporting application procedures" is a function that collects the latest subsidy information and supports users in smoothly proceeding with the subsidy application procedures.

[1745] "Means for analyzing data entered by the user and automatically generating an optimal annual plan using AI technology" refers to a function that analyzes data entered by the user and automatically generates an optimal annual plan based on that data using AI technology.

[1746] "Means for obtaining real-time market data based on conditions specified by the user and proposing trading timing" is a function for obtaining market data in real time based on conditions specified by the user and proposing optimal trading timing.

[1747] "Means for saving subsidy information in a database and for users to obtain the necessary information based on search criteria to assist with applications" refers to a function that saves subsidy information in a database, provides the necessary subsidy information based on search criteria entered by the user, and assists with applications.

[1748] The "means for periodically analyzing the user's environmental data and generating new proposals" is a function that periodically analyzes the user's environmental data and automatically generates new proposals based on that data.

[1749] This system invention functions through the cooperation of the server, the terminal, and the user. The program and processing of this system will be specifically described below.

[1750] goal setting

[1751] The user logs into the system using a terminal. On the goal setting screen displayed on the terminal, the user enters an environmental goal, such as "reducing CO2 emissions by 30% by 2025." When the user enters the goal and presses the send button, the server receives the data. The server stores this goal data in a database and uses it as the base data for future annual plans and simulations.

[1752] Creating an annual plan

[1753] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters this data and presses the submit button, the server receives the entered data, analyzes it using AI technology (for example, Scikit-learn or TensorFlow), and automatically generates an optimal annual plan. The proposed annual plan is presented to the user for approval.

[1754] Prediction and reduction simulation

[1755] When a user wants to simulate a reduction measure, they select the target item on the simulation screen. For example, if they want to check the effect of introducing new energy-saving equipment, they enter the detailed data. When the user enters the detailed data and presses the send button, the server runs a simulation using an AI model (for example, a Keras neural network) based on this data. The resulting predicted reduction amount and cost effect are presented to the user.

[1756] Suggestion feature

[1757] The server periodically analyzes the user's environmental data and generates new proposals. For example, it may propose an environmental awareness program for employees or new energy efficiency improvements based on the data analysis. These proposals are presented to the user on the system, and the user can decide whether to implement the proposals as needed.

[1758] Support for purchasing and selling carbon credits

[1759] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen. The server obtains market data in real time and proposes the optimal trading timing and trading partner for the user's needs. The user can then proceed with the specific transaction based on this proposal.

[1760] Support for using subsidies

[1761] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen. The server provides subsidy information that matches the user's criteria and assists the user in applying for a subsidy. The user can upload the necessary documents through the system and apply for a subsidy.

[1762] Specific examples and examples of prompts to input to the generative AI model

[1763] Examples:

[1764] Goal setting: "The user enters their goal, such as 'reduce CO2 emissions by 30% by 2025,' into a form on their device and saves it on the server."

[1765] Creating an annual plan: "The user inputs energy consumption data, and the server analyzes the data and generates an optimal annual plan."

[1766] Energy Reduction Simulation: "Users can simulate the introduction of new energy-saving equipment, enter detailed data to confirm the effects, and then press the submit button. The server then uses AI to present the simulation results."

[1767] Example prompt for a generative AI model:

[1768] "Please explain the process flow of your system to support goal setting."

[1769] "Please tell me the detailed steps for the system that generates an annual plan based on the environmental goals entered by the user."

[1770] "Please detail the operation of the system to simulate the CO2 reduction effect of introducing new energy-saving equipment."

[1771] This system is highly effective as an integrated support tool for companies and public institutions to efficiently achieve their environmental goals. By utilizing specific data processing and AI technology, users can quickly and accurately obtain the information and support they need.

[1772] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1773] goal setting

[1774] Step 1:

[1775] The user starts up the terminal and logs in to the system. They enter their user ID and password on the login screen and press the submit button. Based on this input, the server retrieves the user information from the database and performs authentication processing. If authentication is successful, the user's dashboard is displayed.

[1776] Step 2:

[1777] The user accesses the goal setting screen from the dashboard, enters an environmental goal such as "reduce CO2 emissions by 30% by 2025," and presses the submit button. The server receives this input data and executes an SQL query to save it in the database.

[1778] Creating an annual plan

[1779] Step 3:

[1780] The user accesses the annual plan creation screen. The annual plan creation screen displays a template for entering energy consumption and current CO2 emission data. The user enters this data and presses the submit button. The server receives the input data and saves it in the database.

[1781] Step 4:

[1782] The server invokes an AI model (such as Scikit-learn or TensorFlow) based on the stored data and performs data analysis. This analysis generates an optimal annual plan. The generated annual plan is converted into an appropriate format for presentation to the user and displayed on a web page.

[1783] Step 5:

[1784] The user checks the presented annual plan and gives feedback on approval or correction. When the user clicks the approval button, the server records the approval and proceeds to the next step.

[1785] Prediction and reduction simulation

[1786] Step 6:

[1787] The user accesses the simulation screen, selects the item to be simulated, enters data to confirm the effect of introducing new energy-saving equipment, for example, and presses the send button. The server receives this data and runs the simulation using an AI model (e.g., Keras).

[1788] Step 7:

[1789] The server generates simulation results, calculates predicted savings and cost benefits, and formats and displays these results on a web page for presentation to the user.

[1790] Suggestion feature

[1791] Step 8:

[1792] The server periodically retrieves and analyzes environmental data from the database. It then applies machine learning algorithms such as clustering and regression analysis to generate new proposals. These proposals include specific content, such as "new energy efficiency improvements to reduce energy consumption by 10%."

[1793] Step 9:

[1794] The generated suggestions are displayed to the user, who can then review them and decide whether to implement them. Once the user clicks the "Implement" button, the server records the information and uses it in the next improvement cycle.

[1795] Support for purchasing and selling carbon credits

[1796] Step 10:

[1797] A user accesses the trading screen and enters the quantity and price of carbon credits they wish to buy or sell. The server receives the input data and calls an API to obtain market data in real time.

[1798] Step 11:

[1799] The server calculates the optimal trading timing and counterparty based on market data and makes a proposal to the user. When this proposal is displayed and the user clicks the trade button, the server records the trading information and proceeds with the actual trade.

[1800] Support for using subsidies

[1801] Step 12:

[1802] The server periodically collects the latest subsidy information from the subsidy information API and stores it in a database. Based on this database information, the user enters the conditions on the subsidy search screen and presses the submit button.

[1803] Step 13:

[1804] The server extracts and presents subsidy information that matches the user's search criteria. Once the user finds the subsidy information they need, they upload their application documents and click the apply button. The server records this information and sends it to the grant agency.

[1805] Through the above processing steps, the system is able to provide comprehensive support for users to achieve their environmental goals efficiently and effectively.

[1806] (Application example 1)

[1807] 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."

[1808] Currently, many factories are setting environmental goals and trying to manage energy consumption based on them, but they face difficulties in generating annual plans, predicting reduction measures, running simulations, and proposing new energy-saving measures. Furthermore, trading carbon credits and utilizing subsidies require real-time data analysis and quick decision-making, which requires specialized knowledge and skills, and many factories are slow to adopt these approaches. Therefore, an integrated system is needed to improve the energy efficiency of the entire factory and effectively achieve environmental goals.

[1809] 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.

[1810] In this invention, the server includes means for saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing subsidy information to users, means for controlling robots to optimize energy efficiency in the factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time, thereby making it possible to improve the energy efficiency of the entire factory and effectively achieve the environmental targets.

[1811] "Environmental goals" are targets set by users for reducing energy consumption and CO2 emissions at facilities such as factories.

[1812] An "annual plan" is a detailed schedule for implementing specific action plans throughout the year to achieve set environmental targets.

[1813] "Abatement measures" are specific measures and methods implemented to reduce energy consumption and mitigate environmental impact.

[1814] "Simulation results" are the results of predicting and analyzing the effects of the set reduction measures if they are actually implemented.

[1815] "Proposals" are specific action plans and measures generated by the server to help achieve environmental goals.

[1816] "Carbon credit trading" is a market transaction that aims to reduce environmental impact by buying and selling reduced CO2 emissions.

[1817] "Subsidy information" refers to information on fundraising provided to support environmental improvement and energy efficiency improvements.

[1818] "Factory environment" refers to the overall situation and conditions regarding production equipment and energy consumption within a factory.

[1819] "Energy efficiency" refers to the ability to effectively utilize energy resources, and means reducing wasteful energy consumption and operating efficiently.

[1820] "Robot control" refers to the operation and supervision of robots deployed within a factory to manage and optimize their movements and operations.

[1821] "Energy-saving equipment" refers to facilities and devices that consume less energy than conventional equipment and can reduce the burden on the environment.

[1822] A "generative AI model" is an algorithm or program that allows artificial intelligence to generate optimal plans and proposals on its own based on data entered by the user.

[1823] "Real-time data analysis" is a technology that instantly analyzes ongoing data and outputs results immediately.

[1824] "Energy saving measures" refer to specific methods and measures for reducing energy consumption.

[1825] The present invention provides an integrated system for optimizing energy efficiency and achieving environmental goals within a factory, which functions through the cooperation of a server, terminals, and users.

[1826] The server includes means for storing input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting the simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting subsidy information and providing it to users, means for controlling robots to optimize energy efficiency in a factory environment, means for simulating the effects of introducing new energy-saving equipment, and means for proposing energy-saving measures in real time.

[1827] An embodiment of this system will now be described in detail.

[1828] First, the user logs into the system using a terminal and inputs their environmental goals. For example, they can set a specific goal such as "reducing CO2 emissions by 30% by 2025." The server receives these goals and stores them in a database. The saved goal data serves as the basis for future annual plans and simulations.

[1829] Next, the user accesses the system again and opens the annual plan creation screen. A template for inputting energy consumption and current CO2 emission data is displayed, and the user enters this data. The server analyzes the input data using a generative AI model (e.g., TensorFlow or PyTorch) and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[1830] Furthermore, if a user wants to simulate the effects of introducing new energy-saving equipment, they can enter details on the simulation screen, such as the annual energy savings of the new energy-saving equipment. Based on this input data, the server uses a generative AI model to run the simulation and presents the results, such as predicted energy savings and cost effectiveness, to the user.

[1831] The server also continuously analyzes the user's environmental data and generates new proposals. For example, based on periodic data analysis, it makes effective proposals such as environmental awareness programs for employees and new energy efficiency improvement measures. The user can review these proposals on the system and decide whether to incorporate them as appropriate.

[1832] The system also supports the use of carbon credit trading and subsidy information. When a user wishes to buy or sell carbon credits, they enter the quantity and price on the system's trading screen. The server obtains market data in real time and suggests the optimal trading timing and trading partner. For subsidy information, the server also collects the latest information, provides information that matches the search criteria, and supports the application process.

[1833] In this way, it is possible to increase the energy efficiency of the entire factory and help to effectively achieve environmental goals.

[1834] As a concrete example, consider a factory manager who has set a goal of reducing CO2 emissions by 30% by 2025. The manager enters the following:

[1835] Example prompt sentence:

[1836] To reduce your factory's CO2 emissions by 30%, use AI to generate an annual plan based on the following data:

[1837] 1. Target year: 2025

[1838] 2. Current energy consumption: 1000 kWh / month

[1839] 3. Current CO2 emissions: 300 kg / month

[1840] 4. Data for new energy-saving devices: 200 kWh saved per year

[1841] Based on these, we will provide you with the best annual plan.

[1842] Based on this prompt, the generative AI model generates an annual plan and presents suggestions to the user.

[1843] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1844] Step 1:

[1845] Users log in to the system using a terminal and input their environmental goals. For example, they can set a goal such as "reducing CO2 emissions by 30% by 2025." The input environmental goals are sent to the server, which receives this goal data and stores it in a database.

[1846] Step 2:

[1847] The user accesses the system again from their terminal and opens the annual plan creation screen. This screen displays a template for entering energy consumption and current CO2 emission data. When the user enters and submits this data, the server receives it. The received data is stored in a database.

[1848] Step 3:

[1849] The server analyzes the stored environmental targets and energy consumption data using a generative AI model (e.g., TensorFlow or PyTorch). Based on the analysis results, an annual plan is automatically generated. The generated annual plan is sent to the user's device as a proposal. The user reviews the presented annual plan and decides whether to approve it.

[1850] Step 4:

[1851] When a user wants to simulate the effects of introducing new energy-saving equipment, they enter detailed data on the simulation screen and submit it. For example, they can enter the annual energy savings of the new energy-saving equipment. The server receives this data and runs the simulation using a generative AI model. The simulation results, such as predicted energy savings and cost effects, are then sent to the user's device.

[1852] Step 5:

[1853] The server continuously analyzes the user's environmental data and generates new energy-saving suggestions. Based on the results of periodic data analysis, the server creates useful suggestions, such as environmental awareness programs for employees or new energy efficiency improvements. The generated suggestions are sent to the user's device. The user reviews the suggestions and decides whether to implement them.

[1854] Step 6:

[1855] When a user wishes to buy or sell carbon credits, they enter the desired quantity and price on the system's trading screen and submit it. The server receives the entered data and obtains market data in real time. It then proposes the optimal trading timing and trading partner. The proposal is sent to the user's device, and the user proceeds with the transaction based on it.

[1856] Step 7:

[1857] When a user wants to search for subsidy information, they enter their search criteria on the system's search screen and submit it. The server searches the database for the latest subsidy information based on the entered criteria. Subsidy information that matches the criteria is sent to the user's device, and the user can use that information to apply for a subsidy.

[1858] 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.

[1859] The present invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the present invention. This system functions by combining a server, a terminal, and an emotion engine.

[1860] ---

[1861] goal setting

[1862] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "reducing CO2 emissions by 30% by 2025."

[1863] The server receives these goals and stores them in a database. This saved goal data will be used as the basis for future annual plans and simulations.

[1864] ---

[1865] Creating an annual plan

[1866] The user accesses the system again and opens the annual plan creation screen, which displays a template for entering energy consumption and current CO2 emissions data.

[1867] Once the user enters this data, the server uses AI technology to analyze the data and automatically generate an optimal annual plan, which is then presented to the user as a proposal for approval.

[1868] ---

[1869] Prediction and reduction simulation

[1870] If a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to check the effect of introducing a new energy-saving device, they enter the details.

[1871] The server uses AI to run a simulation based on this input data, and then presents the results of the simulation, such as predicted reductions and cost effectiveness, to the user.

[1872] ---

[1873] Suggestion feature

[1874] The server continuously analyzes the user's environmental data and generates new proposals, such as effective suggestions for employee environmental awareness programs and new energy efficiency improvements, based on periodic data analysis.

[1875] Furthermore, the emotion engine analyzes the user's input and behavioral data to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is losing motivation for the plan, the server generates additional suggestions and encouraging messages to improve the user's motivation.

[1876] ---

[1877] Support for purchasing and selling carbon credits

[1878] When a user wishes to buy or sell carbon credits, they input the desired quantity and price on the system's trading screen.

[1879] The server obtains market data in real time and proposes optimal trading timing and counterparties based on the user's preferences. The user can then proceed with specific transactions based on these proposals.

[1880] ---

[1881] Support for using subsidies

[1882] The server collects the latest subsidy information and stores it in a database. When a user wants to search for subsidy information, they enter their search criteria on the system's search screen.

[1883] The server provides information on grants that meet the user's requirements and assists the user in applying for grants. Users can upload the necessary documents through the system and apply to the grant agency.

[1884] ---

[1885] Combining Emotion Engines

[1886] As users go through steps such as goal setting, annual planning, simulations, proposals, carbon credit trading, and subsidy information viewing, the emotion engine analyzes their input in real time to recognize emotions such as stress, excitement, or anxiety.

[1887] Based on the results of the emotion engine, the server automatically responds appropriately to the user's situation. For example, if it determines that the user's motivation is declining, it displays positive feedback and encouraging messages to improve motivation.

[1888] ---

[1889] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby enabling companies and public institutions to achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[1890] The processing flow will be explained below.

[1891] ---

[1892] goal setting

[1893] Step 1:

[1894] A user logs into the system using a terminal, entering a username and password to begin the authentication process.

[1895] Step 2:

[1896] The server authenticates the user by checking their username and password against a database, and if successful, redirects them to their dashboard.

[1897] Step 3:

[1898] The user selects the "Set Goal" option from the dashboard and enters an environmental goal (e.g., reduce CO2 emissions by 30% by 2025).

[1899] Step 4:

[1900] The emotion engine analyzes the user's behavioral patterns and typing speed while they are typing to determine their emotional state. For example, if they type smoothly, it will sense "normal," but if their tempo is too fast, it will sense "impatience."

[1901] Step 5:

[1902] The server receives the user's emotion data and goal data and stores them in a database.

[1903] ---

[1904] Creating an annual plan

[1905] Step 1:

[1906] User selects the "Create Annual Plan" option from the dashboard.

[1907] Step 2:

[1908] The server displays to the user a data input template (energy consumption, current CO2 emissions, etc.) required to create an annual plan.

[1909] Step 3:

[1910] The user enters the necessary data according to the template and presses the send button.

[1911] Step 4:

[1912] The emotion engine captures the user's emotional state while they are typing, and if stress or anxiety is detected, the server displays a comforting or encouraging message.

[1913] Step 5:

[1914] The server uses AI to generate an annual plan based on the input data, and presents the generated plan to the user.

[1915] Step 6:

[1916] The user reviews the proposed annual plan and makes any necessary adjustments.

[1917] Step 7:

[1918] The server saves the revised plan as the final version in the database.

[1919] ---

[1920] Prediction and reduction simulation

[1921] Step 1:

[1922] The user selects the "Forecast and Reduction Simulation" option from the dashboard.

[1923] Step 2:

[1924] The user selects the item they wish to simulate (e.g., a specific energy efficiency improvement measure) and enters the necessary data.

[1925] Step 3:

[1926] The emotion engine captures the user's emotional state and displays reminder messages if they are slow to type.

[1927] Step 4:

[1928] The server runs a simulation based on the entered items and data.

[1929] Step 5:

[1930] The server calculates the simulation results and presents them to the user, including the predicted savings and the costs required to run the simulation.

[1931] Step 6:

[1932] The emotion engine monitors the user's emotions regarding the simulation results and displays additional suggestions if satisfaction is low.

[1933] ---

[1934] Suggestion feature

[1935] Step 1:

[1936] The server continuously analyzes your environment data, periodically analyzing it to detect patterns and trends.

[1937] Step 2:

[1938] The server generates effective improvement proposals from the analysis results, such as new energy efficiency measures or environmental education programs for employees.

[1939] Step 3:

[1940] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly. For example, if the user is feeling stressed, simple and effective suggestions will be prioritized.

[1941] Step 4:

[1942] Users view the offers from their dashboard and view details about the offers that interest them.

[1943] Step 5:

[1944] The user will adopt the suggestions as appropriate and reflect them in the implementation plan.

[1945] ---

[1946] Support for purchasing and selling carbon credits

[1947] Step 1:

[1948] User selects the "Carbon Credit Trading" option from the dashboard.

[1949] Step 2:

[1950] The user inputs the desired purchase or sale quantity and desired price, and presses the send button.

[1951] Step 3:

[1952] The server obtains real-time market data and suggests optimal trading timing and trading partners based on the user's preferences.

[1953] Step 4:

[1954] The emotion engine analyzes the user's emotional state before trading, and if the user feels unsure, it displays information to build confidence and past success stories.

[1955] Step 5:

[1956] The user reviews the proposal and, if satisfied, executes the transaction.

[1957] ---

[1958] Support for using subsidies

[1959] Step 1:

[1960] The server collects the latest grant information and updates the database.

[1961] Step 2:

[1962] A user selects the "Grant Information" option from the dashboard and enters criteria to search for available grants.

[1963] Step 3:

[1964] The server searches for subsidy information that matches the user's criteria and displays the matching results to the user.

[1965] Step 4:

[1966] The user initiates the application process and uploads the required documents into the system.

[1967] Step 5:

[1968] The emotion engine analyzes the user's emotional state during the application process and displays a guide to simplify the process if the user is confused.

[1969] Step 6:

[1970] The server reviews the uploaded documents, notifies the user if any information is missing, and finally sends all documents to the appropriate funding agency.

[1971] ---

[1972] This processing flow enables the system, combined with the emotion engine, to grasp the user's emotional state in real time and provide appropriate support and suggestions, helping companies and public institutions achieve their environmental goals more efficiently and effectively.

[1973] Example 2

[1974] 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."

[1975] The purpose of this invention is to improve user motivation throughout the process, from setting environmental goals to managing their achievement, and to realize more effective and sustainable environmental measures. Conventional systems lack the means to respond to fluctuations in users' emotions and motivation, which leads to a high likelihood of users giving up midway through the plan. Furthermore, functions such as setting environmental goals, creating annual plans, simulating reductions, trading carbon credits, and providing subsidy information are not integrated, which makes it difficult to provide consistent support.

[1976] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1977] In this invention, the server includes means for saving environmental goals entered by the user, means for automatically generating an annual plan based on the saved environmental goals, means for predicting the results of a simulation of reduction measures specified by the user, means for continuously generating proposals necessary for environmental improvement, means for supporting carbon credit trading, means for collecting and providing the latest subsidy information to the user, means for analyzing the user's emotional state in real time, and means for providing feedback to improve the user's motivation based on the analysis results, thereby enabling effective support for achieving environmental goals that takes the user's emotional state into consideration.

[1978] "Environmental goals" are specific targets set by the user for environmental protection and improvement.

[1979] An "annual plan" is a plan that indicates specific measures and actions to be implemented within a year based on the environmental goals that have been saved.

[1980] "Reduction measures" are specific measures to reduce environmental impact, such as reducing energy consumption and CO2 emissions.

[1981] A "simulation" is a mock experiment to predict the results of implementing specified reduction measures.

[1982] "Proposals" are measures and actions that are necessary to improve the environment that are presented to the user.

[1983] A "carbon credit" is a tradable unit that represents the amount of greenhouse gas emissions reduction, such as CO2.

[1984] "Subsidy information" refers to information for receiving financial support to implement environmental measures.

[1985] "Saving" is the act of writing data or information entered by a user into a database.

[1986] "Analysis" refers to the act of analyzing and evaluating data based on user input and environmental data.

[1987] "Continuous generation" is the process of periodically analyzing data and generating new suggestions and information.

[1988] "Real time" means that processing is executed the instant the user performs an operation or input.

[1989] "Emotional state" refers to the user's mental and psychological state.

[1990] "Motivation" refers to the user's willingness and motivation to achieve environmental goals.

[1991] "Feedback" refers to information or actions given in response to a user's actions or state.

[1992] The present invention provides a system that recognizes a user's emotions and supports the process from setting environmental goals to managing their achievement accordingly. The system functions by combining a server, a terminal, and an emotion engine. Hereinafter, embodiments of the present invention will be described in detail.

[1993] Hardware and software used

[1994] Server: A physical or virtual computer with high-performance data processing capabilities.

[1995] Terminal: User input device such as a personal computer, smartphone, or tablet.

[1996] Database: Data management systems such as MySQL, PostgreSQL, etc.

[1997] Emotion Engine: Uses AI technology to analyze user emotions

[1998] Generative AI models: Artificial intelligence techniques used to analyze environmental data and generate recommendations

[1999] Detailed Description

[2000] 1. Goal Setting

[2001] User action: The user logs in to the system using a terminal. After logging in, they enter a specific environmental goal into the form on the screen, such as "reducing CO2 emissions by 30% by 2025."

[2002] Server operation: The server receives this goal and stores it in a database. The saved goal data will be used as the basis for future annual planning and simulations.

[2003] 2. Create an annual plan

[2004] User action: The user opens the annual planning screen and enters data such as energy consumption of 500 kWh and current CO2 emissions of 100 tonnes.

[2005] Server operation: The server analyzes the input data using a generative AI model and automatically generates an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[2006] 3. Prediction and reduction simulation

[2007] User action: If the user wants to check the effect of introducing new energy-saving equipment on the simulation screen, he or she enters the details.

[2008] Server operation: The server runs a simulation using a generative AI model based on the input data, and presents the simulation results, such as predicted reductions and cost effectiveness, to the user.

[2009] 4. Suggestion function

[2010] Server operation: The server continuously analyzes the user's environmental data and generates new suggestions, such as environmental awareness programs for employees or new energy efficiency improvements based on data analysis.

[2011] How the emotion engine works: It analyzes user input and behavioral data to recognize the user's emotional state. For example, if the server recognizes that the user is losing motivation for the plan, it will generate additional suggestions or encouraging messages.

[2012] 5. Support for purchasing and selling carbon credits

[2013] User action: The user enters the desired amount and price on the carbon credit trading screen.

[2014] Server operation: The server obtains market data in real time and proposes optimal trading timing and trading partners according to the user's preferences.

[2015] 6. Support for using subsidies

[2016] Server operation: The server collects the latest grant information and stores it in a database.

[2017] User action: The user enters criteria on the grant information search screen.

[2018] Server operation: The server provides information on grants that match the user's requirements and assists with the application process.

[2019] Examples and prompts

[2020] Example 1: Setting environmental goals

[2021] Input: "Reduce CO2 emissions by 30% by 2025"

[2022] Process description: The user inputs a goal into the terminal, and the server stores the goal in the database.

[2023] Example 2: Creating an annual plan

[2024] Input: "Energy consumption: 500kWh, Current CO2 emissions: 100 tons"

[2025] Process description: The user enters data into the annual plan creation screen, and the server uses AI to generate and propose the optimal annual plan.

[2026] Example 3: Using an Emotion Engine

[2027] Input volume: "I get stressed when things don't go as planned."

[2028] Process description: The emotion engine analyzes the input data and generates encouraging messages and new suggestions to reduce the user's stress.

[2029] As described above, this invention provides an integrated system that recognizes users' emotions and proposes and supports appropriate environmental measures, thereby helping companies and public institutions achieve their environmental goals more effectively and contributing to the realization of a sustainable society.

[2030] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2031] Goal setting process flow

[2032] Step 1: User Login

[2033] Input: Username and Password

[2034] Specific behavior:

[2035] 1. A user accesses the system at a terminal and enters their username and password on the login screen.

[2036] 2. The server receives the entered username and password.

[2037] Data processing and calculation:

[2038] The server compares the input password with the hashed password in its database to see if they match.

[2039] Output: If authentication is successful, transition to the main screen. If authentication fails, display an error message.

[2040] Step 2: Enter your goals

[2041] Enter: "Cut CO2 emissions by 30% by 2025."

[2042] Specific behavior:

[2043] 1. After the user logs in, they will be taken to the goal setting screen.

[2044] 2. A user types "Reduce CO2 emissions by 30% by 2025" into a text box.

[2045] 3. The user clicks the "Submit" button.

[2046] Data processing and calculation:

[2047] The server receives the input, checks the format of the data, and converts it if necessary, for example checking the date format or numbers.

[2048] Output: The converted target data is sent to the server.

[2049] Step 3: Save your goal

[2050] Input: Transformed target data

[2051] Specific behavior:

[2052] 1. The server saves the goal data in a database.

[2053] 2. Metadata such as goal ID, user ID, and setting date and time are also stored in the database.

[2054] Data processing and calculation:

[2055] Convert the data format to SQL and insert it into the database.

[2056] Output: Goal setting completion message

[2057] Annual plan creation process flow

[2058] Step 1: Access the user's annual plan screen

[2059] Input: None (accessed by screen transition)

[2060] Specific behavior:

[2061] 1. The user opens the annual plan creation screen on their device.

[2062] 2. The planning template will be displayed.

[2063] Step 2: Enter your data

[2064] Input: "Energy consumption: 500kWh" "Current CO2 emissions: 100 tons"

[2065] Specific behavior:

[2066] 1. The user enters energy consumption of 500 kWh or current CO2 emissions of 100 tons into the template.

[2067] 2. After entering the information, the user clicks the "Submit" button.

[2068] Data processing and calculation:

[2069] The server receives the data, checks the format, and checks whether it is a numeric format.

[2070] Output: Validated data is passed to the server.

[2071] Step 3: Automatically generate annual plans

[2072] Input: Validated Data

[2073] Specific behavior:

[2074] 1. The server analyzes the verified data using an AI model.

[2075] 2. The model generates an optimal annual plan.

[2076] Data processing and calculation:

[2077] A generative AI model analyzes input data and derives recommended measures and action plans.

[2078] Output: Generated annual planning data

[2079] Step 4: Annual plan presentation and approval

[2080] Input: Generated annual plan data

[2081] Specific behavior:

[2082] 1. The server presents the generated annual plan to the user.

[2083] 2. The user reviews the plan and, if satisfied, clicks the "Approve" button.

[2084] Data processing and calculation:

[2085] Add approval data and finalize the annual plan.

[2086] Output: Confirmed annual plan

[2087] Processing flow for prediction and reduction simulation

[2088] Step 1: Open the simulation screen

[2089] Input: None (accessed by screen transition)

[2090] Specific behavior:

[2091] 1. The user opens the simulation screen on their device.

[2092] 2. A menu will appear for selecting the items to be simulated.

[2093] Step 2: Selecting simulation items and entering data

[2094] Input: "Installation of new energy-efficient equipment"

[2095] Specific behavior:

[2096] 1. The user selects the items to be simulated and enters the necessary data, such as "power consumption of new equipment," "installation cost," and "estimated lifespan."

[2097] 2. The user clicks the "Simulate" button.

[2098] Data processing and calculation:

[2099] The server receives the input data and performs format validation and format conversion.

[2100] Output: The transformed input data

[2101] Step 3: Run the simulation and present the results

[2102] Input: The converted input data

[2103] Specific behavior:

[2104] 1. The server runs a simulation using the AI ​​model.

[2105] 2. A generative AI model calculates the projected savings and cost benefits.

[2106] 3. The server presents the calculation results to the user.

[2107] Data processing and calculation:

[2108] Calculate the predicted reductions, cost effectiveness, etc. and compile the results.

[2109] Output: Simulation result data (e.g., "Predicted reduction: 20 tons, cost effect: 50,000 yen")

[2110] The above are the typical processing steps and their specific operations of the present invention, and show in detail how the system functions to help users achieve their environmental goals.

[2111] (Application example 2)

[2112] 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."

[2113] Setting environmental goals and creating annual plans based on them takes time and effort for many users, making it particularly difficult to maintain motivation. Furthermore, systems that ignore user emotions make it difficult to implement sustainable environmental measures, and their effectiveness is often limited. In particular, in the case of eco-driving in autonomous vehicles, the lack of feedback and assistance that takes user emotions into account makes it difficult to achieve goals such as improving fuel efficiency.

[2114] 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 saving input environmental targets, means for generating an annual plan based on the environmental targets, means for predicting simulation results of specified reduction measures, means for generating proposals necessary for environmental improvement, means for recognizing the user's emotions and providing feedback in real time, means for inputting driving data and generating an eco-driving plan, and means for providing feedback to improve motivation according to the user's emotional state. This makes it possible to implement effective environmental measures and eco-driving while maintaining the user's motivation.

[2115] "Environmental Goals" refers to specific targets or outcomes regarding environmental protection and sustainability set by a user.

[2116] "Annual Plan" refers to a plan that is implemented in stages throughout the year to achieve set environmental targets.

[2117] "Simulation results of reduction measures" refers to the predicted results obtained when simulating specific environmental measures or reduction measures.

[2118] "Carbon credit trading" refers to the transaction of buying and selling rights to reduce CO2 emissions.

[2119] "Subsidy Information" refers to detailed information about financial support provided for environmental and energy conservation projects.

[2120] An "emotion engine" refers to a system that analyzes a user's emotions and provides dynamic feedback based on those emotions.

[2121] "Real-time feedback" refers to feedback provided instantly in response to a user's input or actions.

[2122] "Driving data" refers to data related to the user's driving behavior and the vehicle's operating conditions.

[2123] An "eco-driving plan" refers to a driving method or plan that improves fuel efficiency.

[2124] "Motivational feedback" refers to encouragement or suggestions provided to motivate users.

[2125] This invention is a system that supports more effective environmental measures by improving the user's motivation in the process from setting environmental goals to managing their achievement by combining an emotion engine that recognizes the user's emotions. The following describes in detail an embodiment of the invention. This system functions by combining a server, a terminal, and an emotion engine.

[2126] goal setting

[2127] The user logs into the system using a terminal and enters the required environmental goals into the form on the screen. For example, a specific goal can be set, such as "improving fuel efficiency by 15% annually." The server receives this goal and stores it in a database. This saved goal data becomes the basis for future annual plans and simulations.

[2128] Creating an annual plan

[2129] The user accesses the system again and opens the annual plan creation screen. This screen displays a template for entering driving data and current fuel consumption data. Once the user enters this data, the server uses AI technology to analyze the entered data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal for approval.

[2130] Prediction and reduction simulation

[2131] When a user wants to simulate a specific reduction measure, they select the item to be simulated on the simulation screen and enter the necessary data. For example, if they want to confirm the effect of introducing a new energy-saving driving method, they enter the details. The server uses AI to run the simulation based on this input data. The server then presents the simulation results, such as predicted fuel efficiency improvements and cost effects, to the user.

[2132] Sentiment Analysis and Feedback

[2133] The emotion engine analyzes the user's emotions in real time while the user is driving the vehicle. For example, if the user is feeling high stress, the server will send suggestions for relaxation methods and advice on safe driving. Conversely, if the user is maintaining motivation, the server will send encouraging messages. This helps keep the user highly motivated and helps them achieve their environmental goals.

[2134] Providing feedback

[2135] The server periodically analyzes the user's driving data and emotional data to evaluate their eco-driving achievement rate and areas for improvement. Based on this information, the server generates feedback and presents it to the user. For example, it may provide a message such as, "Your driving efficiency has improved by 10%. Keep it up next month!"

[2136] Examples and prompts

[2137] For example, if a user operates the app after a long drive and is judged to be "stressed," the server will suggest "5 minutes of deep breathing exercises." Also, if the user achieves their eco-driving goal, the server will display rewards such as points or coupons.

[2138] Example prompt sentence:

[2139] "When users set eco-driving goals, they input their daily driving data and the AI ​​will suggest the optimal driving plan. The emotion engine analyzes emotions in real time and provides appropriate feedback and suggestions."

[2140] This system is implemented using smartphones and vehicle infotainment systems. It uses the emotion engine for emotion analysis, the driving_data_analysis library for driving data analysis, and the feedback_system library for feedback provision. This allows users to maintain their motivation while driving efficiently and eco-friendly.

[2141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2142] Step 1:

[2143] A user logs into the system using a terminal and inputs their environmental goals. This input includes specific targets such as "improving fuel efficiency by 15% annually." The server receives this input data and stores it in a database. The input data is saved as target data and is used as the basis for subsequent annual plans and simulations.

[2144] Step 2:

[2145] The user accesses the system again and opens the annual plan creation screen. On this screen, they are prompted to enter driving data (mileage, fuel economy, etc.) according to a template. The server uses AI technology to analyze the entered driving data. Specifically, it uses the driving_data_analysis library to analyze the data and automatically generate an optimal annual plan. The generated annual plan is presented to the user as a proposal.

[2146] Step 3:

[2147] When a user wants to simulate a specific reduction measure, they select the specific measure (e.g., introducing energy-saving driving methods) on the simulation screen and enter the necessary data. The server uses AI to run the simulation based on this input data. It predicts the energy-saving and cost-effectiveness effects from the input data and presents the results to the user.

[2148] Step 4:

[2149] While the user is driving the vehicle, the emotion engine analyzes the user's emotions in real time. For example, emotion analysis is performed using data collected by cameras and sensors. If the analysis shows that the user is feeling high stress, the server sends advice on relaxation methods and safe driving through a feedback system. Emotional state data is input in real time and output as the emotion analysis results.

[2150] Step 5:

[2151] The server periodically analyzes the user's driving data and emotional data. This analysis includes past driving records and emotional states. Based on this data, the server evaluates the user's eco-driving achievement rate and areas for improvement. The server analyzes the driving data using the driving_data_analysis library and the emotional data using the emotion_engine, and provides the results as feedback to the user.

[2152] Step 6:

[2153] The feedback system generates messages and suggestions to maintain or improve the user's motivation. For example, it sends a message such as, "Your driving efficiency has improved by 10%. Let's keep it up next month!" This increases the user's motivation and supports sustainable eco-driving.

[2154] Through the above steps, users can maintain their motivation while taking effective environmental measures and practicing eco-driving.

[2155] 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.

[2156] 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.

[2157] 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.

[2158] 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.

[2159] 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.

[2160] 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.

[2161] 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 visib...

Claims

1. a means for storing the input environmental goals; a means of generating annual plans based on environmental goals; a means for predicting the simulation results of a specified abatement measure; a means for generating recommendations for environmental improvement; measures to support carbon credit trading; A means of collecting and providing subsidy information to users A system including:

2. The system according to claim 1, further comprising means for utilizing AI to propose an annual plan based on the target data stored in the database.

3. The system according to claim 1, further comprising means for generating a plurality of simulation results using AI based on input data and presenting the results to the user.

Images