system

Abstract

Provide a system. 【Solution means】 An information input means for inputting the goals of an enterprise, A processing means for automatically generating an optimal action plan based on the goals of the enterprise, A data processing means for collecting and analyzing data from a measuring device in real time, A display means for visualizing and displaying the analysis results, A report generation means for evaluating the progress and automatically generating a report, A database device for accumulating and updating past performance data in real time, A control means for cooperating with the measuring device and dynamically optimizing the machine operation in the factory, A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to provide a system for efficiently implementing environmental protection activities while coping with the complexity of environmental regulations in medium-scale manufacturing industries. Currently, there are problems that setting environmental goals, collecting data, and complying with regulations are time-consuming, and it is difficult to implement effective CSR activities. It is required to solve such problems and help build a sustainable business model.

Means for Solving the Problems

[0005] This invention provides an interface for inputting a company's environmental targets and automatically generates an optimal action plan based on these targets. It also features the ability to collect environmental data from various sensors in real time and analyze it over time. The analysis results are visualized, and progress reports are automatically generated. Furthermore, it aims to improve corporate sustainability by predicting environmental impact and proposing proactive measures against risks. These system elements work together to ensure reliable and efficient compliance with regulations while reducing environmental impact.

[0006] An "interface" refers to the screen or operating method that users use to input a company's environmental targets.

[0007] "Automatic action plan generation" is a process that uses AI technology to automatically create optimal environmental protection activity plans tailored to a company's environmental goals.

[0008] A "sensor" is a measuring device used to collect environmental data from the field.

[0009] "Collecting and analyzing data in real time" means acquiring environmental data instantly and analyzing that data immediately.

[0010] "Visualization" refers to displaying the results of data analysis in an easy-to-understand format, such as graphs and charts.

[0011] "Evaluating progress and automatically generating reports" means evaluating the progress of environmental protection activities and automatically creating reports based on the results.

[0012] "Environmental impact" is a general term for the effects that a company's activities have on the natural environment.

[0013] "Proposing proactive measures against risks" means predicting future environmental risks and outlining strategies to prevent them. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. ]>

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is an AI agent system for medium-sized manufacturing companies to efficiently implement environmental protection activities. The system consists of terminals, servers, and users who operate and input data into them.

[0036] The server first receives the company's environmental objectives entered by the user via the terminal interface and stores them in a database. This includes objective settings, deadlines for achievement, and specific numerical targets. Next, based on this information, the server leverages its large-scale language model to automatically generate an action plan suitable for the objectives. The generated plan is optimized based on historical data and existing regulatory information.

[0037] The terminal works in conjunction with sensors installed on-site to collect necessary environmental data in real time. This data is immediately sent to a server, which analyzes it instantly. The analysis results, such as changes in CO2 emissions and trends in temperature and humidity, are displayed on the terminal as a visual dashboard.

[0038] Users can monitor progress at any time through the provided dashboard and adjust action plans or provide additional instructions as needed. The server automatically evaluates progress at regular intervals and generates a report on CSR activities based on the results. This report is used to communicate the company's environmental efforts to management and stakeholders.

[0039] For example, if a company sets a goal of reducing CO2 emissions by 10% in one year, the server will generate an action plan that includes energy efficiency measures and improvements to production processes aligned with this goal. It also measures progress based on collected real-time data, analyzes predictive risks, and provides advance notifications.

[0040] In this way, the entire system works together to reduce environmental impact and contribute to sustainable business operations.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses the terminal interface to input the company's environmental goals and specific parameters (e.g., CO2 emission reduction targets and deadlines). The terminal then compiles this information and sends it to the server.

[0044] Step 2:

[0045] The server receives environmental targets submitted by users and stores them in a database. These targets are stored along with historical performance data and existing environmental regulation information, making them available for later processes.

[0046] Step 3:

[0047] The server automatically generates an optimal action plan using an AI model based on the stored environmental targets and information in the database. Here, it proposes specific and feasible measures, taking into account the company's past success stories and current environmental regulations.

[0048] Step 4:

[0049] The terminal uses sensors on-site to collect environmental data (e.g., CO2 concentration, temperature, humidity) in real time. This also includes integration with various IoT devices.

[0050] Step 5:

[0051] The server instantly analyzes environmental data sent from the terminal. Data analysis includes trend analysis, anomaly detection, and assessment of the current environmental load.

[0052] Step 6:

[0053] The server visualizes the analyzed data and displays it on the terminal in a dashboard format. On the terminal, users can check the degree of achievement of environmental targets and newly identified risk information.

[0054] Step 7:

[0055] The user checks the device's dashboard and reviews the action plan suggested by the AI ​​agent. They can approve or modify the plan as needed.

[0056] Step 8:

[0057] The server evaluates the progress of the action plan at regular intervals and automatically generates a report. This report is used to visualize the company's CSR activities and to report on its environmental efforts to stakeholders.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] Currently, implementing environmental protection activities in companies involves a complex process, from setting goals and developing action plans to real-time monitoring of implementation status, to evaluating progress and providing feedback based on results. This often results in manual data entry, analysis, and report creation, leading to challenges in efficiency and accuracy. Furthermore, the prediction of environmental impact and the development of risk avoidance measures lack immediacy, highlighting the need for the development of efficient environmental management systems.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for providing an information display device for inputting a company's environmental targets, means for automatically generating an optimal activity plan based on the company's environmental targets and a specific generative model, and means for collecting and analyzing environmental data obtained from information sensors. This enables companies to efficiently manage their environmental protection activities, appropriately evaluate progress, and quickly identify areas for improvement.

[0063] An "information display device" is a device that includes an input device and an interface for users to input a company's environmental targets.

[0064] A "generative model" is a type of large-scale data model used to automatically generate optimal action plans under specific conditions, based on historical data and regulatory information.

[0065] An "activity plan" is an action plan that specifically outlines the actions and improvement measures necessary to achieve a company's environmental goals.

[0066] An "information sensor" is a type of sensor device used to collect environmental data in real time, capable of measuring temperature, humidity, CO2 concentration, and other parameters.

[0067] An "output device" is a device used to visually display data analysis results, allowing users to easily check progress and trends.

[0068] A "data storage device" is a digital storage medium used to record and instantly update a company's environmental targets and past performance information.

[0069] A "prompt" is a command or input statement used when generating an activity plan using a generative model.

[0070] In this embodiment of the invention, the system is configured and operates as follows.

[0071] The server collects and stores information entered by users via terminals in order to effectively manage the company's environmental objectives. The objective information entered through the information display device includes the company's objective settings, numerical criteria, and deadlines for achievement. The server stores and manages this information in a data storage device.

[0072] Furthermore, the server utilizes a pre-configured generative AI model to automatically generate appropriate action plans based on the company's environmental goals. This generative model incorporates historical data and environmental regulatory information, enabling the development of highly effective plans. For example, it can utilize a prompt such as, "Generate a specific action plan to reduce CO2 emissions by 10% annually."

[0073] The terminal works in conjunction with information sensors installed on-site to collect environmental data in real time. The collected data is immediately transmitted from the terminal to the server. The server analyzes this data, visualizes the analysis results, and displays them on the terminal via an output device. This allows the user to monitor progress in real time.

[0074] Users can use the provided dashboard to check their progress toward achieving environmental goals and, if necessary, adjust their action plans or issue additional directives. In particular, modifying action plans using generative AI models enables truly efficient environmental management.

[0075] Through this system, companies can efficiently manage their environmental impact and support sustainable operations. The server also automatically generates regular reports, allowing companies to promptly inform management and stakeholders about their environmental management efforts. This reporting function plays a crucial role in enabling companies to implement continuous improvement measures and maintain transparency regarding environmental protection.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users input the company's environmental targets using the terminal's information display device. The entered data includes specific target values, deadlines, and objectives. The terminal transmits this information as data packets to the server. The server receives this input and stores it in a data storage device.

[0079] Step 2:

[0080] The server extracts stored environmental target data and inputs prompts into the generating AI model. For example, it might be given the command, "Generate a specific action plan to reduce CO2 emissions by 10% annually." Based on this prompt, the generating AI model automatically generates an action plan. The resulting plan is then processed to further refine it and make it shareable with users.

[0081] Step 3:

[0082] The terminal works in conjunction with information sensors installed on-site to collect environmental data such as CO2 concentration, temperature, and humidity in real time. These data packets are immediately transmitted from the terminal to the server. The server processes the received data using data analysis algorithms to analyze changes and trends in the input data.

[0083] Step 4:

[0084] The server converts the analyzed data into visual information and generates a dataset for the dashboard. This output is sent to the terminal and displayed to the user through the terminal's output device. Through this visual information, the user can check the progress and future predictions in real time.

[0085] Step 5:

[0086] Based on the information on the dashboard, users adjust their activity plans as needed. This input is sent to the server via the terminal, which uses a generative AI model to receive new instructions and regenerate or modify the activity plan. This ensures that the activity plan is adapted to the latest situation.

[0087] Step 6:

[0088] The server automatically evaluates progress at regular intervals and generates a report. This report includes achievement status and improvement suggestions, and is stored in a data storage device and provided to users and stakeholders. This ensures transparent information sharing regarding the company's environmental initiatives.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In manufacturing, efficient environmental management and sustainable production practices are increasingly important challenges for modern businesses. Traditional systems have made it difficult to integrate environmental data collection and analysis, and action planning, and to reflect these in actual manufacturing processes. Furthermore, there has been a lack of means to flexibly respond to real-time, fluctuating environmental conditions and achieve effective energy efficiency.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes processing means for automatically generating an optimal action plan based on the company's goals, data processing means for collecting and analyzing data from measuring devices in real time, and control means for dynamically optimizing factory machine operations in cooperation with the measuring devices. This enables efficient and sustainable production operations utilizing environmental data.

[0094] "Information input means" refers to devices or interfaces that allow users to input a company's goals.

[0095] A "processing device" is a device that has the function of automatically generating an optimal action plan based on a company's objectives.

[0096] "Data processing means" refers to technologies for collecting and analyzing data acquired from measuring devices in real time.

[0097] "Display means" refers to monitors or dashboards used to visually present analysis results.

[0098] A "report generation means" is a means that has the function of evaluating progress and automatically creating a report based on that evaluation.

[0099] A "database device" is a system that stores historical performance data and updates it in real time as needed.

[0100] "Control means" refers to technologies for dynamically optimizing machine operations within a factory based on environmental data.

[0101] A "control algorithm" is a methodology for adjusting the operation of mechanical devices in order to achieve energy efficiency.

[0102] The system implementing this invention involves servers, terminals, and users working together to achieve environmental goals set by a company. At its core, the server performs advanced processing using various means.

[0103] The server provides an information input mechanism for users to input company goals. The goal information entered through this mechanism is stored in a database on the server. Based on this, the server automatically generates an optimal action plan through a processing mechanism using a generative AI model. The generated action plan is updated or optimized each time based on past performance data.

[0104] Furthermore, the terminals work in conjunction with various sensors installed within the factory to collect environmental data in real time. This collected data is immediately transmitted to a server and analyzed by data processing tools. The analysis results are displayed on the terminals using various data visualization tools, allowing users to check the progress.

[0105] Based on the analysis results, the server dynamically optimizes machine operations within the factory using control mechanisms. This function contributes to energy efficiency and increased productivity, supporting sustainable operations. For example, it optimizes energy use by automatically adjusting the air conditioning system based on real-time data from temperature sensors.

[0106] Furthermore, the system incorporates a reporting mechanism that automatically generates reports by evaluating the degree of achievement of action plans in accordance with progress, which is useful for transparent reporting to management and stakeholders.

[0107] As a concrete example, when aiming to reduce energy consumption in a factory, the server proposes the optimal operation of manufacturing equipment. An example of a prompt message supplied to the generated AI model is, "To reduce energy consumption in the factory, please propose an efficient operation plan for the air conditioning system using real-time data from temperature sensors."

[0108] In this way, this invention utilizes advanced data processing and control technologies to provide comprehensive support essential for achieving a company's environmental goals.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input their company's environmental goals into the server using an information input device. This input includes specific numerical targets and deadlines for achievement. The server stores this information in a database and uses it as basic data for future processing.

[0112] Step 2:

[0113] The server automatically generates an optimal action plan using a generative AI model based on the input target information. In this process, the server analyzes past performance data and relevant external information to create the action plan. The output provides specific tasks and execution schedules.

[0114] Step 3:

[0115] The terminals work in conjunction with various sensors placed throughout the factory to collect environmental data in real time. This data includes CO2 emissions, temperature, humidity, and other parameters. This data is immediately transmitted to a server for analysis.

[0116] Step 4:

[0117] The server analyzes the collected environmental data using data processing tools. Based on the measurement data, it detects anomalies and performs trend analysis to understand environmental changes. The results are output as analysis results and used in the next step.

[0118] Step 5:

[0119] Based on the analysis results, the server utilizes control mechanisms to dynamically optimize machine operations in the factory. Specifically, it makes adjustments aimed at optimizing equipment energy consumption and reducing emissions. As a result, the optimized production process conditions are output.

[0120] Step 6:

[0121] Users can view analysis results and progress information on their terminals. A visual dashboard provided by the server allows users to easily understand the progress and revise plans or provide additional instructions as needed. Operational instructions and adjustment suggestions are generated as output.

[0122] Step 7:

[0123] The server automatically generates reports based on various data and progress. These reports summarize past activities, results, and suggestions for future improvements, providing transparent feedback to company management and stakeholders. The output is the final report, stored in a shareable format.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is an AI agent system designed to enable medium-sized manufacturing companies to efficiently carry out environmental protection activities, optimizing user interaction by combining it with an emotion engine. The system consists of three main components: a terminal, a server, and a user.

[0126] First, the device provides the user with an interface for inputting environmental goals. Here, the emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state at that moment. This information is used to improve the user experience; for example, if the user is unmotivated, the device can provide motivational feedback.

[0127] The server stores the entered environmental goals in a database and uses an AI model to generate the optimal action plan. The proposed plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the system prioritizes presenting plans that are achievable with ample time.

[0128] The device collects environmental data in real time via IoT devices and sends it to a server. This data is analyzed and visualized on the server and presented to the user on the device's dashboard. The user can check their progress and adjust their plan as needed. Furthermore, the emotion engine plays a role in reducing psychological burden by displaying relaxing messages when the user's emotions are negative.

[0129] As a concrete example, consider a case where a company sets a goal of "reducing energy consumption by 20% within six months." If the emotion engine determines that the user is experiencing emotional stress, the server can flexibly modify the plan, such as suggesting a rest day to incorporate relaxation into the intermediate goal.

[0130] In this way, systems that combine an emotional engine enable companies to carry out environmental protection activities in a more humane and efficient manner.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user inputs the company's environmental goals using the device's interface. Upon input, an emotion engine is activated, analyzing the user's emotional state in real time based on their facial expressions and voice. The device then provides feedback, estimating the user's level of motivation based on the analysis results.

[0134] Step 2:

[0135] The server receives data on environmental goals and emotional states transmitted from the terminal. The received data is stored in a database and used as material for analysis by an AI model. This prepares the server to generate an optimal action plan that takes the user's emotional state into account.

[0136] Step 3:

[0137] The server uses an AI model to automatically generate specific action plans aligned with environmental goals. If the user is experiencing stress, the system is configured to recommend realistic and less burdensome plans.

[0138] Step 4:

[0139] The terminal collects environmental data in real time by connecting with on-site sensors. The collected data is immediately sent to the server, allowing for continuous monitoring of progress.

[0140] Step 5:

[0141] The server analyzes and visualizes environmental data, displaying the results on the terminal's dashboard. This allows users to see at a glance their progress towards their initial environmental goals.

[0142] Step 6:

[0143] Users can check their progress on the device's dashboard and adjust their action plan as needed, based on feedback from the emotion engine. If emotions are perceived negatively, the device will display messages designed to promote relaxation.

[0144] Step 7:

[0145] The server automatically generates periodic CSR activity reports based on progress information and user feedback. These reports take into account changes in sentiment and include comparative analysis with past data.

[0146] Step 8:

[0147] Users can review the report and, if further optimization through the emotion engine is needed, use the data to make an overall review of their activities. This facilitates steady progress toward achieving long-term environmental goals.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0150] Corporate environmental protection activities require the generation of efficient action plans that take into account users' emotional states, as well as progress management through real-time data analysis and visualization. However, conventional systems often present uniform plans that do not consider user emotions, making it difficult to maintain motivation. Furthermore, the collection, analysis, and visualization of environmental data are not sufficiently automated, and user-friendly interfaces are not provided.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes a terminal device for inputting company information, an emotion analysis device equipped with means for recognizing the user's emotional state, a computing device using a generative model that automatically generates an optimal work plan based on the company information, means for aggregating and analyzing information from a data collection device in real time, a display device for visualizing and presenting the analysis results, and means for generating motivational messages according to the user's emotional state. This enables the presentation of flexible action plans that take the user's emotional state into consideration, as well as progress management and motivation based on real-time data.

[0153] A "corporation" is a legally established organization that engages in activities aimed at achieving specific objectives, such as commercial activities or environmental activities.

[0154] A "terminal device" is an electronic device used by users to input and receive information, and generally includes a display and input interface.

[0155] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0156] A "generative model" is a mathematical model that uses algorithms based on data to automatically generate optimal plans and predictions.

[0157] A "computational device" refers to hardware or software used to perform data processing and calculations, enabling the execution of generative models.

[0158] A "data acquisition device" is a device that acquires information from the external environment or internal processes, and sensors are commonly used for this purpose.

[0159] "Analysis results" refer to the conclusions and information derived from analyzing the collected data.

[0160] A "display device" is a screen-based device that visually presents data and analysis results, and is used by users to make decisions and take actions based on that information.

[0161] A "motivational message" is an inspiring message provided to motivate users and promote goal achievement.

[0162] In this invention, three main elements—a terminal, a server, and a user—work together to provide a system that supports efficient environmental protection activities for businesses.

[0163] terminal

[0164] The terminal provides an interface for users to input environmental goals. This interface includes input devices such as a touchscreen or keyboard. The terminal incorporates an emotion analyzer that captures the user's facial expressions with a camera and collects their voice tone with a microphone to analyze the user's emotional state in real time. For example, if the user shows a tired expression, the terminal recognizes this information using the emotion analyzer.

[0165] server

[0166] The server stores the company's environmental targets, submitted from the terminal, in a database. Database management typically uses database software such as PostgreSQL. The server also incorporates a generative AI model that generates optimal action plans based on the stored data. Large-scale models like OpenAI® are used as the generative AI model. The generated plan is adjusted based on the user's emotional state. For example, if the user is experiencing stress, the server presents a feasible plan that breaks down tasks to alleviate stress.

[0167] Specific example

[0168] For example, if a company sets a goal of "reducing energy consumption by 20% within six months," the server will generate a feasible action plan based on this goal. Specifically, it might suggest a plan such as "aiming for a 10% reduction in the first two months, and adjusting the target for the second half as progress is made."

[0169] Example of a prompt

[0170] When using generative AI models, the following prompts are used.

[0171] "Please propose a concrete action plan to achieve the user's environmental goal of 'reducing energy consumption by 20% within six months.' The user's current emotional state is judged to be stressed."

[0172] By using a system configured in this way, users can engage in efficient and sustainable environmental protection activities while taking their emotions into consideration.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The terminal provides a screen for users to input environmental targets through an interface. Specific target data entered might include, for example, "Reduce waste by 15% within three months." The entered data is temporarily stored within the terminal and prepared for transmission to the server.

[0176] Step 2:

[0177] The device captures the user's facial expressions with a camera and collects their voice tone with a microphone. This data is input into an emotion analysis device to analyze the user's emotional state. The analysis results are output as an emotional state, such as "the user is relaxed" or "the user is stressed," and sent to a server.

[0178] Step 3:

[0179] The server receives environmental target data and emotional state data from the terminal and stores it in a database. For example, PostgreSQL is used for database storage. Using the stored data, the server applies a generative AI model to generate an optimal action plan. The inputs in this process are environmental targets and emotional states, and the output is a specific action plan.

[0180] Step 4:

[0181] The server further refines the generated action plan to match the user's emotional state. For example, if the user is stressed, the action plan is broken down into tasks to alleviate that stress. This refined plan is then sent to the terminal as the final output.

[0182] Step 5:

[0183] The terminal visually presents the action plan received from the server to the user. A dashboard is used for this presentation, showing progress and the next steps. Based on this information, the user can plan actions to achieve their goals. An interface is also provided for the user to provide feedback as needed.

[0184] Step 6:

[0185] When a user begins taking action based on an action plan, the device collects environmental data in real time via IoT devices and transmits it to a server. This data includes, for example, measurements of energy consumption and waste generation. The server aggregates and analyzes this data, generates progress visualization data, and sends the results back to the device.

[0186] In this way, the entire system works closely together, enabling users to carry out highly efficient environmental protection activities while also being considerate of their emotions.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In environmental protection activities, it is necessary not only to provide efficient action plans, but also to take into account the emotional state of users and optimize their individual experiences. This requires a system that effectively achieves environmental goals while maintaining motivation for the activities.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for providing a device for inputting a company's environmental targets, means for providing a design for analyzing emotional states and improving the user experience, and means for acquiring emotional analysis data in real time and providing the next steps toward achieving the environmental targets. This enables flexible adjustment of action plans optimized for the user and improvement of the individual user experience.

[0192] "Corporate environmental targets" are indicators used by companies to set specific environmental goals, such as sustainability and improved energy efficiency.

[0193] An "action plan" is a plan that outlines the specific steps and strategies necessary to achieve environmental goals.

[0194] "Emotional state" refers to the user's current psychological or emotional state, and is information analyzed through facial expressions, voice, and other means.

[0195] "Improving the user experience" refers to optimizing interaction design to increase user satisfaction and efficiency when using a system.

[0196] "Emotional analysis data" refers to data collected to evaluate a user's emotions, and is information obtained through an emotion engine.

[0197] "Real-time data acquisition" refers to the immediate processing of data and information without delay, demonstrating the system's ability to respond quickly on the spot.

[0198] This invention constructs a system to support companies' environmental protection activities. The system consists of three main components: terminals, servers, and users.

[0199] The terminal functions as a device for users to input the company's environmental goals. Here, the terminal uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. The acquired data is analyzed by an emotion engine to evaluate the user's emotional state. Software such as OpenCV and TENSORFLOW® is used for the analysis. Based on this information, the system provides user-optimized feedback to support user motivation.

[0200] The server uses an AI model to generate an optimal action plan based on the input environmental goals and environmental data collected in real time. This process involves data processing using Node.js and AWS® Lambda, and database management using MongoDB. The action plan takes sentiment analysis data into account and is adjusted according to the user's emotional state.

[0201] Users can view analysis results and progress on their device's dashboard. This allows them to modify their action plans as needed and manage their actions toward achieving their goals in real time.

[0202] For example, if a user at a certain company sets a goal of "reducing electricity consumption by 10% within one month," the emotion engine can understand the user's emotional state and suggest relaxation breaks or actionable energy-saving measures as needed. An example of a prompt message might be, "The user's current emotional state is positive / neutral / negative. Please suggest the next steps to reduce energy consumption."

[0203] In this way, users can engage in environmental protection activities in a more human-centered way by utilizing a combination of an emotion engine and an AI model.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The terminal receives the company's environmental targets from the user via an input device. The entered target information is temporarily stored in the terminal's memory.

[0207] Step 2:

[0208] The device uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion engine, where it is analyzed to evaluate the user's emotional state. The input consists of facial expression data and voice data, and the output is the result of the emotional state evaluation.

[0209] Step 3:

[0210] The server receives environmental goals and the user's emotional state from the terminal. Based on the received data, the server runs a generative AI model to create an optimal action plan. The input is the environmental goals and emotional state, and the output is the action plan.

[0211] Step 4:

[0212] The server collects environmental data from IoT devices to adjust the action plan in real time. This data is processed using Node.js, and the feedback is incorporated into the action plan. The input is environmental data, and the output is the improved action plan.

[0213] Step 5:

[0214] The terminal visualizes and displays the final action plan and analysis results on a dashboard. Based on this information, users can consider various options and modify their action plan if necessary. The inputs are the action plan and analysis results, while the output is the information presented to the user.

[0215] Step 6:

[0216] Users monitor progress and evaluate action plans through a dashboard. User feedback is sent to the server, which automatically generates reports based on that feedback. The input is user feedback, and the output is the generated report.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is an AI agent system for medium-sized manufacturing companies to efficiently implement environmental protection activities. The system consists of terminals, servers, and users who operate and input data into them.

[0234] The server first receives the company's environmental objectives entered by the user via the terminal interface and stores them in a database. This includes objective settings, deadlines for achievement, and specific numerical targets. Next, based on this information, the server leverages its large-scale language model to automatically generate an action plan suitable for the objectives. The generated plan is optimized based on historical data and existing regulatory information.

[0235] The terminal works in conjunction with sensors installed on-site to collect necessary environmental data in real time. This data is immediately sent to a server, which analyzes it instantly. The analysis results, such as changes in CO2 emissions and trends in temperature and humidity, are displayed on the terminal as a visual dashboard.

[0236] Users can monitor progress at any time through the provided dashboard and adjust action plans or provide additional instructions as needed. The server automatically evaluates progress at regular intervals and generates a report on CSR activities based on the results. This report is used to communicate the company's environmental efforts to management and stakeholders.

[0237] For example, if a company sets a goal of reducing CO2 emissions by 10% in one year, the server will generate an action plan that includes energy efficiency measures and improvements to production processes aligned with this goal. It also measures progress based on collected real-time data, analyzes predictive risks, and provides advance notifications.

[0238] In this way, the entire system works together to reduce environmental impact and contribute to sustainable business operations.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user uses the terminal interface to input the company's environmental goals and specific parameters (e.g., CO2 emission reduction targets and deadlines). The terminal then compiles this information and sends it to the server.

[0242] Step 2:

[0243] The server receives environmental targets submitted by users and stores them in a database. These targets are stored along with historical performance data and existing environmental regulation information, making them available for later processes.

[0244] Step 3:

[0245] The server automatically generates an optimal action plan using an AI model based on the stored environmental targets and information in the database. Here, it proposes specific and feasible measures, taking into account the company's past success stories and current environmental regulations.

[0246] Step 4:

[0247] The terminal uses sensors on-site to collect environmental data (e.g., CO2 concentration, temperature, humidity) in real time. This also includes integration with various IoT devices.

[0248] Step 5:

[0249] The server instantly analyzes environmental data sent from the terminal. Data analysis includes trend analysis, anomaly detection, and assessment of the current environmental load.

[0250] Step 6:

[0251] The server visualizes the analyzed data and displays it on the terminal in a dashboard format. On the terminal, users can check the degree of achievement of environmental targets and newly identified risk information.

[0252] Step 7:

[0253] The user checks the device's dashboard and reviews the action plan suggested by the AI ​​agent. They can approve or modify the plan as needed.

[0254] Step 8:

[0255] The server evaluates the progress of the action plan at regular intervals and automatically generates a report. This report is used to visualize the company's CSR activities and to report on its environmental efforts to stakeholders.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] Currently, implementing environmental protection activities in companies involves a complex process, from setting goals and developing action plans to real-time monitoring of implementation status, to evaluating progress and providing feedback based on results. This often results in manual data entry, analysis, and report creation, leading to challenges in efficiency and accuracy. Furthermore, the prediction of environmental impact and the development of risk avoidance measures lack immediacy, highlighting the need for the development of efficient environmental management systems.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for providing an information display device for inputting a company's environmental targets, means for automatically generating an optimal activity plan based on the company's environmental targets and a specific generative model, and means for collecting and analyzing environmental data obtained from information sensors. This enables companies to efficiently manage their environmental protection activities, appropriately evaluate progress, and quickly identify areas for improvement.

[0261] An "information display device" is a device that includes an input device and an interface for users to input a company's environmental targets.

[0262] A "generative model" is a type of large-scale data model used to automatically generate optimal action plans under specific conditions, based on historical data and regulatory information.

[0263] An "activity plan" is an action plan that specifically outlines the actions and improvement measures necessary to achieve a company's environmental goals.

[0264] An "information sensor" is a type of sensor device used to collect environmental data in real time, capable of measuring temperature, humidity, CO2 concentration, and other parameters.

[0265] An "output device" is a device used to visually display data analysis results, allowing users to easily check progress and trends.

[0266] A "data storage device" is a digital storage medium used to record and instantly update a company's environmental targets and past performance information.

[0267] A "prompt" is a command or input statement used when generating an activity plan using a generative model.

[0268] In this embodiment of the invention, the system is configured and operates as follows.

[0269] The server collects and stores information entered by users via terminals in order to effectively manage the company's environmental objectives. The objective information entered through the information display device includes the company's objective settings, numerical criteria, and deadlines for achievement. The server stores and manages this information in a data storage device.

[0270] Furthermore, the server utilizes a pre-configured generative AI model to automatically generate appropriate action plans based on the company's environmental goals. This generative model incorporates historical data and environmental regulatory information, enabling the development of highly effective plans. For example, it can utilize a prompt such as, "Generate a specific action plan to reduce CO2 emissions by 10% annually."

[0271] The terminal works in conjunction with information sensors installed on-site to collect environmental data in real time. The collected data is immediately transmitted from the terminal to the server. The server analyzes this data, visualizes the analysis results, and displays them on the terminal via an output device. This allows the user to monitor progress in real time.

[0272] Users can use the provided dashboard to check their progress toward achieving environmental goals and, if necessary, adjust their action plans or issue additional directives. In particular, modifying action plans using generative AI models enables truly efficient environmental management.

[0273] Through this system, companies can efficiently manage their environmental impact and support sustainable operations. The server also automatically generates regular reports, allowing companies to promptly inform management and stakeholders about their environmental management efforts. This reporting function plays a crucial role in enabling companies to implement continuous improvement measures and maintain transparency regarding environmental protection.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] Users input the company's environmental targets using the terminal's information display device. The entered data includes specific target values, deadlines, and objectives. The terminal transmits this information as data packets to the server. The server receives this input and stores it in a data storage device.

[0277] Step 2:

[0278] The server extracts the stored environmental target data and inputs a prompt sentence into the generative AI model. For example, a command such as "Please generate a specific action plan to reduce CO2 emissions by 10% annually." is given. Based on this prompt, the generative AI model automatically generates an activity plan. The obtained plan is subjected to data shaping processing for further adjustment and made into a form that can be shared with the user.

[0279] Step 3:

[0280] The terminal collaborates with information sensors installed at the site and collects environmental data such as CO2 concentration, temperature, and humidity in real time. These data packets are immediately transmitted from the terminal to the server. The server processes the received data using a data analysis algorithm and analyzes the changes and trends in the input data.

[0281] Step 4:

[0282] The server converts the analyzed data into visual information and generates a data set for the dashboard. This output is transmitted to the terminal and displayed to the user through the output device on the terminal. The user can confirm the progress status and future predictions in real time through the visual information.

[0283] Step 5:

[0284] The user adjusts the activity plan as needed based on the information on the dashboard. This input is transmitted to the server via the terminal, and the server receives a new command using the generative AI model and regenerates or modifies the activity plan. As a result, the activity plan is adapted to the latest situation.

[0285] Step 6:

[0286] The server automatically evaluates the progress at regular intervals and creates a report. The report includes the achievement status and improvement plans, is stored in the data storage device, and provided to the user and stakeholders. This enables the company's environmental efforts to be transparently shared.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0289] In a manufacturing site, efficient environmental management and sustainable production activities are increasingly important issues for modern enterprises. In conventional systems, the collection and analysis of environmental data and the formulation of action plans were carried out individually, and it was difficult to integrate them and reflect them in the actual manufacturing process. In addition, there was a lack of means to flexibly respond to a changing environment in real time and achieve effective energy efficiency improvement.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes processing means for automatically generating an optimal action plan based on the goals of the enterprise, data processing means for collecting and analyzing data from the measuring device in real time, and control means for dynamically optimizing the machine operation in the factory in cooperation with the measuring device. As a result, efficient and sustainable production operation using environmental data becomes possible.

[0292] The "information input means" is a device or interface for the user to input the goals of the enterprise.

[0293] The "processing means" is a device having a function of automatically generating an optimal action plan based on the goals of the enterprise.

[0294] The "data processing means" is a technology for collecting and analyzing data acquired from the measuring device in real time.

[0295] The "display means" is a monitor or dashboard for visually presenting the analysis results.

[0296] A "report generation means" is a means that has the function of evaluating progress and automatically creating a report based on that evaluation.

[0297] A "database device" is a system that stores historical performance data and updates it in real time as needed.

[0298] "Control means" refers to technologies for dynamically optimizing machine operations within a factory based on environmental data.

[0299] A "control algorithm" is a methodology for adjusting the operation of mechanical devices in order to achieve energy efficiency.

[0300] The system implementing this invention involves servers, terminals, and users working together to achieve environmental goals set by a company. At its core, the server performs advanced processing using various means.

[0301] The server provides an information input mechanism for users to input company goals. The goal information entered through this mechanism is stored in a database on the server. Based on this, the server automatically generates an optimal action plan through a processing mechanism using a generative AI model. The generated action plan is updated or optimized each time based on past performance data.

[0302] Furthermore, the terminals work in conjunction with various sensors installed within the factory to collect environmental data in real time. This collected data is immediately transmitted to a server and analyzed by data processing tools. The analysis results are displayed on the terminals using various data visualization tools, allowing users to check the progress.

[0303] Based on the analysis results, the server dynamically optimizes the machine operations in the factory using the control means. This function contributes to energy efficiency improvement and productivity enhancement, and supports sustainable operations. For example, by automatically adjusting the air conditioning system based on real-time data from temperature sensors, energy usage is optimized.

[0304] In addition, a report generation means is incorporated that evaluates the degree of achievement of the action plan according to the progress and automatically generates a report, which is useful for transparent reporting to management and stakeholders.

[0305] As a specific example, when aiming to reduce energy in the factory, the server proposes optimal operation of manufacturing equipment. An example of the prompt text supplied to the generation AI model is "Please propose an efficient operation plan for the air conditioning system using real-time data from temperature sensors to reduce energy in the factory."

[0306] In this way, this invention makes full use of advanced data processing and control technologies and provides comprehensive support essential for enterprises to achieve their environmental goals.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user inputs the enterprise's environmental goals into the server using the information input means. This input includes specific numerical goals, deadlines, etc. The server stores this information in the database and uses it as the basic data for future processing.

[0310] Step 2:

[0311] Based on the input target information, the server automatically generates an optimal action plan using the generation AI model. In this process, the server analyzes past performance data and related external information to create an action plan. As output, specific tasks and execution schedules are obtained.

[0312] Step 3:

[0313] The terminals work in conjunction with various sensors placed throughout the factory to collect environmental data in real time. This data includes CO2 emissions, temperature, humidity, and other parameters. This data is immediately transmitted to a server for analysis.

[0314] Step 4:

[0315] The server analyzes the collected environmental data using data processing tools. Based on the measurement data, it detects anomalies and performs trend analysis to understand environmental changes. The results are output as analysis results and used in the next step.

[0316] Step 5:

[0317] Based on the analysis results, the server utilizes control mechanisms to dynamically optimize machine operations in the factory. Specifically, it makes adjustments aimed at optimizing equipment energy consumption and reducing emissions. As a result, the optimized production process conditions are output.

[0318] Step 6:

[0319] Users can view analysis results and progress information on their terminals. A visual dashboard provided by the server allows users to easily understand the progress and revise plans or provide additional instructions as needed. Operational instructions and adjustment suggestions are generated as output.

[0320] Step 7:

[0321] The server automatically generates reports based on various data and progress. These reports summarize past activities, results, and suggestions for future improvements, providing transparent feedback to company management and stakeholders. The output is the final report, stored in a shareable format.

[0322] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0323] This invention is an AI agent system designed to enable medium-sized manufacturing companies to efficiently carry out environmental protection activities, optimizing user interaction by combining it with an emotion engine. The system consists of three main components: a terminal, a server, and a user.

[0324] First, the device provides the user with an interface for inputting environmental goals. Here, the emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state at that moment. This information is used to improve the user experience; for example, if the user is unmotivated, the device can provide motivational feedback.

[0325] The server stores the entered environmental goals in a database and uses an AI model to generate the optimal action plan. The proposed plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the system prioritizes presenting plans that are achievable with ample time.

[0326] The device collects environmental data in real time via IoT devices and sends it to a server. This data is analyzed and visualized on the server and presented to the user on the device's dashboard. The user can check their progress and adjust their plan as needed. Furthermore, the emotion engine plays a role in reducing psychological burden by displaying relaxing messages when the user's emotions are negative.

[0327] As a concrete example, consider a case where a company sets a goal of "reducing energy consumption by 20% within six months." If the emotion engine determines that the user is experiencing emotional stress, the server can flexibly modify the plan, such as suggesting a rest day to incorporate relaxation into the intermediate goal.

[0328] In this way, systems that combine an emotional engine enable companies to carry out environmental protection activities in a more humane and efficient manner.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user inputs the company's environmental goals using the device's interface. Upon input, an emotion engine is activated, analyzing the user's emotional state in real time based on their facial expressions and voice. The device then provides feedback, estimating the user's level of motivation based on the analysis results.

[0332] Step 2:

[0333] The server receives data on environmental goals and emotional states transmitted from the terminal. The received data is stored in a database and used as material for analysis by an AI model. This prepares the server to generate an optimal action plan that takes the user's emotional state into account.

[0334] Step 3:

[0335] The server uses an AI model to automatically generate specific action plans aligned with environmental goals. If the user is experiencing stress, the system is configured to recommend realistic and less burdensome plans.

[0336] Step 4:

[0337] The terminal collects environmental data in real time by connecting with on-site sensors. The collected data is immediately sent to the server, allowing for continuous monitoring of progress.

[0338] Step 5:

[0339] The server analyzes and visualizes environmental data, displaying the results on the terminal's dashboard. This allows users to see at a glance their progress towards their initial environmental goals.

[0340] Step 6:

[0341] Users can check their progress on the device's dashboard and adjust their action plan as needed, based on feedback from the emotion engine. If emotions are perceived negatively, the device will display messages designed to promote relaxation.

[0342] Step 7:

[0343] The server automatically generates periodic CSR activity reports based on progress information and user feedback. These reports take into account changes in sentiment and include comparative analysis with past data.

[0344] Step 8:

[0345] Users can review the report and, if further optimization through the emotion engine is needed, use the data to make an overall review of their activities. This facilitates steady progress toward achieving long-term environmental goals.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] Corporate environmental protection activities require the generation of efficient action plans that take into account users' emotional states, as well as progress management through real-time data analysis and visualization. However, conventional systems often present uniform plans that do not consider user emotions, making it difficult to maintain motivation. Furthermore, the collection, analysis, and visualization of environmental data are not sufficiently automated, and user-friendly interfaces are not provided.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes a terminal device for inputting company information, an emotion analysis device equipped with means for recognizing the user's emotional state, a computing device using a generative model that automatically generates an optimal work plan based on the company information, means for aggregating and analyzing information from a data collection device in real time, a display device for visualizing and presenting the analysis results, and means for generating motivational messages according to the user's emotional state. This enables the presentation of flexible action plans that take the user's emotional state into consideration, as well as progress management and motivation based on real-time data.

[0351] A "corporation" is a legally established organization that engages in activities aimed at achieving specific objectives, such as commercial activities or environmental activities.

[0352] A "terminal device" is an electronic device used by users to input and receive information, and generally includes a display and input interface.

[0353] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0354] A "generative model" is a mathematical model that uses algorithms based on data to automatically generate optimal plans and predictions.

[0355] A "computational device" refers to hardware or software used to perform data processing and calculations, enabling the execution of generative models.

[0356] A "data acquisition device" is a device that acquires information from the external environment or internal processes, and sensors are commonly used for this purpose.

[0357] "Analysis results" refer to the conclusions and information derived from analyzing the collected data.

[0358] A "display device" is a screen-based device that visually presents data and analysis results, and is used by users to make decisions and take actions based on that information.

[0359] A "motivational message" is an inspiring message provided to motivate users and promote goal achievement.

[0360] In this invention, three main elements—a terminal, a server, and a user—work together to provide a system that supports efficient environmental protection activities for businesses.

[0361] terminal

[0362] The terminal provides an interface for users to input environmental goals. This interface includes input devices such as a touchscreen or keyboard. The terminal incorporates an emotion analyzer that captures the user's facial expressions with a camera and collects their voice tone with a microphone to analyze the user's emotional state in real time. For example, if the user shows a tired expression, the terminal recognizes this information using the emotion analyzer.

[0363] server

[0364] The server stores the company's environmental targets, submitted from the terminal, in a database. Database management typically uses database software such as PostgreSQL. The server also incorporates a generative AI model that generates optimal action plans based on the stored data. Large-scale models like OpenAI are used as the generative AI model. The generated plan is adjusted based on the user's emotional state. For example, if the user is experiencing stress, the server presents a feasible plan that breaks down tasks to alleviate stress.

[0365] Specific example

[0366] For example, if a company sets a goal of "reducing energy consumption by 20% within six months," the server will generate a feasible action plan based on this goal. Specifically, it might suggest a plan such as "aiming for a 10% reduction in the first two months, and adjusting the target for the second half as progress is made."

[0367] Example of a prompt

[0368] When using generative AI models, the following prompts are used.

[0369] "Please propose a concrete action plan to achieve the user's environmental goal of 'reducing energy consumption by 20% within six months.' The user's current emotional state is judged to be stressed."

[0370] By using a system configured in this way, users can engage in efficient and sustainable environmental protection activities while taking their emotions into consideration.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The terminal provides a screen for users to input environmental targets through an interface. Specific target data entered might include, for example, "Reduce waste by 15% within three months." The entered data is temporarily stored within the terminal and prepared for transmission to the server.

[0374] Step 2:

[0375] The device captures the user's facial expressions with a camera and collects their voice tone with a microphone. This data is input into an emotion analysis device to analyze the user's emotional state. The analysis results are output as an emotional state, such as "the user is relaxed" or "the user is stressed," and sent to a server.

[0376] Step 3:

[0377] The server receives environmental target data and emotional state data from the terminal and stores it in a database. For example, PostgreSQL is used for database storage. Using the stored data, the server applies a generative AI model to generate an optimal action plan. The inputs in this process are environmental targets and emotional states, and the output is a specific action plan.

[0378] Step 4:

[0379] The server further refines the generated action plan to match the user's emotional state. For example, if the user is stressed, the action plan is broken down into tasks to alleviate that stress. This refined plan is then sent to the terminal as the final output.

[0380] Step 5:

[0381] The terminal visually presents the action plan received from the server to the user. A dashboard is used for this presentation, showing progress and the next steps. Based on this information, the user can plan actions to achieve their goals. An interface is also provided for the user to provide feedback as needed.

[0382] Step 6:

[0383] When a user begins taking action based on an action plan, the device collects environmental data in real time via IoT devices and transmits it to a server. This data includes, for example, measurements of energy consumption and waste generation. The server aggregates and analyzes this data, generates progress visualization data, and sends the results back to the device.

[0384] In this way, the entire system works closely together, enabling users to carry out highly efficient environmental protection activities while also being considerate of their emotions.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0387] In environmental protection activities, it is necessary not only to provide efficient action plans, but also to take into account the emotional state of users and optimize their individual experiences. This requires a system that effectively achieves environmental goals while maintaining motivation for the activities.

[0388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0389] In this invention, the server includes means for providing a device for inputting a company's environmental targets, means for providing a design for analyzing emotional states and improving the user experience, and means for acquiring emotional analysis data in real time and providing the next steps toward achieving the environmental targets. This enables flexible adjustment of action plans optimized for the user and improvement of the individual user experience.

[0390] "Corporate environmental targets" are indicators used by companies to set specific environmental goals, such as sustainability and improved energy efficiency.

[0391] An "action plan" is a plan that outlines the specific steps and strategies necessary to achieve environmental goals.

[0392] "Emotional state" refers to the user's current psychological or emotional state, and is information analyzed through facial expressions, voice, and other means.

[0393] "Improving the user experience" refers to optimizing interaction design to increase user satisfaction and efficiency when using a system.

[0394] "Emotional analysis data" refers to data collected to evaluate a user's emotions, and is information obtained through an emotion engine.

[0395] "Real-time data acquisition" refers to the immediate processing of data and information without delay, demonstrating the system's ability to respond quickly on the spot.

[0396] This invention constructs a system to support companies' environmental protection activities. The system consists of three main components: terminals, servers, and users.

[0397] The terminal functions as a device for users to input the company's environmental goals. Here, the terminal uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. The acquired data is analyzed by an emotion engine to evaluate the user's emotional state. Software such as OpenCV and TensorFlow is used for the analysis. Based on this information, the system provides user-optimized feedback to support user motivation.

[0398] The server uses an AI model to generate an optimal action plan based on the input environmental goals and environmental data collected in real time. This process involves data processing using Node.js and AWS Lambda, and database management using MongoDB. The action plan takes sentiment analysis data into account and is adjusted according to the user's emotional state.

[0399] Users can view analysis results and progress on their device's dashboard. This allows them to modify their action plans as needed and manage their actions toward achieving their goals in real time.

[0400] For example, if a user at a certain company sets a goal of "reducing electricity consumption by 10% within one month," the emotion engine can understand the user's emotional state and suggest relaxation breaks or actionable energy-saving measures as needed. An example of a prompt message might be, "The user's current emotional state is positive / neutral / negative. Please suggest the next steps to reduce energy consumption."

[0401] In this way, users can engage in environmental protection activities in a more human-centered way by utilizing a combination of an emotion engine and an AI model.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The terminal receives the company's environmental targets from the user via an input device. The entered target information is temporarily stored in the terminal's memory.

[0405] Step 2:

[0406] The device uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion engine, where it is analyzed to evaluate the user's emotional state. The input consists of facial expression data and voice data, and the output is the result of the emotional state evaluation.

[0407] Step 3:

[0408] The server receives environmental goals and the user's emotional state from the terminal. Based on the received data, the server runs a generative AI model to create an optimal action plan. The input is the environmental goals and emotional state, and the output is the action plan.

[0409] Step 4:

[0410] The server collects environmental data from IoT devices to adjust the action plan in real time. This data is processed using Node.js, and the feedback is incorporated into the action plan. The input is environmental data, and the output is the improved action plan.

[0411] Step 5:

[0412] The terminal visualizes and displays the final action plan and analysis results on a dashboard. Based on this information, users can consider various options and modify their action plan if necessary. The inputs are the action plan and analysis results, while the output is the information presented to the user.

[0413] Step 6:

[0414] Users monitor progress and evaluate action plans through a dashboard. User feedback is sent to the server, which automatically generates reports based on that feedback. The input is user feedback, and the output is the generated report.

[0415] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0426] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention is an AI agent system for medium-sized manufacturing companies to efficiently implement environmental protection activities. The system consists of terminals, servers, and users who operate and input data into them.

[0432] The server first receives the company's environmental objectives entered by the user via the terminal interface and stores them in a database. This includes objective settings, deadlines for achievement, and specific numerical targets. Next, based on this information, the server leverages its large-scale language model to automatically generate an action plan suitable for the objectives. The generated plan is optimized based on historical data and existing regulatory information.

[0433] The terminal works in conjunction with sensors installed on-site to collect necessary environmental data in real time. This data is immediately sent to a server, which analyzes it instantly. The analysis results, such as changes in CO2 emissions and trends in temperature and humidity, are displayed on the terminal as a visual dashboard.

[0434] Users can monitor progress at any time through the provided dashboard and adjust action plans or provide additional instructions as needed. The server automatically evaluates progress at regular intervals and generates a report on CSR activities based on the results. This report is used to communicate the company's environmental efforts to management and stakeholders.

[0435] For example, if a company sets a goal of reducing CO2 emissions by 10% in one year, the server will generate an action plan that includes energy efficiency measures and improvements to production processes aligned with this goal. It also measures progress based on collected real-time data, analyzes predictive risks, and provides advance notifications.

[0436] In this way, the entire system works together to reduce environmental impact and contribute to sustainable business operations.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user uses the terminal interface to input the company's environmental goals and specific parameters (e.g., CO2 emission reduction targets and deadlines). The terminal then compiles this information and sends it to the server.

[0440] Step 2:

[0441] The server receives environmental targets submitted by users and stores them in a database. These targets are stored along with historical performance data and existing environmental regulation information, making them available for later processes.

[0442] Step 3:

[0443] The server automatically generates an optimal action plan using an AI model based on the stored environmental targets and information in the database. Here, it proposes specific and feasible measures, taking into account the company's past success stories and current environmental regulations.

[0444] Step 4:

[0445] The terminal uses sensors on-site to collect environmental data (e.g., CO2 concentration, temperature, humidity) in real time. This also includes integration with various IoT devices.

[0446] Step 5:

[0447] The server instantly analyzes environmental data sent from the terminal. Data analysis includes trend analysis, anomaly detection, and assessment of the current environmental load.

[0448] Step 6:

[0449] The server visualizes the analyzed data and displays it on the terminal in a dashboard format. On the terminal, users can check the degree of achievement of environmental targets and newly identified risk information.

[0450] Step 7:

[0451] The user checks the device's dashboard and reviews the action plan suggested by the AI ​​agent. They can approve or modify the plan as needed.

[0452] Step 8:

[0453] The server evaluates the progress of the action plan at regular intervals and automatically generates a report. This report is used to visualize the company's CSR activities and to report on its environmental efforts to stakeholders.

[0454] (Example 1)

[0455] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0456] Currently, implementing environmental protection activities in companies involves a complex process, from setting goals and developing action plans to real-time monitoring of implementation status, to evaluating progress and providing feedback based on results. This often results in manual data entry, analysis, and report creation, leading to challenges in efficiency and accuracy. Furthermore, the prediction of environmental impact and the development of risk avoidance measures lack immediacy, highlighting the need for the development of efficient environmental management systems.

[0457] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0458] In this invention, the server includes means for providing an information display device for inputting a company's environmental targets, means for automatically generating an optimal activity plan based on the company's environmental targets and a specific generative model, and means for collecting and analyzing environmental data obtained from information sensors. This enables companies to efficiently manage their environmental protection activities, appropriately evaluate progress, and quickly identify areas for improvement.

[0459] An "information display device" is a device that includes an input device and an interface for users to input a company's environmental targets.

[0460] A "generative model" is a type of large-scale data model used to automatically generate optimal action plans under specific conditions, based on historical data and regulatory information.

[0461] An "activity plan" is an action plan that specifically outlines the actions and improvement measures necessary to achieve a company's environmental goals.

[0462] An "information sensor" is a type of sensor device used to collect environmental data in real time, capable of measuring temperature, humidity, CO2 concentration, and other parameters.

[0463] An "output device" is a device used to visually display data analysis results, allowing users to easily check progress and trends.

[0464] A "data storage device" is a digital storage medium used to record and instantly update a company's environmental targets and past performance information.

[0465] A "prompt" is a command or input statement used when generating an activity plan using a generative model.

[0466] In this embodiment of the invention, the system is configured and operates as follows.

[0467] The server collects and stores information entered by users via terminals in order to effectively manage the company's environmental objectives. The objective information entered through the information display device includes the company's objective settings, numerical criteria, and deadlines for achievement. The server stores and manages this information in a data storage device.

[0468] Furthermore, the server utilizes a pre-configured generative AI model to automatically generate appropriate action plans based on the company's environmental goals. This generative model incorporates historical data and environmental regulatory information, enabling the development of highly effective plans. For example, it can utilize a prompt such as, "Generate a specific action plan to reduce CO2 emissions by 10% annually."

[0469] The terminal works in conjunction with information sensors installed on-site to collect environmental data in real time. The collected data is immediately transmitted from the terminal to the server. The server analyzes this data, visualizes the analysis results, and displays them on the terminal via an output device. This allows the user to monitor progress in real time.

[0470] Users can use the provided dashboard to check their progress toward achieving environmental goals and, if necessary, adjust their action plans or issue additional directives. In particular, modifying action plans using generative AI models enables truly efficient environmental management.

[0471] Through this system, companies can efficiently manage their environmental impact and support sustainable operations. The server also automatically generates regular reports, allowing companies to promptly inform management and stakeholders about their environmental management efforts. This reporting function plays a crucial role in enabling companies to implement continuous improvement measures and maintain transparency regarding environmental protection.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] Users input the company's environmental targets using the terminal's information display device. The entered data includes specific target values, deadlines, and objectives. The terminal transmits this information as data packets to the server. The server receives this input and stores it in a data storage device.

[0475] Step 2:

[0476] The server extracts stored environmental target data and inputs prompts into the generating AI model. For example, it might be given the command, "Generate a specific action plan to reduce CO2 emissions by 10% annually." Based on this prompt, the generating AI model automatically generates an action plan. The resulting plan is then processed to further refine it and make it shareable with users.

[0477] Step 3:

[0478] The terminal works in conjunction with information sensors installed on-site to collect environmental data such as CO2 concentration, temperature, and humidity in real time. These data packets are immediately transmitted from the terminal to the server. The server processes the received data using data analysis algorithms to analyze changes and trends in the input data.

[0479] Step 4:

[0480] The server converts the analyzed data into visual information and generates a dataset for the dashboard. This output is sent to the terminal and displayed to the user through the terminal's output device. Through this visual information, the user can check the progress and future predictions in real time.

[0481] Step 5:

[0482] Based on the information on the dashboard, users adjust their activity plans as needed. This input is sent to the server via the terminal, which uses a generative AI model to receive new instructions and regenerate or modify the activity plan. This ensures that the activity plan is adapted to the latest situation.

[0483] Step 6:

[0484] The server automatically evaluates progress at regular intervals and generates a report. This report includes achievement status and improvement suggestions, and is stored in a data storage device and provided to users and stakeholders. This ensures transparent information sharing regarding the company's environmental initiatives.

[0485] (Application Example 1)

[0486] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] In manufacturing, efficient environmental management and sustainable production practices are increasingly important challenges for modern businesses. Traditional systems have made it difficult to integrate environmental data collection and analysis, and action planning, and to reflect these in actual manufacturing processes. Furthermore, there has been a lack of means to flexibly respond to real-time, fluctuating environmental conditions and achieve effective energy efficiency.

[0488] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0489] In this invention, the server includes processing means for automatically generating an optimal action plan based on the company's goals, data processing means for collecting and analyzing data from measuring devices in real time, and control means for dynamically optimizing factory machine operations in cooperation with the measuring devices. This enables efficient and sustainable production operations utilizing environmental data.

[0490] "Information input means" refers to devices or interfaces that allow users to input a company's goals.

[0491] A "processing device" is a device that has the function of automatically generating an optimal action plan based on a company's objectives.

[0492] "Data processing means" refers to technologies for collecting and analyzing data acquired from measuring devices in real time.

[0493] "Display means" refers to monitors or dashboards used to visually present analysis results.

[0494] A "report generation means" is a means that has the function of evaluating progress and automatically creating a report based on that evaluation.

[0495] A "database device" is a system that stores historical performance data and updates it in real time as needed.

[0496] "Control means" refers to technologies for dynamically optimizing machine operations within a factory based on environmental data.

[0497] A "control algorithm" is a methodology for adjusting the operation of mechanical devices in order to achieve energy efficiency.

[0498] The system implementing this invention involves servers, terminals, and users working together to achieve environmental goals set by a company. At its core, the server performs advanced processing using various means.

[0499] The server provides an information input mechanism for users to input company goals. The goal information entered through this mechanism is stored in a database on the server. Based on this, the server automatically generates an optimal action plan through a processing mechanism using a generative AI model. The generated action plan is updated or optimized each time based on past performance data.

[0500] Furthermore, the terminals work in conjunction with various sensors installed within the factory to collect environmental data in real time. This collected data is immediately transmitted to a server and analyzed by data processing tools. The analysis results are displayed on the terminals using various data visualization tools, allowing users to check the progress.

[0501] Based on the analysis results, the server dynamically optimizes machine operations within the factory using control mechanisms. This function contributes to energy efficiency and increased productivity, supporting sustainable operations. For example, it optimizes energy use by automatically adjusting the air conditioning system based on real-time data from temperature sensors.

[0502] Furthermore, the system incorporates a reporting mechanism that automatically generates reports by evaluating the degree of achievement of action plans in accordance with progress, which is useful for transparent reporting to management and stakeholders.

[0503] As a concrete example, when aiming to reduce energy consumption in a factory, the server proposes the optimal operation of manufacturing equipment. An example of a prompt message supplied to the generated AI model is, "To reduce energy consumption in the factory, please propose an efficient operation plan for the air conditioning system using real-time data from temperature sensors."

[0504] In this way, this invention utilizes advanced data processing and control technologies to provide comprehensive support essential for achieving a company's environmental goals.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] Users input their company's environmental goals into the server using an information input device. This input includes specific numerical targets and deadlines for achievement. The server stores this information in a database and uses it as basic data for future processing.

[0508] Step 2:

[0509] The server automatically generates an optimal action plan using a generative AI model based on the input target information. In this process, the server analyzes past performance data and relevant external information to create the action plan. The output provides specific tasks and execution schedules.

[0510] Step 3:

[0511] The terminals work in conjunction with various sensors placed throughout the factory to collect environmental data in real time. This data includes CO2 emissions, temperature, humidity, and other parameters. This data is immediately transmitted to a server for analysis.

[0512] Step 4:

[0513] The server analyzes the collected environmental data using data processing tools. Based on the measurement data, it detects anomalies and performs trend analysis to understand environmental changes. The results are output as analysis results and used in the next step.

[0514] Step 5:

[0515] Based on the analysis results, the server utilizes control mechanisms to dynamically optimize machine operations in the factory. Specifically, it makes adjustments aimed at optimizing equipment energy consumption and reducing emissions. As a result, the optimized production process conditions are output.

[0516] Step 6:

[0517] Users can view analysis results and progress information on their terminals. A visual dashboard provided by the server allows users to easily understand the progress and revise plans or provide additional instructions as needed. Operational instructions and adjustment suggestions are generated as output.

[0518] Step 7:

[0519] The server automatically generates reports based on various data and progress. These reports summarize past activities, results, and suggestions for future improvements, providing transparent feedback to company management and stakeholders. The output is the final report, stored in a shareable format.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention is an AI agent system designed to enable medium-sized manufacturing companies to efficiently carry out environmental protection activities, optimizing user interaction by combining it with an emotion engine. The system consists of three main components: a terminal, a server, and a user.

[0522] First, the device provides the user with an interface for inputting environmental goals. Here, the emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state at that moment. This information is used to improve the user experience; for example, if the user is unmotivated, the device can provide motivational feedback.

[0523] The server stores the entered environmental goals in a database and uses an AI model to generate the optimal action plan. The proposed plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the system prioritizes presenting plans that are achievable with ample time.

[0524] The device collects environmental data in real time via IoT devices and sends it to a server. This data is analyzed and visualized on the server and presented to the user on the device's dashboard. The user can check their progress and adjust their plan as needed. Furthermore, the emotion engine plays a role in reducing psychological burden by displaying relaxing messages when the user's emotions are negative.

[0525] As a concrete example, consider a case where a company sets a goal of "reducing energy consumption by 20% within six months." If the emotion engine determines that the user is experiencing emotional stress, the server can flexibly modify the plan, such as suggesting a rest day to incorporate relaxation into the intermediate goal.

[0526] In this way, systems that combine an emotional engine enable companies to carry out environmental protection activities in a more humane and efficient manner.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The user inputs the company's environmental goals using the device's interface. Upon input, an emotion engine is activated, analyzing the user's emotional state in real time based on their facial expressions and voice. The device then provides feedback, estimating the user's level of motivation based on the analysis results.

[0530] Step 2:

[0531] The server receives data on environmental goals and emotional states transmitted from the terminal. The received data is stored in a database and used as material for analysis by an AI model. This prepares the server to generate an optimal action plan that takes the user's emotional state into account.

[0532] Step 3:

[0533] The server uses an AI model to automatically generate specific action plans aligned with environmental goals. If the user is experiencing stress, the system is configured to recommend realistic and less burdensome plans.

[0534] Step 4:

[0535] The terminal collects environmental data in real time by connecting with on-site sensors. The collected data is immediately sent to the server, allowing for continuous monitoring of progress.

[0536] Step 5:

[0537] The server analyzes and visualizes environmental data, displaying the results on the terminal's dashboard. This allows users to see at a glance their progress towards their initial environmental goals.

[0538] Step 6:

[0539] Users can check their progress on the device's dashboard and adjust their action plan as needed, based on feedback from the emotion engine. If emotions are perceived negatively, the device will display messages designed to promote relaxation.

[0540] Step 7:

[0541] The server automatically generates periodic CSR activity reports based on progress information and user feedback. These reports take into account changes in sentiment and include comparative analysis with past data.

[0542] Step 8:

[0543] Users can review the report and, if further optimization through the emotion engine is needed, use the data to make an overall review of their activities. This facilitates steady progress toward achieving long-term environmental goals.

[0544] (Example 2)

[0545] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] Corporate environmental protection activities require the generation of efficient action plans that take into account users' emotional states, as well as progress management through real-time data analysis and visualization. However, conventional systems often present uniform plans that do not consider user emotions, making it difficult to maintain motivation. Furthermore, the collection, analysis, and visualization of environmental data are not sufficiently automated, and user-friendly interfaces are not provided.

[0547] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0548] In this invention, the server includes a terminal device for inputting company information, an emotion analysis device equipped with means for recognizing the user's emotional state, a computing device using a generative model that automatically generates an optimal work plan based on the company information, means for aggregating and analyzing information from a data collection device in real time, a display device for visualizing and presenting the analysis results, and means for generating motivational messages according to the user's emotional state. This enables the presentation of flexible action plans that take the user's emotional state into consideration, as well as progress management and motivation based on real-time data.

[0549] A "corporation" is a legally established organization that engages in activities aimed at achieving specific objectives, such as commercial activities or environmental activities.

[0550] A "terminal device" is an electronic device used by users to input and receive information, and generally includes a display and input interface.

[0551] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0552] A "generative model" is a mathematical model that uses algorithms based on data to automatically generate optimal plans and predictions.

[0553] A "computational device" refers to hardware or software used to perform data processing and calculations, enabling the execution of generative models.

[0554] A "data acquisition device" is a device that acquires information from the external environment or internal processes, and sensors are commonly used for this purpose.

[0555] "Analysis results" refer to the conclusions and information derived from analyzing the collected data.

[0556] A "display device" is a screen-based device that visually presents data and analysis results, and is used by users to make decisions and take actions based on that information.

[0557] A "motivational message" is an inspiring message provided to motivate users and promote goal achievement.

[0558] In this invention, three main elements—a terminal, a server, and a user—work together to provide a system that supports efficient environmental protection activities for businesses.

[0559] terminal

[0560] The terminal provides an interface for users to input environmental goals. This interface includes input devices such as a touchscreen or keyboard. The terminal incorporates an emotion analyzer that captures the user's facial expressions with a camera and collects their voice tone with a microphone to analyze the user's emotional state in real time. For example, if the user shows a tired expression, the terminal recognizes this information using the emotion analyzer.

[0561] server

[0562] The server stores the company's environmental targets, submitted from the terminal, in a database. Database management typically uses database software such as PostgreSQL. The server also incorporates a generative AI model that generates optimal action plans based on the stored data. Large-scale models like OpenAI are used as the generative AI model. The generated plan is adjusted based on the user's emotional state. For example, if the user is experiencing stress, the server presents a feasible plan that breaks down tasks to alleviate stress.

[0563] Specific example

[0564] For example, if a company sets a goal of "reducing energy consumption by 20% within six months," the server will generate a feasible action plan based on this goal. Specifically, it might suggest a plan such as "aiming for a 10% reduction in the first two months, and adjusting the target for the second half as progress is made."

[0565] Example of a prompt

[0566] When using generative AI models, the following prompts are used.

[0567] "Please propose a concrete action plan to achieve the user's environmental goal of 'reducing energy consumption by 20% within six months.' The user's current emotional state is judged to be stressed."

[0568] By using a system configured in this way, users can engage in efficient and sustainable environmental protection activities while taking their emotions into consideration.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The terminal provides a screen for users to input environmental targets through an interface. Specific target data entered might include, for example, "Reduce waste by 15% within three months." The entered data is temporarily stored within the terminal and prepared for transmission to the server.

[0572] Step 2:

[0573] The device captures the user's facial expressions with a camera and collects their voice tone with a microphone. This data is input into an emotion analysis device to analyze the user's emotional state. The analysis results are output as an emotional state, such as "the user is relaxed" or "the user is stressed," and sent to a server.

[0574] Step 3:

[0575] The server receives environmental target data and emotional state data from the terminal and stores it in a database. For example, PostgreSQL is used for database storage. Using the stored data, the server applies a generative AI model to generate an optimal action plan. The inputs in this process are environmental targets and emotional states, and the output is a specific action plan.

[0576] Step 4:

[0577] The server further refines the generated action plan to match the user's emotional state. For example, if the user is stressed, the action plan is broken down into tasks to alleviate that stress. This refined plan is then sent to the terminal as the final output.

[0578] Step 5:

[0579] The terminal visually presents the action plan received from the server to the user. A dashboard is used for this presentation, showing progress and the next steps. Based on this information, the user can plan actions to achieve their goals. An interface is also provided for the user to provide feedback as needed.

[0580] Step 6:

[0581] When a user begins taking action based on an action plan, the device collects environmental data in real time via IoT devices and transmits it to a server. This data includes, for example, measurements of energy consumption and waste generation. The server aggregates and analyzes this data, generates progress visualization data, and sends the results back to the device.

[0582] In this way, the entire system works closely together, enabling users to carry out highly efficient environmental protection activities while also being considerate of their emotions.

[0583] (Application Example 2)

[0584] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0585] In environmental protection activities, it is necessary not only to provide efficient action plans, but also to take into account the emotional state of users and optimize their individual experiences. This requires a system that effectively achieves environmental goals while maintaining motivation for the activities.

[0586] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0587] In this invention, the server includes means for providing a device for inputting a company's environmental targets, means for providing a design for analyzing emotional states and improving the user experience, and means for acquiring emotional analysis data in real time and providing the next steps toward achieving the environmental targets. This enables flexible adjustment of action plans optimized for the user and improvement of the individual user experience.

[0588] "Corporate environmental targets" are indicators used by companies to set specific environmental goals, such as sustainability and improved energy efficiency.

[0589] An "action plan" is a plan that outlines the specific steps and strategies necessary to achieve environmental goals.

[0590] "Emotional state" refers to the user's current psychological or emotional state, and is information analyzed through facial expressions, voice, and other means.

[0591] "Improving the user experience" refers to optimizing interaction design to increase user satisfaction and efficiency when using a system.

[0592] "Emotional analysis data" refers to data collected to evaluate a user's emotions, and is information obtained through an emotion engine.

[0593] "Real-time data acquisition" refers to the immediate processing of data and information without delay, demonstrating the system's ability to respond quickly on the spot.

[0594] This invention constructs a system to support companies' environmental protection activities. The system consists of three main components: terminals, servers, and users.

[0595] The terminal functions as a device for users to input the company's environmental goals. Here, the terminal uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. The acquired data is analyzed by an emotion engine to evaluate the user's emotional state. Software such as OpenCV and TensorFlow is used for the analysis. Based on this information, the system provides user-optimized feedback to support user motivation.

[0596] The server uses an AI model to generate an optimal action plan based on the input environmental goals and environmental data collected in real time. This process involves data processing using Node.js and AWS Lambda, and database management using MongoDB. The action plan takes sentiment analysis data into account and is adjusted according to the user's emotional state.

[0597] Users can view analysis results and progress on their device's dashboard. This allows them to modify their action plans as needed and manage their actions toward achieving their goals in real time.

[0598] For example, if a user at a certain company sets a goal of "reducing electricity consumption by 10% within one month," the emotion engine can understand the user's emotional state and suggest relaxation breaks or actionable energy-saving measures as needed. An example of a prompt message might be, "The user's current emotional state is positive / neutral / negative. Please suggest the next steps to reduce energy consumption."

[0599] In this way, users can engage in environmental protection activities in a more human-centered way by utilizing a combination of an emotion engine and an AI model.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The terminal receives the company's environmental targets from the user via an input device. The entered target information is temporarily stored in the terminal's memory.

[0603] Step 2:

[0604] The device uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion engine, where it is analyzed to evaluate the user's emotional state. The input consists of facial expression data and voice data, and the output is the result of the emotional state evaluation.

[0605] Step 3:

[0606] The server receives environmental goals and the user's emotional state from the terminal. Based on the received data, the server runs a generative AI model to create an optimal action plan. The input is the environmental goals and emotional state, and the output is the action plan.

[0607] Step 4:

[0608] The server collects environmental data from IoT devices to adjust the action plan in real time. This data is processed using Node.js, and the feedback is incorporated into the action plan. The input is environmental data, and the output is the improved action plan.

[0609] Step 5:

[0610] The terminal visualizes and displays the final action plan and analysis results on a dashboard. Based on this information, users can consider various options and modify their action plan if necessary. The inputs are the action plan and analysis results, while the output is the information presented to the user.

[0611] Step 6:

[0612] Users monitor progress and evaluate action plans through a dashboard. User feedback is sent to the server, which automatically generates reports based on that feedback. The input is user feedback, and the output is the generated report.

[0613] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0619] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0620] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0621] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0623] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0624] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0625] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0626] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0627] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0628] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0629] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] This invention is an AI agent system for medium-sized manufacturing companies to efficiently implement environmental protection activities. The system consists of terminals, servers, and users who operate and input data into them.

[0631] The server first receives the company's environmental objectives entered by the user via the terminal interface and stores them in a database. This includes objective settings, deadlines for achievement, and specific numerical targets. Next, based on this information, the server leverages its large-scale language model to automatically generate an action plan suitable for the objectives. The generated plan is optimized based on historical data and existing regulatory information.

[0632] The terminal works in conjunction with sensors installed on-site to collect necessary environmental data in real time. This data is immediately sent to a server, which analyzes it instantly. The analysis results, such as changes in CO2 emissions and trends in temperature and humidity, are displayed on the terminal as a visual dashboard.

[0633] Users can monitor progress at any time through the provided dashboard and adjust action plans or provide additional instructions as needed. The server automatically evaluates progress at regular intervals and generates a report on CSR activities based on the results. This report is used to communicate the company's environmental efforts to management and stakeholders.

[0634] For example, if a company sets a goal of reducing CO2 emissions by 10% in one year, the server will generate an action plan that includes energy efficiency measures and improvements to production processes aligned with this goal. It also measures progress based on collected real-time data, analyzes predictive risks, and provides advance notifications.

[0635] In this way, the entire system works together to reduce environmental impact and contribute to sustainable business operations.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The user uses the terminal interface to input the company's environmental goals and specific parameters (e.g., CO2 emission reduction targets and deadlines). The terminal then compiles this information and sends it to the server.

[0639] Step 2:

[0640] The server receives environmental targets submitted by users and stores them in a database. These targets are stored along with historical performance data and existing environmental regulation information, making them available for later processes.

[0641] Step 3:

[0642] The server automatically generates an optimal action plan using an AI model based on the stored environmental targets and information in the database. Here, it proposes specific and feasible measures, taking into account the company's past success stories and current environmental regulations.

[0643] Step 4:

[0644] The terminal uses sensors on-site to collect environmental data (e.g., CO2 concentration, temperature, humidity) in real time. This also includes integration with various IoT devices.

[0645] Step 5:

[0646] The server instantly analyzes environmental data sent from the terminal. Data analysis includes trend analysis, anomaly detection, and assessment of the current environmental load.

[0647] Step 6:

[0648] The server visualizes the analyzed data and displays it on the terminal in a dashboard format. On the terminal, users can check the degree of achievement of environmental targets and newly identified risk information.

[0649] Step 7:

[0650] The user checks the device's dashboard and reviews the action plan suggested by the AI ​​agent. They can approve or modify the plan as needed.

[0651] Step 8:

[0652] The server evaluates the progress of the action plan at regular intervals and automatically generates a report. This report is used to visualize the company's CSR activities and to report on its environmental efforts to stakeholders.

[0653] (Example 1)

[0654] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] Currently, implementing environmental protection activities in companies involves a complex process, from setting goals and developing action plans to real-time monitoring of implementation status, to evaluating progress and providing feedback based on results. This often results in manual data entry, analysis, and report creation, leading to challenges in efficiency and accuracy. Furthermore, the prediction of environmental impact and the development of risk avoidance measures lack immediacy, highlighting the need for the development of efficient environmental management systems.

[0656] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0657] In this invention, the server includes means for providing an information display device for inputting a company's environmental targets, means for automatically generating an optimal activity plan based on the company's environmental targets and a specific generative model, and means for collecting and analyzing environmental data obtained from information sensors. This enables companies to efficiently manage their environmental protection activities, appropriately evaluate progress, and quickly identify areas for improvement.

[0658] An "information display device" is a device that includes an input device and an interface for users to input a company's environmental targets.

[0659] A "generative model" is a type of large-scale data model used to automatically generate optimal action plans under specific conditions, based on historical data and regulatory information.

[0660] An "activity plan" is an action plan that specifically outlines the actions and improvement measures necessary to achieve a company's environmental goals.

[0661] An "information sensor" is a type of sensor device used to collect environmental data in real time, capable of measuring temperature, humidity, CO2 concentration, and other parameters.

[0662] An "output device" is a device used to visually display data analysis results, allowing users to easily check progress and trends.

[0663] A "data storage device" is a digital storage medium used to record and instantly update a company's environmental targets and past performance information.

[0664] A "prompt" is a command or input statement used when generating an activity plan using a generative model.

[0665] In this embodiment of the invention, the system is configured and operates as follows.

[0666] The server collects and stores information entered by users via terminals in order to effectively manage the company's environmental objectives. The objective information entered through the information display device includes the company's objective settings, numerical criteria, and deadlines for achievement. The server stores and manages this information in a data storage device.

[0667] Furthermore, the server utilizes a pre-configured generative AI model to automatically generate appropriate action plans based on the company's environmental goals. This generative model incorporates historical data and environmental regulatory information, enabling the development of highly effective plans. For example, it can utilize a prompt such as, "Generate a specific action plan to reduce CO2 emissions by 10% annually."

[0668] The terminal works in conjunction with information sensors installed on-site to collect environmental data in real time. The collected data is immediately transmitted from the terminal to the server. The server analyzes this data, visualizes the analysis results, and displays them on the terminal via an output device. This allows the user to monitor progress in real time.

[0669] Users can use the provided dashboard to check their progress toward achieving environmental goals and, if necessary, adjust their action plans or issue additional directives. In particular, modifying action plans using generative AI models enables truly efficient environmental management.

[0670] Through this system, companies can efficiently manage their environmental impact and support sustainable operations. The server also automatically generates regular reports, allowing companies to promptly inform management and stakeholders about their environmental management efforts. This reporting function plays a crucial role in enabling companies to implement continuous improvement measures and maintain transparency regarding environmental protection.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] Users input the company's environmental targets using the terminal's information display device. The entered data includes specific target values, deadlines, and objectives. The terminal transmits this information as data packets to the server. The server receives this input and stores it in a data storage device.

[0674] Step 2:

[0675] The server extracts stored environmental target data and inputs prompts into the generating AI model. For example, it might be given the command, "Generate a specific action plan to reduce CO2 emissions by 10% annually." Based on this prompt, the generating AI model automatically generates an action plan. The resulting plan is then processed to further refine it and make it shareable with users.

[0676] Step 3:

[0677] The terminal works in conjunction with information sensors installed on-site to collect environmental data such as CO2 concentration, temperature, and humidity in real time. These data packets are immediately transmitted from the terminal to the server. The server processes the received data using data analysis algorithms to analyze changes and trends in the input data.

[0678] Step 4:

[0679] The server converts the analyzed data into visual information and generates a dataset for the dashboard. This output is sent to the terminal and displayed to the user through the terminal's output device. Through this visual information, the user can check the progress and future predictions in real time.

[0680] Step 5:

[0681] Based on the information on the dashboard, users adjust their activity plans as needed. This input is sent to the server via the terminal, which uses a generative AI model to receive new instructions and regenerate or modify the activity plan. This ensures that the activity plan is adapted to the latest situation.

[0682] Step 6:

[0683] The server automatically evaluates progress at regular intervals and generates a report. This report includes achievement status and improvement suggestions, and is stored in a data storage device and provided to users and stakeholders. This ensures transparent information sharing regarding the company's environmental initiatives.

[0684] (Application Example 1)

[0685] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] In manufacturing, efficient environmental management and sustainable production practices are increasingly important challenges for modern businesses. Traditional systems have made it difficult to integrate environmental data collection and analysis, and action planning, and to reflect these in actual manufacturing processes. Furthermore, there has been a lack of means to flexibly respond to real-time, fluctuating environmental conditions and achieve effective energy efficiency.

[0687] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0688] In this invention, the server includes processing means for automatically generating an optimal action plan based on the company's goals, data processing means for collecting and analyzing data from measuring devices in real time, and control means for dynamically optimizing factory machine operations in cooperation with the measuring devices. This enables efficient and sustainable production operations utilizing environmental data.

[0689] "Information input means" refers to devices or interfaces that allow users to input a company's goals.

[0690] A "processing device" is a device that has the function of automatically generating an optimal action plan based on a company's objectives.

[0691] "Data processing means" refers to technologies for collecting and analyzing data acquired from measuring devices in real time.

[0692] "Display means" refers to monitors or dashboards used to visually present analysis results.

[0693] A "report generation means" is a means that has the function of evaluating progress and automatically creating a report based on that evaluation.

[0694] A "database device" is a system that stores historical performance data and updates it in real time as needed.

[0695] "Control means" refers to technologies for dynamically optimizing machine operations within a factory based on environmental data.

[0696] A "control algorithm" is a methodology for adjusting the operation of mechanical devices in order to achieve energy efficiency.

[0697] The system implementing this invention involves servers, terminals, and users working together to achieve environmental goals set by a company. At its core, the server performs advanced processing using various means.

[0698] The server provides an information input mechanism for users to input company goals. The goal information entered through this mechanism is stored in a database on the server. Based on this, the server automatically generates an optimal action plan through a processing mechanism using a generative AI model. The generated action plan is updated or optimized each time based on past performance data.

[0699] Furthermore, the terminals work in conjunction with various sensors installed within the factory to collect environmental data in real time. This collected data is immediately transmitted to a server and analyzed by data processing tools. The analysis results are displayed on the terminals using various data visualization tools, allowing users to check the progress.

[0700] Based on the analysis results, the server dynamically optimizes machine operations within the factory using control mechanisms. This function contributes to energy efficiency and increased productivity, supporting sustainable operations. For example, it optimizes energy use by automatically adjusting the air conditioning system based on real-time data from temperature sensors.

[0701] Furthermore, the system incorporates a reporting mechanism that automatically generates reports by evaluating the degree of achievement of action plans in accordance with progress, which is useful for transparent reporting to management and stakeholders.

[0702] As a concrete example, when aiming to reduce energy consumption in a factory, the server proposes the optimal operation of manufacturing equipment. An example of a prompt message supplied to the generated AI model is, "To reduce energy consumption in the factory, please propose an efficient operation plan for the air conditioning system using real-time data from temperature sensors."

[0703] In this way, this invention utilizes advanced data processing and control technologies to provide comprehensive support essential for achieving a company's environmental goals.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] Users input their company's environmental goals into the server using an information input device. This input includes specific numerical targets and deadlines for achievement. The server stores this information in a database and uses it as basic data for future processing.

[0707] Step 2:

[0708] The server automatically generates an optimal action plan using a generative AI model based on the input target information. In this process, the server analyzes past performance data and relevant external information to create the action plan. The output provides specific tasks and execution schedules.

[0709] Step 3:

[0710] The terminals work in conjunction with various sensors placed throughout the factory to collect environmental data in real time. This data includes CO2 emissions, temperature, humidity, and other parameters. This data is immediately transmitted to a server for analysis.

[0711] Step 4:

[0712] The server analyzes the collected environmental data using data processing tools. Based on the measurement data, it detects anomalies and performs trend analysis to understand environmental changes. The results are output as analysis results and used in the next step.

[0713] Step 5:

[0714] Based on the analysis results, the server utilizes control mechanisms to dynamically optimize machine operations in the factory. Specifically, it makes adjustments aimed at optimizing equipment energy consumption and reducing emissions. As a result, the optimized production process conditions are output.

[0715] Step 6:

[0716] Users can view analysis results and progress information on their terminals. A visual dashboard provided by the server allows users to easily understand the progress and revise plans or provide additional instructions as needed. Operational instructions and adjustment suggestions are generated as output.

[0717] Step 7:

[0718] The server automatically generates reports based on various data and progress. These reports summarize past activities, results, and suggestions for future improvements, providing transparent feedback to company management and stakeholders. The output is the final report, stored in a shareable format.

[0719] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0720] This invention is an AI agent system designed to enable medium-sized manufacturing companies to efficiently carry out environmental protection activities, optimizing user interaction by combining it with an emotion engine. The system consists of three main components: a terminal, a server, and a user.

[0721] First, the device provides the user with an interface for inputting environmental goals. Here, the emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state at that moment. This information is used to improve the user experience; for example, if the user is unmotivated, the device can provide motivational feedback.

[0722] The server stores the entered environmental goals in a database and uses an AI model to generate the optimal action plan. The proposed plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the system prioritizes presenting plans that are achievable with ample time.

[0723] The device collects environmental data in real time via IoT devices and sends it to a server. This data is analyzed and visualized on the server and presented to the user on the device's dashboard. The user can check their progress and adjust their plan as needed. Furthermore, the emotion engine plays a role in reducing psychological burden by displaying relaxing messages when the user's emotions are negative.

[0724] As a concrete example, consider a case where a company sets a goal of "reducing energy consumption by 20% within six months." If the emotion engine determines that the user is experiencing emotional stress, the server can flexibly modify the plan, such as suggesting a rest day to incorporate relaxation into the intermediate goal.

[0725] In this way, systems that combine an emotional engine enable companies to carry out environmental protection activities in a more humane and efficient manner.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The user inputs the company's environmental goals using the device's interface. Upon input, an emotion engine is activated, analyzing the user's emotional state in real time based on their facial expressions and voice. The device then provides feedback, estimating the user's level of motivation based on the analysis results.

[0729] Step 2:

[0730] The server receives data on environmental goals and emotional states transmitted from the terminal. The received data is stored in a database and used as material for analysis by an AI model. This prepares the server to generate an optimal action plan that takes the user's emotional state into account.

[0731] Step 3:

[0732] The server uses an AI model to automatically generate specific action plans aligned with environmental goals. If the user is experiencing stress, the system is configured to recommend realistic and less burdensome plans.

[0733] Step 4:

[0734] The terminal collects environmental data in real time by connecting with on-site sensors. The collected data is immediately sent to the server, allowing for continuous monitoring of progress.

[0735] Step 5:

[0736] The server analyzes and visualizes environmental data, displaying the results on the terminal's dashboard. This allows users to see at a glance their progress towards their initial environmental goals.

[0737] Step 6:

[0738] Users can check their progress on the device's dashboard and adjust their action plan as needed, based on feedback from the emotion engine. If emotions are perceived negatively, the device will display messages designed to promote relaxation.

[0739] Step 7:

[0740] The server automatically generates periodic CSR activity reports based on progress information and user feedback. These reports take into account changes in sentiment and include comparative analysis with past data.

[0741] Step 8:

[0742] Users can review the report and, if further optimization through the emotion engine is needed, use the data to make an overall review of their activities. This facilitates steady progress toward achieving long-term environmental goals.

[0743] (Example 2)

[0744] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] Corporate environmental protection activities require the generation of efficient action plans that take into account users' emotional states, as well as progress management through real-time data analysis and visualization. However, conventional systems often present uniform plans that do not consider user emotions, making it difficult to maintain motivation. Furthermore, the collection, analysis, and visualization of environmental data are not sufficiently automated, and user-friendly interfaces are not provided.

[0746] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0747] In this invention, the server includes a terminal device for inputting company information, an emotion analysis device equipped with means for recognizing the user's emotional state, a computing device using a generative model that automatically generates an optimal work plan based on the company information, means for aggregating and analyzing information from a data collection device in real time, a display device for visualizing and presenting the analysis results, and means for generating motivational messages according to the user's emotional state. This enables the presentation of flexible action plans that take the user's emotional state into consideration, as well as progress management and motivation based on real-time data.

[0748] A "corporation" is a legally established organization that engages in activities aimed at achieving specific objectives, such as commercial activities or environmental activities.

[0749] A "terminal device" is an electronic device used by users to input and receive information, and generally includes a display and input interface.

[0750] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0751] A "generative model" is a mathematical model that uses algorithms based on data to automatically generate optimal plans and predictions.

[0752] A "computational device" refers to hardware or software used to perform data processing and calculations, enabling the execution of generative models.

[0753] A "data acquisition device" is a device that acquires information from the external environment or internal processes, and sensors are commonly used for this purpose.

[0754] "Analysis results" refer to the conclusions and information derived from analyzing the collected data.

[0755] A "display device" is a screen-based device that visually presents data and analysis results, and is used by users to make decisions and take actions based on that information.

[0756] A "motivational message" is an inspiring message provided to motivate users and promote goal achievement.

[0757] In this invention, three main elements—a terminal, a server, and a user—work together to provide a system that supports efficient environmental protection activities for businesses.

[0758] terminal

[0759] The terminal provides an interface for users to input environmental goals. This interface includes input devices such as a touchscreen or keyboard. The terminal incorporates an emotion analyzer that captures the user's facial expressions with a camera and collects their voice tone with a microphone to analyze the user's emotional state in real time. For example, if the user shows a tired expression, the terminal recognizes this information using the emotion analyzer.

[0760] server

[0761] The server stores the company's environmental targets, submitted from the terminal, in a database. Database management typically uses database software such as PostgreSQL. The server also incorporates a generative AI model that generates optimal action plans based on the stored data. Large-scale models like OpenAI are used as the generative AI model. The generated plan is adjusted based on the user's emotional state. For example, if the user is experiencing stress, the server presents a feasible plan that breaks down tasks to alleviate stress.

[0762] Specific example

[0763] For example, if a company sets a goal of "reducing energy consumption by 20% within six months," the server will generate a feasible action plan based on this goal. Specifically, it might suggest a plan such as "aiming for a 10% reduction in the first two months, and adjusting the target for the second half as progress is made."

[0764] Example of a prompt

[0765] When using generative AI models, the following prompts are used.

[0766] "Please propose a concrete action plan to achieve the user's environmental goal of 'reducing energy consumption by 20% within six months.' The user's current emotional state is judged to be stressed."

[0767] By using a system configured in this way, users can engage in efficient and sustainable environmental protection activities while taking their emotions into consideration.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The terminal provides a screen for users to input environmental targets through an interface. Specific target data entered might include, for example, "Reduce waste by 15% within three months." The entered data is temporarily stored within the terminal and prepared for transmission to the server.

[0771] Step 2:

[0772] The device captures the user's facial expressions with a camera and collects their voice tone with a microphone. This data is input into an emotion analysis device to analyze the user's emotional state. The analysis results are output as an emotional state, such as "the user is relaxed" or "the user is stressed," and sent to a server.

[0773] Step 3:

[0774] The server receives environmental target data and emotional state data from the terminal and stores it in a database. For example, PostgreSQL is used for database storage. Using the stored data, the server applies a generative AI model to generate an optimal action plan. The inputs in this process are environmental targets and emotional states, and the output is a specific action plan.

[0775] Step 4:

[0776] The server further refines the generated action plan to match the user's emotional state. For example, if the user is stressed, the action plan is broken down into tasks to alleviate that stress. This refined plan is then sent to the terminal as the final output.

[0777] Step 5:

[0778] The terminal visually presents the action plan received from the server to the user. A dashboard is used for this presentation, showing progress and the next steps. Based on this information, the user can plan actions to achieve their goals. An interface is also provided for the user to provide feedback as needed.

[0779] Step 6:

[0780] When a user begins taking action based on an action plan, the device collects environmental data in real time via IoT devices and transmits it to a server. This data includes, for example, measurements of energy consumption and waste generation. The server aggregates and analyzes this data, generates progress visualization data, and sends the results back to the device.

[0781] In this way, the entire system works closely together, enabling users to carry out highly efficient environmental protection activities while also being considerate of their emotions.

[0782] (Application Example 2)

[0783] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0784] In environmental protection activities, it is necessary not only to provide efficient action plans, but also to take into account the emotional state of users and optimize their individual experiences. This requires a system that effectively achieves environmental goals while maintaining motivation for the activities.

[0785] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0786] In this invention, the server includes means for providing a device for inputting a company's environmental targets, means for providing a design for analyzing emotional states and improving the user experience, and means for acquiring emotional analysis data in real time and providing the next steps toward achieving the environmental targets. This enables flexible adjustment of action plans optimized for the user and improvement of the individual user experience.

[0787] "Corporate environmental targets" are indicators used by companies to set specific environmental goals, such as sustainability and improved energy efficiency.

[0788] An "action plan" is a plan that outlines the specific steps and strategies necessary to achieve environmental goals.

[0789] "Emotional state" refers to the user's current psychological or emotional state, and is information analyzed through facial expressions, voice, and other means.

[0790] "Improving the user experience" refers to optimizing interaction design to increase user satisfaction and efficiency when using a system.

[0791] "Emotional analysis data" refers to data collected to evaluate a user's emotions, and is information obtained through an emotion engine.

[0792] "Real-time data acquisition" refers to the immediate processing of data and information without delay, demonstrating the system's ability to respond quickly on the spot.

[0793] This invention constructs a system to support companies' environmental protection activities. The system consists of three main components: terminals, servers, and users.

[0794] The terminal functions as a device for users to input the company's environmental goals. Here, the terminal uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. The acquired data is analyzed by an emotion engine to evaluate the user's emotional state. Software such as OpenCV and TensorFlow is used for the analysis. Based on this information, the system provides user-optimized feedback to support user motivation.

[0795] The server uses an AI model to generate an optimal action plan based on the input environmental goals and environmental data collected in real time. This process involves data processing using Node.js and AWS Lambda, and database management using MongoDB. The action plan takes sentiment analysis data into account and is adjusted according to the user's emotional state.

[0796] Users can view analysis results and progress on their device's dashboard. This allows them to modify their action plans as needed and manage their actions toward achieving their goals in real time.

[0797] For example, if a user at a certain company sets a goal of "reducing electricity consumption by 10% within one month," the emotion engine can understand the user's emotional state and suggest relaxation breaks or actionable energy-saving measures as needed. An example of a prompt message might be, "The user's current emotional state is positive / neutral / negative. Please suggest the next steps to reduce energy consumption."

[0798] In this way, users can engage in environmental protection activities in a more human-centered way by utilizing a combination of an emotion engine and an AI model.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The terminal receives the company's environmental targets from the user via an input device. The entered target information is temporarily stored in the terminal's memory.

[0802] Step 2:

[0803] The device uses a high-sensitivity camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion engine, where it is analyzed to evaluate the user's emotional state. The input consists of facial expression data and voice data, and the output is the result of the emotional state evaluation.

[0804] Step 3:

[0805] The server receives environmental goals and the user's emotional state from the terminal. Based on the received data, the server runs a generative AI model to create an optimal action plan. The input is the environmental goals and emotional state, and the output is the action plan.

[0806] Step 4:

[0807] The server collects environmental data from IoT devices to adjust the action plan in real time. This data is processed using Node.js, and the feedback is incorporated into the action plan. The input is environmental data, and the output is the improved action plan.

[0808] Step 5:

[0809] The terminal visualizes and displays the final action plan and analysis results on a dashboard. Based on this information, users can consider various options and modify their action plan if necessary. The inputs are the action plan and analysis results, while the output is the information presented to the user.

[0810] Step 6:

[0811] Users monitor progress and evaluate action plans through a dashboard. User feedback is sent to the server, which automatically generates reports based on that feedback. The input is user feedback, and the output is the generated report.

[0812] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0813] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0814] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0815] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0816] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0817] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0818] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0819] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0820] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0821] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0822] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0823] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0824] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0825] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0826] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0827] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0828] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0829] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0830] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0831] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0832] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means of providing an interface for inputting a company's environmental targets,

[0836] A means to automatically generate the optimal action plan based on a company's environmental objectives,

[0837] A means of collecting and analyzing environmental data from sensors in real time,

[0838] Means for visualizing and presenting analysis results,

[0839] A system that includes means for evaluating progress and automatically generating reports.

[0840] (Claim 2)

[0841] The system according to claim 1, comprising means for predicting a company's environmental impact and proposing proactive measures against risks.

[0842] (Claim 3)

[0843] The system according to claim 1, comprising means for storing a company's environmental targets and past performance data in a database and updating it in real time.

[0844] "Example 1"

[0845] (Claim 1)

[0846] A means for providing an information display device for inputting a company's environmental targets,

[0847] A means for automatically generating an optimal action plan based on a company's environmental objectives and a specific generative model,

[0848] A means of collecting and analyzing environmental data obtained from information sensors,

[0849] Means for providing an output device that visually displays the analysis results,

[0850] A method for evaluating progress and automatically generating regular reports,

[0851] A means of monitoring the rate of achievement of targets through periodic evaluation results,

[0852] A means of appropriately adjusting the activity plan using prompts from a generative model,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, comprising means for predicting the environmental impact of a company and proposing measures to prevent potential hazards.

[0856] (Claim 3)

[0857] The system according to claim 1, comprising means for storing a company's environmental targets and past performance information in a data storage device and updating it immediately.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] A means of inputting information for entering company goals,

[0861] A processing method that automatically generates an optimal action plan based on the company's goals,

[0862] A data processing means for collecting and analyzing data from measuring devices in real time,

[0863] A display method for visualizing and displaying the analysis results,

[0864] A report generation method that evaluates progress and automatically generates reports,

[0865] A database device that stores past performance data and updates it in real time,

[0866] A control system that works in conjunction with measuring devices to dynamically optimize the operation of factory machinery,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, comprising processing means for predicting load and proposing proactive measures against risk.

[0870] (Claim 3)

[0871] The system according to claim 1, comprising a control algorithm that controls the operation of a machine based on an automatically generated action plan and achieves energy efficiency.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] A terminal device for inputting company information,

[0875] An emotion analysis device equipped with means for recognizing the user's emotional state,

[0876] A computing device that uses a generative model to automatically generate an optimal work plan based on company information,

[0877] A means of aggregating and analyzing information from data collection devices in real time,

[0878] A display device that visualizes and presents the analysis results,

[0879] A system that includes means for generating motivational messages according to the user's emotional state.

[0880] (Claim 2)

[0881] The system according to claim 1, comprising means for predicting the burden on a company and proposing proactive measures against risks.

[0882] (Claim 3)

[0883] The system according to claim 1, which includes means for storing corporate target information and past performance information in an information repository and updating it in real time.

[0884] "Application example 2 when combining with an emotional engine"

[0885] (Claim 1)

[0886] A means of providing a device for inputting a company's environmental targets,

[0887] A means to automatically generate an optimal action plan based on a company's environmental objectives,

[0888] A means to analyze emotional states and provide designs to improve the user experience,

[0889] A means of collecting and analyzing environmental data in real time,

[0890] Means for visualizing and presenting analysis results,

[0891] A system that includes means for evaluating progress and automatically generating reports.

[0892] (Claim 2)

[0893] The system according to claim 1, comprising means for flexibly adjusting the action plan in consideration of the user's emotional state.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for acquiring emotion analysis data in real time and providing the next steps toward achieving environmental goals. [Explanation of Symbols]

[0896] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting information for entering company goals, A processing method that automatically generates an optimal action plan based on the company's goals, A data processing means for collecting and analyzing data from measuring devices in real time, A display method for visualizing and displaying the analysis results, A report generation method that evaluates progress and automatically generates reports, A database device that stores past performance data and updates it in real time, A control system that works in conjunction with measuring devices to dynamically optimize the operation of factory machinery, A system that includes this.

2. The system according to claim 1, comprising processing means for predicting load and proposing proactive measures against risk.

3. The system according to claim 1, comprising a control algorithm that controls the operation of a machine based on an automatically generated action plan and achieves energy efficiency.

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