system

Abstract

Provide a system. 【Solution means】 Means for collecting environmental data from a plurality of data sources related to a group, Means for integrating the collected environmental data and complementing or deleting missing values and inaccurate data, Means for performing analysis to identify trends and patterns using the integrated data, Means for constructing a model for predicting future environmental risks based on the analysis results, Means for creating proposals for supporting strategic decision-making based on the prediction model, Means for displaying the generated proposals on an information display device and transmitting information to users, Means for automating part of the activity process, Means for continuously monitoring the operating status of the system and updating the system to comply with the latest technologies and regulations, Means for providing a training program aimed at improving the technical understanding of users, A system including means for providing information to visualize energy consumption and environmental management at the city level to citizens.

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] Currently, many companies are facing challenges in achieving sustainable operations such as suppressing energy consumption, managing environmental risks, and effectively utilizing resources. Also, it is extremely difficult to cope with the large volume and diversity of environmental data and make effective decisions. Furthermore, it is difficult to quickly respond to changes in environmental regulations and technological advancements, which may result in a decline in the competitiveness of enterprises.

Means for Solving the Problems

[0005] This invention provides a system that efficiently collects and integrates environmental data necessary for companies to achieve sustainable business activities, enabling advanced analysis and prediction. Specifically, it includes means for acquiring environmental data from multiple sources, integrating the data, and performing data cleansing. Furthermore, it provides means for analyzing patterns and trends using the integrated data, and means for building models to predict future environmental risks. By providing a system that includes means for generating proposals to support strategic decision-making based on predictive models and providing them through a user interface, the invention enables companies to achieve sustainable operations. In addition, it includes means for improving corporate efficiency by automating parts of business processes and for continuously monitoring the system's operational status to adapt to the latest technologies and regulations.

[0006] "Environmental data" is a general term for data that affects the environment related to a company's business activities, such as energy consumption, waste emissions, and resource use.

[0007] "Integration" is a method of centralizing data collected from multiple sources and managing and analyzing it while maintaining consistency.

[0008] "Data cleansing" is the process of removing inaccurate data and imputing missing values ​​in order to ensure the accuracy and consistency of the data.

[0009] A "pattern" refers to a consistent regularity or similarity present within data, which is revealed through data analysis.

[0010] A "trend" indicates the direction or tendency of changes in data over time and is used to predict future trends.

[0011] A "predictive model" refers to a mathematical or statistical representation used to predict future events based on past data.

[0012] "Strategic decision-making" refers to the process of formulating the optimal choices and course of action for achieving organizational goals, from a long-term perspective.

[0013] A "proposal" is a specific plan or action plan presented to achieve a particular objective, and is used to support decision-making.

[0014] A "user interface" refers to the screens and operating methods used for exchanging information between a system and a user, and should be designed with user convenience in mind.

[0015] "Automation" refers to the process of making a manual process automatically performed using technological means.

[0016] "Monitoring" is the activity of constantly checking whether a system or process is functioning correctly and making adjustments as needed.

[0017] A "training program" refers to a systematically designed course or program of education and training designed to help individuals acquire specific knowledge or skills. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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

[0019] 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.

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

[0021] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

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

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0033] 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.

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

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is an AI-powered environmental consulting system designed to help companies achieve sustainable business operations. This system operates through the collaboration of a server, terminals, and users. The roles and specific operations of each component are described below.

[0040] Server roles and operations

[0041] The server automatically collects environmental data generated daily by businesses and integrates it into a database. This data includes all environmentally relevant information, such as energy consumption, waste management, and resource utilization. The server cleanses the collected data to create an accurate dataset. Next, it analyzes the data using AI algorithms to identify patterns and trends. This makes it possible to build models that predict future environmental risks. Based on the built predictive models, the server generates recommendations to support strategic decision-making for businesses.

[0042] Terminal role and operation

[0043] The terminal displays information provided by the server on the user interface. This allows users to directly view data visualizations and analysis results. The terminal also features an interactive dashboard to support users in comparing different scenarios and considering actions based on specific suggestions. Furthermore, it automates routine tasks, efficiently handling report generation and monitoring settings.

[0044] User roles and actions

[0045] Users make decisions aimed at sustainable business operations based on information displayed via their devices. Users can also input customization requests for the system and provide feedback to the server for solutions tailored to their specific needs. Furthermore, users can improve their AI and system literacy through training programs, enabling them to utilize the system more effectively.

[0046] Specific example

[0047] For example, in the case of a company aiming to optimize energy consumption, the server analyzes past consumption data to identify peak times and factors contributing to high energy consumption. It then generates suggestions for improvements to ensure efficient energy management. The terminal displays these suggestions as interactive graphs, allowing the user to visually compare the effects of each suggestion before making a decision.

[0048] Based on the above, the present invention provides an innovative system that significantly reduces the environmental impact of companies and supports sustainable operations.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The server collects environmental data from both internal and external data sources within the company. This includes data from smart meters, IoT devices, and existing management systems.

[0052] Step 2:

[0053] The server cleanses the collected data, correcting and removing incomplete or erroneous data. This creates a clean dataset suitable for analysis.

[0054] Step 3:

[0055] The server integrates the cleansed data into the database, making it easily accessible. This enables centralized data management and allows for rapid processing.

[0056] Step 4:

[0057] The server applies AI algorithms to analyze the integrated data and identify patterns and trends. This includes detecting increases and decreases in consumption over time and identifying anomalies.

[0058] Step 5:

[0059] The server builds a predictive model based on the analyzed data. This model predicts future environmental risks and identifies high-risk areas.

[0060] Step 6:

[0061] Based on the information identified by the predictive model, the server generates specific suggestions to support strategic decision-making. These include improvement measures and risk mitigation strategies.

[0062] Step 7:

[0063] The terminal displays suggestions and analysis results received from the server in its user interface. Interactive graphs and dashboards are also provided to make the information easy for users to understand.

[0064] Step 8:

[0065] Based on the information on their device, users evaluate the effectiveness of each proposal and decide on specific actions as needed. Different strategic scenarios can be considered to support the decision-making process.

[0066] Step 9:

[0067] The terminal automates routine tasks and monitoring based on predetermined actions, improving operational efficiency. This automation allows users to focus on higher-value tasks.

[0068] Step 10:

[0069] The server continuously monitors system performance and updates the system based on new technologies and regulations to maintain optimal functionality.

[0070] Step 11:

[0071] Users will participate in training programs on AI systems and learn how to use the system effectively. This will further enhance their utilization of the system.

[0072] (Example 1)

[0073] 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."

[0074] In recent years, companies have been required to operate their businesses sustainably, making the reduction of environmental impact a crucial issue. However, companies generate large amounts of environmental data on a daily basis, and effective decision-making based on this data requires a complex process of data collection, analysis, and proposal generation. Therefore, there is a need to develop systems that combine efficient data processing, highly accurate prediction, and automation of business processes.

[0075] 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.

[0076] In this invention, the server includes means for collecting and integrating environmental information from multiple data sources, means for correcting or removing outliers and missing values ​​from the collected data, and means for identifying patterns and trends by utilizing a generative AI model with the cleansed data. This enables companies to automate complex data processing and make highly accurate strategic decisions.

[0077] A "data source" refers to a location or system where various environmental information generated or acquired by a company is stored.

[0078] "Environmental information" refers to all information, including data on energy consumption, waste management, and resource utilization.

[0079] "Data cleansing" refers to the process of detecting outliers and missing values ​​from data and correcting or removing them.

[0080] "Generative AI models" refer to artificial intelligence technologies used to analyze data and predict patterns and trends.

[0081] "Patterns and trends" refer to recurring characteristics derived from past data and information that predicts future changes.

[0082] "Recommendations" refer to information that suggests specific actions or strategies that a company should take based on the analysis results.

[0083] An "interactive interface" refers to a user screen designed to allow users to intuitively operate and obtain information.

[0084] "Business process automation" refers to the process of automatically performing some tasks that were previously done manually using software or machines.

[0085] "Technological updates and compliance with new regulations" refers to the adjustments and adaptations made to ensure that a system continues to keep up with the latest technologies and changes in laws and regulations.

[0086] A "training program" refers to a program provided to users to learn how to operate the system and acquire related knowledge.

[0087] This invention is an environmental consulting system designed to support the sustainable operation of businesses. The system operates with the coordinated cooperation of its server, terminal, and user components.

[0088] The server automatically collects and integrates environmental information from various data sources within the enterprise. Hardware used includes cloud servers and local servers, enabling real-time processing of large amounts of data. Software includes database management systems (DBMS) and machine learning algorithms. The data is cleansed, correcting or removing outliers and missing values. The server then uses this cleansed data to leverage generative AI models to identify patterns and trends. This allows enterprises to predict future environmental risks and proactively develop countermeasures.

[0089] The terminal displays analysis results and suggestions provided by the server on its user interface. The hardware used is a personal computer or tablet, and the software includes a dedicated dashboard application. The interactive dashboard allows users to visually review, compare, and select proposed strategies. The terminal also includes features to automate business processes, such as automating the creation of periodic reports.

[0090] Users play a role in implementing sustainable strategies based on the information provided on their devices. For example, they can select and implement specific measures to reduce peak energy consumption. Users can also provide feedback to the system and request customizations as needed. Furthermore, users can improve their knowledge and skills to make the most of the system's capabilities by participating in training programs on system usage.

[0091] For example, if a company aims to improve energy efficiency, the server analyzes historical energy consumption data to identify peak consumption times. Based on this, it generates specific suggestions for efficient energy use and displays them on the user's device. The user can then adjust their energy strategy based on these suggestions.

[0092] An example of a prompt message would be, "Analyze energy consumption data from the past three months, identify peak times, and propose the optimal energy management strategy."

[0093] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0094] Step 1:

[0095] The server collects environmental information from various data sources within the company. Inputs include raw data on energy consumption and resource use. The server retrieves this data and integrates it into a centrally managed database on the cloud. Specifically, it periodically retrieves data from APIs and sensors and saves it to the database. The output is the integrated raw dataset.

[0096] Step 2:

[0097] The server performs data cleansing on the collected raw data. The input includes an integrated raw dataset. The server detects outliers and processes the data to correct or remove them. This results in a cleansed dataset. Specific operations include imputing missing data and smoothing spikes in data. The output is the cleansed data from which anomalies have been removed.

[0098] Step 3:

[0099] The server inputs cleansed data into a generating AI model and performs data analysis. The input is a cleaned dataset. The server performs data calculations to predict future trends by identifying patterns and trends in the AI ​​model. Specifically, this includes training and evaluating a predictive model using machine learning algorithms. The output is a model that predicts future environmental risks.

[0100] Step 4:

[0101] The server generates strategic recommendations based on a predictive model. The input includes the predictive model. The server analyzes this model and generates recommendations for environmental management and energy efficiency tailored to the company. Specifically, the process involves creating concrete action plans based on the insights gained from the model. The output is a list of strategic recommendations.

[0102] Step 5:

[0103] The terminal displays proposals sent from the server in its user interface. Input includes a generated list of strategic proposals. The terminal uses an interactive dashboard to visualize the proposals, making them easy for the user to understand. Specific actions include displaying proposals as charts and graphs to support user decision-making. Output is a visualized report that the user can view.

[0104] Step 6:

[0105] Users make decisions for sustainable operations based on the displayed suggestions. They analyze the provided information and decide on actions that meet their specific needs. These actions include considering the suggestions, developing a feasible action plan, and implementing it. The output is the determined sustainability strategy.

[0106] (Application Example 1)

[0107] 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."

[0108] In recent years, with the advancement of urbanization, optimizing energy consumption and reducing environmental impact have become crucial issues. However, individual citizens lack access to specific information regarding energy use and environmental management, making it difficult for them to take appropriate action. Therefore, real-time data visualization and concrete proposals to citizens are needed to enhance the sustainability of the entire city.

[0109] 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.

[0110] In this invention, the server includes means for collecting environmental data from multiple data sources related to a population, means for integrating the collected environmental data and supplementing or removing missing or inaccurate data, and means for performing analysis to identify trends and patterns using the integrated data. This makes it possible to visualize energy consumption and environmental management at the city level for citizens and provide them with specific information and suggestions necessary to take sustainable actions.

[0111] A "group" is a unit to which multiple individuals or organizations belong, such as a society or an organization, and is a collection of members who share a common purpose or challenge.

[0112] A "data source" is the origin of information and data, and the source of information needed by a system to collect the data it requires.

[0113] "Environmental data" refers to various types of information related to the environment, such as energy consumption, waste management, and resource utilization, and is data necessary for analysis toward realizing a sustainable society.

[0114] An "information display device" is a device that provides information to a user visually, and includes monitors, displays, tablets, and other similar devices.

[0115] A "user" is an individual or organization that uses the system to obtain information and receive suggestions.

[0116] "Technological understanding" refers to the ability to deepen one's knowledge and awareness of a particular technology and to use it more effectively.

[0117] A "training program" is an educational curriculum designed to teach technology and knowledge and improve skills.

[0118] A "trend" is a concept that indicates a certain direction or pattern observed in a series of data.

[0119] A "trend" refers to the direction of change or development observed over a specific period of time.

[0120] To realize this invention, a system is constructed in which the server, terminal, and user each play a specific role.

[0121] The server collects environmental data from multiple sources related to society and organizations. The collected data is first integrated, and missing or inaccurate data is either filled in or removed. Next, analysis is performed to identify trends and patterns based on the integrated data. AI algorithms are used for this analysis. The server uses the results of this analysis to build models that predict future environmental risks and generate suggestions to support strategic decision-making.

[0122] The terminal visually displays information provided by the server on its interface. This allows users to check environmental data and analysis results in real time. The data is visualized and displayed in an interactive format, allowing users to examine items of interest in detail and compare various scenarios. The terminal also has functions for automated report generation and monitoring settings.

[0123] Users plan and execute sustainable actions based on information obtained from their devices. By implementing specific recommended actions, they contribute to improving the management of energy consumption and environmental impact across the city. Users can also contribute to system improvements by providing feedback to the server with customization requests.

[0124] For example, consider a citizen who uses a smartphone app to check the times of day when electricity consumption is minimized on holidays. By following the app's suggestions, this citizen can adjust their laundry and cooking times to optimize energy consumption. Such support can help reduce peak energy consumption across the entire city and contribute to sustainable operations.

[0125] An example of a prompt for a generated AI model is: "Please suggest an app design to visualize energy consumption in a smart city. How can we enable citizens to view data in real time and take concrete energy-saving actions?"

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The server collects environmental data from multiple data sources related to society and organizations. The input in this step is environmental data such as energy consumption, waste management, and resource use, while the output is raw data before integration. Specifically, it acquires data from sensors and external databases and prepares it for data cleansing.

[0129] Step 2:

[0130] The server cleanses the collected raw data, imputing or removing missing or inaccurate values. The input is raw environmental data, and the output is clean, prepared data. Specifically, this involves imputation using statistical methods and filtering of inconsistent data.

[0131] Step 3:

[0132] The server utilizes the cleansed data and performs analysis using AI algorithms to identify trends and patterns. The input is clean data, and the output is analyzed trend data. In this step, the data is fed into a machine learning model, and pattern analysis is performed based on the time history.

[0133] Step 4:

[0134] The server builds a model to predict future environmental risks based on the analysis results. The input is trend data, and the output is the predictive model. Specifically, it trains an AI model and generates future risk scenarios using a risk assessment algorithm.

[0135] Step 5:

[0136] The server generates proposals to support strategic decision-making from the built predictive model. The input is the predictive model, and the output is specific proposals. The generated AI model is used to output risk reduction measures for each scenario.

[0137] Step 6:

[0138] The terminal visually displays suggestions retrieved from the server on its interface. The input is the specific suggestion content, and the output is a visualized information presentation screen. Specifically, it displays interactive graphs and charts to organize information in a way that is easily understandable to the user.

[0139] Step 7:

[0140] The user plans and executes sustainable actions based on the information presented on the device. The input is the suggested information displayed on the device, and the output is an action plan based on that information. For example, the user might select time periods to reduce electricity consumption based on the suggestions and adjust individual activities accordingly.

[0141] 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.

[0142] This invention combines an emotion engine with an enterprise's environmental management support system to assist in effective decision-making that responds to user emotions. The system operates through server, terminal, and user interaction.

[0143] Server roles and operations

[0144] The server collects and integrates environmental data from a company's diverse data sources and performs data cleansing. Furthermore, it analyzes the data using AI algorithms to identify patterns and trends. Based on this, it builds predictive models and generates strategic recommendations for environmental risks. It also incorporates an emotion engine, incorporating user sentiment data into the analysis to personalize recommendations and enable more user-oriented strategic suggestions.

[0145] Terminal role and operation

[0146] The terminal is responsible for displaying information sent from the server on the user interface. In addition to normal data display functions, it adjusts the interface according to the user's emotional state based on information from the emotion engine, making the system intuitive for the user. Furthermore, when automating routine tasks, it can select the optimal timing and format by taking emotional data into consideration.

[0147] User roles and actions

[0148] Users make decisions for sustainable business operations based on data and suggestions displayed through their devices. As users interact with the system, the emotion engine instantly recognizes emotional data, and the priority and content of suggestions are automatically adjusted accordingly. Furthermore, participating in training programs that reflect emotional information contributes to improved system utilization.

[0149] Specific example

[0150] For example, when a user is presented with a proposal for a new environmental strategy, the emotion engine may detect the user's distress. In this case, the server uses the engine's output to restructure the proposal in a more understandable way and add information to aid comprehension. The terminal then simplifies the interface on the spot and displays information to reduce the user's burden. As a result, the user can make a decision that is emotionally acceptable.

[0151] Based on the above, the present invention, which combines an emotion engine, provides a system that can support flexible and effective environmental management decision-making while taking into account the user's emotions.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The server collects and integrates environmental data from internal and external data sources within the company. After data collection, it performs data cleansing to prepare a dataset suitable for analysis.

[0155] Step 2:

[0156] The server applies AI algorithms to analyze data and identify patterns and trends. This allows it to build models for predicting future environmental risks.

[0157] Step 3:

[0158] The server uses an emotion engine to retrieve user emotion data. This retrieved emotion data is then used to personalize strategic recommendations.

[0159] Step 4:

[0160] The server generates suggestions to support strategic decision-making at the appropriate time and with appropriate content, based on predictive models and sentiment data.

[0161] Step 5:

[0162] The device displays data and suggestions sent from the server via a user interface. The display format is adjusted according to the user's emotional state.

[0163] Step 6:

[0164] Users make decisions based on suggestions presented through their devices. The system provides a more user-friendly operating environment through interface adjustments that reflect the user's emotions.

[0165] Step 7:

[0166] The device optimizes the automation process for routine tasks based on information from the emotion engine. The timing and content of task execution are set according to the user's emotions.

[0167] Step 8:

[0168] The server continuously monitors the system's operational status and updates the system to meet new technological and regulatory requirements.

[0169] Step 9:

[0170] Users can participate in an emotionally conscious training program and learn how to effectively operate the system.

[0171] (Example 2)

[0172] 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 as the "terminal".

[0173] For companies to manage environmental risks and achieve sustainable operations, they need to effectively collect and analyze vast amounts of information. However, traditional systems do not consider user emotions, resulting in suggestions that are not optimized for individual users and failing to fully realize the benefits of decision-making. Furthermore, unintuitive user interfaces can lead to user stress and confusion.

[0174] 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.

[0175] In this invention, the server includes means for integrating environmental information using multiple data sets collected from companies, means for removing unnecessary data from the information, and means for extracting trends from the integrated information using an analytical algorithm. This enables personalized strategic proposals that take user emotions into account, leading to more effective decision-making.

[0176] A "business" is a social entity that engages in commercial activities and provides goods and services.

[0177] A "data collection" is a set of information that has been collected and stored for a specific purpose.

[0178] "Environmental information" refers to all information related to the natural environment and human activities.

[0179] "Integration" is the process of combining different pieces of information to form a unified whole.

[0180] "Removal" means removing unnecessary or inaccurate elements.

[0181] A "trend" is a certain direction or sign of change that can be observed as a result of analyzing data.

[0182] "Risk" refers to the possibility of undesirable outcomes occurring.

[0183] A "model" is an abstract or concrete representation used to understand and predict a particular phenomenon or process.

[0184] A "strategic proposal" refers to a planned action plan designed to achieve a specific objective.

[0185] "Display information" refers to data and instructions that are provided to the user visually.

[0186] A "routine operation" is a standard process that is repeatedly performed according to a specific procedure.

[0187] "Emotional state" refers to the temporary psychological feelings a person experiences in a particular situation.

[0188] A "generative AI model" is an artificial intelligence system that has the ability to generate new information and content based on large amounts of data.

[0189] A "prompt" refers to an instruction or question given to a system to prompt it to take a specific response or action.

[0190] "Information means" refers to a set of technologies and methods for collecting, processing, storing, and transmitting information.

[0191] "Usage" refers to the circumstances and conditions under which a system or tool is actually used.

[0192] "Updating" means bringing a system to its latest state by incorporating new data and technologies.

[0193] "Information technology literacy" refers to the ability and skills to understand and utilize information technology.

[0194] A "training program" refers to a learning process designed to acquire specific goals or skills.

[0195] This invention is a system designed to support corporate environmental management. The system aims to provide users with optimal strategic recommendations by integrating and analyzing vast amounts of data collected from both inside and outside the company. Furthermore, the system incorporates an emotion engine, enabling it to provide information that takes into account the user's emotional state.

[0196] The server collects environmental data from corporate databases and external sources. Specifically, it acquires data using database management systems and APIs, and performs data cleansing using data preparation tools (e.g., OpenRefine). Then, it uses AI analysis software (e.g., TENSORFLOW®) to extract trends and patterns from the data. This allows for the construction of a model that predicts future environmental risks and generates strategic recommendations.

[0197] The device is responsible for displaying this information through the user interface. The front-end framework used (e.g., React or Vue.js) provides an intuitive and easy-to-understand interface. Furthermore, a sentiment analysis engine analyzes the user's emotions in real time and adjusts the interface accordingly. For example, if the user is stressed, the interface is simplified, and only important information is highlighted.

[0198] Users make decisions for sustainable operations based on this displayed information. The server supports the user experience by using a generative AI model to generate specific prompts. For example, it can generate a prompt such as, "The user is feeling anxious, so restructure the high-priority suggestions into easily understandable language."

[0199] In this way, the system, which incorporates an emotion engine, can support flexible and effective environmental management decision-making optimized for the user's emotions.

[0200] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0201] Step 1:

[0202] The server collects environmental data via internal and external databases and APIs. Input is raw data obtained from various sources, and output is a unified format of this data. Specifically, the server executes queries, collects the necessary data, and then converts that data into a unified format.

[0203] Step 2:

[0204] The server performs data cleansing to organize the integrated data. The input is the data integrated in step 1, and the output is the data from which unnecessary elements have been removed and accuracy has been verified. Specifically, the server uses cleansing tools to remove missing values ​​and duplicates and correct outliers.

[0205] Step 3:

[0206] The server feeds the cleansed data into an AI analysis algorithm for analysis. The input is the cleansed data, and the output is the patterns and trends extracted from the data. Specifically, the server uses analysis software to analyze the data using regression analysis and clustering methods.

[0207] Step 4:

[0208] The server builds a model to predict environmental risks based on the analysis results. The input is the patterns and trends obtained in step 3, and the output is the model for predicting risks. Specifically, the server uses machine learning techniques to simulate future scenarios from past data.

[0209] Step 5:

[0210] The server generates and outputs strategic proposals based on a predictive model. The input is a model for predicting risk, and the output is a strategic proposal presented to the user. Specifically, the server uses a generation AI model to create proposals that reflect the analysis results.

[0211] Step 6:

[0212] The terminal displays strategic proposals sent from the server on its user interface. The input is the strategic proposals generated by the server, and the output is the information visually displayed on the screen. Specifically, the terminal provides the information with an appropriate layout and color scheme.

[0213] Step 7:

[0214] The device analyzes the user's emotional data using an emotion engine and adjusts the interface accordingly. The input is the user's real-time emotional data, and the output is the interface adjustment based on the user's emotions. Specifically, the device runs emotion analysis software and changes the layout and amount of information displayed to match the user's state.

[0215] Step 8:

[0216] The user makes a decision based on the information presented. The input is the strategic proposal displayed on the terminal, and the output is the decision chosen by the user. In terms of specific actions, the user interacts with the interface and expresses their opinion on the proposal.

[0217] Step 9:

[0218] The server collects user decisions and feedback, and uses this information to improve future suggestions. The input is user feedback, and the output is the improved suggestion. Specifically, the server accumulates feedback information and retrains its machine learning model to improve the accuracy of the suggestions.

[0219] (Application Example 2)

[0220] 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".

[0221] In modern urban environments, traffic management is complex and can amplify residents' stress and anxiety. In particular, stress caused by traffic congestion and delays significantly impacts residents' daily lives. Furthermore, corporate environmental management systems often make decisions without considering users' emotional states, resulting in proposals that don't suit the user's situation. Therefore, there is a need for a system that utilizes emotional data to support decision-making, reduce user stress, and manage optimal transportation options.

[0222] 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. In this invention, the server includes means for acquiring data from information sources, means for integrating the acquired data and supplementing or removing missing values ​​or inaccurate data, and means for performing analysis to identify patterns and trends using the integrated data. This enables optimal traffic management and decision support that takes into account the user's emotional data.

[0223] text

[0224] An "information source" refers to the fundamental devices or platforms used to provide data.

[0225] "Data integration" is the process of organizing data obtained from different sources into a single, unified format.

[0226] "Missing values" refer to a state in a dataset where information that should be present is missing.

[0227] "Inaccurate data" refers to data that contains false information or biases, and therefore lacks reliability in analysis.

[0228] "Patterns and trends" are elements that indicate recurring tendencies or movements found in data.

[0229] "Performing analysis" is the process of applying statistical methods and machine learning techniques to data to extract meaningful information.

[0230] A "risk prediction model" is a mathematical or statistical model built to predict potential problems or difficulties in the future.

[0231] "Suggestions to support decision-making" refers to information and advice generated to effectively assist users in making decisions.

[0232] A "user display device" is a digital screen device used to convey information visually.

[0233] "Automating part of a process" means minimizing human intervention and allowing machines or software to independently execute the procedures.

[0234] "Monitoring and updating" means constantly checking the functionality and performance of a system and making improvements to adapt to new technologies and regulations.

[0235] An "educational program" is a series of instructions designed to enhance the user's understanding and technical skills.

[0236] An "emotional processing device" is a system or device that analyzes a user's emotional state and takes appropriate countermeasures.

[0237] "Managing transportation in an urban environment" means optimizing elements related to transportation and movement, and making adjustments and controls to realize efficient urban living.

[0238] The system for implementing this invention supports traffic management decision-making that takes user emotions into consideration.

[0239] The server efficiently acquires data from various sources, integrates that data, and supplements or removes inaccurate values. Based on the integrated data, it analyzes patterns and trends using AI algorithms. Furthermore, it builds a risk prediction model based on this analysis and generates optimal recommendations for the user. To reflect the user's emotional state in these recommendations, it uses an emotion processing device.

[0240] The user's device receives suggestions generated by the server in real time and presents them to the user visually. The user interface reflects the user's current emotional state, and the information is adjusted to be more easily understood. This makes it easier for the user to intuitively accept suggestions such as stress reduction measures and efficient modes of transportation.

[0241] For example, if traffic congestion occurs during the morning commute, the server generates alternative route suggestions and presents them to the user without causing any inconvenience. If the emotion processing unit determines that the user is frustrated with the traffic situation, it automatically adjusts the color scheme and font size to create a more calming environment.

[0242] Example prompt: "Please consider emotional data when designing the interface for a smart city's traffic management system to reduce resident stress."

[0243] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0244] text

[0245] Step 1:

[0246] The server collects data in real time from various information sources. Inputs include traffic sensor data, weather data, and historical traffic history. This data is stored in a database to obtain output for the next analysis step.

[0247] Step 2:

[0248] The server cleanses the collected data, correcting or removing missing values ​​and inaccurate data. This process transforms the information in the database into accurate and consistent data. As a result, integrated, cleansed data is output.

[0249] Step 3:

[0250] The server applies AI algorithms to cleansed data to identify patterns and trends. This input includes integrated data from a database. The output generates predicted congestion patterns and potential risks.

[0251] Step 4:

[0252] The server uses AI-generated risk patterns to build a model for predicting future traffic risks. It utilizes identified trend data as input, and the predictive model is completed as output.

[0253] Step 5:

[0254] The server generates suggestions that take into account the user's emotional state, based on a predictive model. The emotion processing unit uses the user's collected emotional data as input and outputs suggestions that optimize the content presented in the user interface.

[0255] Step 6:

[0256] The terminal displays optimized suggestions generated by the server to the user. The user interface visualizes the output information by dynamically adjusting colors and font sizes, reflecting the emotional state input from the user.

[0257] Step 7:

[0258] The user receives information from their device and determines their own transportation strategy. Optimized suggestion information from the device is used as input, and the user's next action is guided as output.

[0259] 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.

[0260] 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.

[0261] 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.

[0262] [Second Embodiment]

[0263] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0264] 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.

[0265] 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).

[0266] 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.

[0267] 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.

[0268] 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).

[0269] 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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".

[0275] This invention is an AI-powered environmental consulting system designed to help companies achieve sustainable business operations. This system operates through the collaboration of a server, terminals, and users. The roles and specific operations of each component are described below.

[0276] Server roles and operations

[0277] The server automatically collects environmental data generated daily by businesses and integrates it into a database. This data includes all environmentally relevant information, such as energy consumption, waste management, and resource utilization. The server cleanses the collected data to create an accurate dataset. Next, it analyzes the data using AI algorithms to identify patterns and trends. This makes it possible to build models that predict future environmental risks. Based on the built predictive models, the server generates recommendations to support strategic decision-making for businesses.

[0278] Terminal role and operation

[0279] The terminal displays information provided by the server on the user interface. This allows users to directly view data visualizations and analysis results. The terminal also features an interactive dashboard to support users in comparing different scenarios and considering actions based on specific suggestions. Furthermore, it automates routine tasks, efficiently handling report generation and monitoring settings.

[0280] User roles and actions

[0281] Users make decisions aimed at sustainable business operations based on information displayed via their devices. Users can also input customization requests for the system and provide feedback to the server for solutions tailored to their specific needs. Furthermore, users can improve their AI and system literacy through training programs, enabling them to utilize the system more effectively.

[0282] Specific example

[0283] For example, in the case of a company aiming to optimize energy consumption, the server analyzes past consumption data to identify peak time periods and factors contributing to high energy consumption. Then, it generates improvement proposals for efficient energy management. The terminal displays these proposals as interactive graphs, allowing users to visually compare the effects of each proposal and make decisions.

[0284] As described above, the present invention provides an innovative system for significantly reducing the environmental impact of enterprises and supporting sustainable operations.

[0285] The following describes the processing flow.

[0286] Step 1:

[0287] The server collects environmental-related data from internal and external data sources within the enterprise. This includes data from smart meters, IoT devices, and existing management systems.

[0288] Step 2:

[0289] The server performs a process of cleansing the collected data to correct and remove incomplete or incorrect data. This creates a clean dataset suitable for analysis.

[0290] Step 3:

[0291] The server integrates the cleansed data into a database to make it easily accessible. This enables unified management of data and allows for rapid processing.

[0292] Step 4:

[0293] The server applies AI algorithms to analyze the integrated data and identify patterns and trends. This includes detecting increases and decreases in consumption over time and identifying outliers.

[0294] Step 5:

[0295] The server builds a predictive model based on the analyzed data. This model predicts future environmental risks and identifies high-risk areas.

[0296] Step 6:

[0297] Based on the information identified by the predictive model, the server generates specific suggestions to support strategic decision-making. These include improvement measures and risk mitigation strategies.

[0298] Step 7:

[0299] The terminal displays suggestions and analysis results received from the server in its user interface. Interactive graphs and dashboards are also provided to make the information easy for users to understand.

[0300] Step 8:

[0301] Based on the information on their device, users evaluate the effectiveness of each proposal and decide on specific actions as needed. Different strategic scenarios can be considered to support the decision-making process.

[0302] Step 9:

[0303] The terminal automates routine tasks and monitoring based on predetermined actions, improving operational efficiency. This automation allows users to focus on higher-value tasks.

[0304] Step 10:

[0305] The server continuously monitors system performance and updates the system based on new technologies and regulations to maintain optimal functionality.

[0306] Step 11:

[0307] The user takes a training program on the AI system and acquires effective usage methods of the system. Thereby, the utilization rate of the system can be further increased.

[0308] (Example 1)

[0309] Next, 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".

[0310] In recent years, companies are required to operate sustainable businesses, and reducing environmental impact has become an important issue. However, since companies generate a large amount of environment-related data daily, a complex process of data collection, analysis, and proposal generation is required to make effective decisions using this data. Therefore, the development of a system that combines efficient data processing, highly accurate prediction, and automation of business processes is required.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0312] In this invention, the server includes means for collecting and integrating environment-related information from a plurality of data sources, means for correcting or removing outliers and missing values from the collected data, and means for identifying patterns and trends by utilizing an AI model generated using the cleansed data. Thereby, companies can automate complex data processing and make highly accurate strategic decisions.

[0313] "Data source" refers to a location or system where various environment-related information generated or acquired by a company is stored.

[0314] "Environment-related information" refers to all information including data related to energy consumption, waste management, and resource utilization.

[0315] "Data cleansing" refers to the process of detecting outliers and missing values ​​from data and correcting or removing them.

[0316] "Generative AI models" refer to artificial intelligence technologies used to analyze data and predict patterns and trends.

[0317] "Patterns and trends" refer to recurring characteristics derived from past data and information that predicts future changes.

[0318] "Recommendations" refer to information that suggests specific actions or strategies that a company should take based on the analysis results.

[0319] An "interactive interface" refers to a user screen designed to allow users to intuitively operate and obtain information.

[0320] "Business process automation" refers to the process of automatically performing some tasks that were previously done manually using software or machines.

[0321] "Technological updates and compliance with new regulations" refers to the adjustments and adaptations made to ensure that a system continues to keep up with the latest technologies and changes in laws and regulations.

[0322] A "training program" refers to a program provided to users to learn how to operate the system and acquire related knowledge.

[0323] This invention is an environmental consulting system designed to support the sustainable operation of businesses. The system operates with the coordinated cooperation of its server, terminal, and user components.

[0324] The server automatically collects and integrates environmental information from various data sources within the enterprise. Hardware used includes cloud servers and local servers, enabling real-time processing of large amounts of data. Software includes database management systems (DBMS) and machine learning algorithms. The data is cleansed, correcting or removing outliers and missing values. The server then uses this cleansed data to leverage generative AI models to identify patterns and trends. This allows enterprises to predict future environmental risks and proactively develop countermeasures.

[0325] The terminal displays analysis results and suggestions provided by the server on its user interface. The hardware used is a personal computer or tablet, and the software includes a dedicated dashboard application. The interactive dashboard allows users to visually review, compare, and select proposed strategies. The terminal also includes features to automate business processes, such as automating the creation of periodic reports.

[0326] Users play a role in implementing sustainable strategies based on the information provided on their devices. For example, they can select and implement specific measures to reduce peak energy consumption. Users can also provide feedback to the system and request customizations as needed. Furthermore, users can improve their knowledge and skills to make the most of the system's capabilities by participating in training programs on system usage.

[0327] For example, if a company aims to improve energy efficiency, the server analyzes historical energy consumption data to identify peak consumption times. Based on this, it generates specific suggestions for efficient energy use and displays them on the user's device. The user can then adjust their energy strategy based on these suggestions.

[0328] An example of a prompt message would be, "Analyze energy consumption data from the past three months, identify peak times, and propose the optimal energy management strategy."

[0329] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0330] Step 1:

[0331] The server collects environmental information from various data sources within the company. Inputs include raw data on energy consumption and resource use. The server retrieves this data and integrates it into a centrally managed database on the cloud. Specifically, it periodically retrieves data from APIs and sensors and saves it to the database. The output is the integrated raw dataset.

[0332] Step 2:

[0333] The server performs data cleansing on the collected raw data. The input includes an integrated raw dataset. The server detects outliers and processes the data to correct or remove them. This results in a cleansed dataset. Specific operations include imputing missing data and smoothing spikes in data. The output is the cleansed data from which anomalies have been removed.

[0334] Step 3:

[0335] The server inputs cleansed data into a generating AI model and performs data analysis. The input is a cleaned dataset. The server performs data calculations to predict future trends by identifying patterns and trends in the AI ​​model. Specifically, this includes training and evaluating a predictive model using machine learning algorithms. The output is a model that predicts future environmental risks.

[0336] Step 4:

[0337] The server generates strategic recommendations based on a predictive model. The input includes the predictive model. The server analyzes this model and generates recommendations for environmental management and energy efficiency tailored to the company. Specifically, the process involves creating concrete action plans based on the insights gained from the model. The output is a list of strategic recommendations.

[0338] Step 5:

[0339] The terminal displays proposals sent from the server in its user interface. Input includes a generated list of strategic proposals. The terminal uses an interactive dashboard to visualize the proposals, making them easy for the user to understand. Specific actions include displaying proposals as charts and graphs to support user decision-making. Output is a visualized report that the user can view.

[0340] Step 6:

[0341] Users make decisions for sustainable operations based on the displayed suggestions. They analyze the provided information and decide on actions that meet their specific needs. These actions include considering the suggestions, developing a feasible action plan, and implementing it. The output is the determined sustainability strategy.

[0342] (Application Example 1)

[0343] 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 glasses 214 will be referred to as the "terminal."

[0344] In recent years, with the advancement of urbanization, optimizing energy consumption and reducing environmental impact have become crucial issues. However, individual citizens lack access to specific information regarding energy use and environmental management, making it difficult for them to take appropriate action. Therefore, real-time data visualization and concrete proposals to citizens are needed to enhance the sustainability of the entire city.

[0345] 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.

[0346] In this invention, the server includes means for collecting environmental data from multiple data sources related to a population, means for integrating the collected environmental data and supplementing or removing missing or inaccurate data, and means for performing analysis to identify trends and patterns using the integrated data. This makes it possible to visualize energy consumption and environmental management at the city level for citizens and provide them with specific information and suggestions necessary to take sustainable actions.

[0347] A "group" is a unit to which multiple individuals or organizations belong, such as a society or an organization, and is a collection of members who share a common purpose or challenge.

[0348] A "data source" is the origin of information and data, and the source of information needed by a system to collect the data it requires.

[0349] "Environmental data" refers to various types of information related to the environment, such as energy consumption, waste management, and resource utilization, and is data necessary for analysis toward realizing a sustainable society.

[0350] An "information display device" is a device that provides information to a user visually, and includes monitors, displays, tablets, and other similar devices.

[0351] A "user" is an individual or organization that uses the system to obtain information and receive suggestions.

[0352] "Technological understanding" refers to the ability to deepen one's knowledge and awareness of a particular technology and to use it more effectively.

[0353] A "training program" is an educational curriculum designed to teach technology and knowledge and improve skills.

[0354] A "trend" is a concept that indicates a certain direction or pattern observed in a series of data.

[0355] A "trend" refers to the direction of change or development observed over a specific period of time.

[0356] To realize this invention, a system is constructed in which the server, terminal, and user each play a specific role.

[0357] The server collects environmental data from multiple sources related to society and organizations. The collected data is first integrated, and missing or inaccurate data is either filled in or removed. Next, analysis is performed to identify trends and patterns based on the integrated data. AI algorithms are used for this analysis. The server uses the results of this analysis to build models that predict future environmental risks and generate suggestions to support strategic decision-making.

[0358] The terminal visually displays information provided by the server on its interface. This allows users to check environmental data and analysis results in real time. The data is visualized and displayed in an interactive format, allowing users to examine items of interest in detail and compare various scenarios. The terminal also has functions for automated report generation and monitoring settings.

[0359] Users plan and execute sustainable actions based on information obtained from their devices. By implementing specific recommended actions, they contribute to improving the management of energy consumption and environmental impact across the city. Users can also contribute to system improvements by providing feedback to the server with customization requests.

[0360] For example, consider a citizen who uses a smartphone app to check the times of day when electricity consumption is minimized on holidays. By following the app's suggestions, this citizen can adjust their laundry and cooking times to optimize energy consumption. Such support can help reduce peak energy consumption across the entire city and contribute to sustainable operations.

[0361] An example of a prompt for a generated AI model is: "Please suggest an app design to visualize energy consumption in a smart city. How can we enable citizens to view data in real time and take concrete energy-saving actions?"

[0362] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0363] Step 1:

[0364] The server collects environmental data from multiple data sources related to society and organizations. The input in this step is environmental data such as energy consumption, waste management, and resource use, while the output is raw data before integration. Specifically, it acquires data from sensors and external databases and prepares it for data cleansing.

[0365] Step 2:

[0366] The server cleanses the collected raw data, imputing or removing missing or inaccurate values. The input is raw environmental data, and the output is clean, prepared data. Specifically, this involves imputation using statistical methods and filtering of inconsistent data.

[0367] Step 3:

[0368] The server utilizes the cleansed data and performs analysis using AI algorithms to identify trends and patterns. The input is clean data, and the output is analyzed trend data. In this step, the data is fed into a machine learning model, and pattern analysis is performed based on the time history.

[0369] Step 4:

[0370] The server builds a model to predict future environmental risks based on the analysis results. The input is trend data, and the output is the predictive model. Specifically, it trains an AI model and generates future risk scenarios using a risk assessment algorithm.

[0371] Step 5:

[0372] The server generates proposals to support strategic decision-making from the built predictive model. The input is the predictive model, and the output is specific proposals. The generated AI model is used to output risk mitigation measures for each scenario.

[0373] Step 6:

[0374] The terminal visually displays suggestions retrieved from the server on its interface. The input is the specific suggestion content, and the output is a visualized information presentation screen. Specifically, it displays interactive graphs and charts to organize information in a way that is easily understandable to the user.

[0375] Step 7:

[0376] The user plans and executes sustainable actions based on the information presented on the device. The input is the suggested information displayed on the device, and the output is an action plan based on that information. For example, the user might select time periods to reduce electricity consumption based on the suggestions and adjust individual activities accordingly.

[0377] 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.

[0378] This invention combines an emotion engine with an enterprise's environmental management support system to assist in effective decision-making that responds to user emotions. The system operates through server, terminal, and user interaction.

[0379] Server roles and operations

[0380] The server collects and integrates environmental data from a company's diverse data sources and performs data cleansing. Furthermore, it analyzes the data using AI algorithms to identify patterns and trends. Based on this, it builds predictive models and generates strategic recommendations for environmental risks. It also incorporates an emotion engine, incorporating user sentiment data into the analysis to personalize recommendations and enable more user-oriented strategic suggestions.

[0381] Terminal role and operation

[0382] The terminal is responsible for displaying information sent from the server on the user interface. In addition to normal data display functions, it adjusts the interface according to the user's emotional state based on information from the emotion engine, making the system intuitive for the user. Furthermore, when automating routine tasks, it can select the optimal timing and format by taking emotional data into consideration.

[0383] User roles and actions

[0384] Users make decisions for sustainable business operations based on data and suggestions displayed through their devices. As users interact with the system, the emotion engine instantly recognizes emotional data, and the priority and content of suggestions are automatically adjusted accordingly. Furthermore, participating in training programs that reflect emotional information contributes to improved system utilization.

[0385] Specific example

[0386] For example, when a user is presented with a proposal for a new environmental strategy, the emotion engine may detect the user's distress. In this case, the server uses the engine's output to restructure the proposal in a more understandable way and add information to aid comprehension. The terminal then simplifies the interface on the spot and displays information to reduce the user's burden. As a result, the user can make a decision that is emotionally acceptable.

[0387] Based on the above, the present invention, which combines an emotion engine, provides a system that can support flexible and effective environmental management decision-making while taking into account the user's emotions.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The server collects and integrates environmental data from internal and external data sources within the company. After data collection, it performs data cleansing to prepare a dataset suitable for analysis.

[0391] Step 2:

[0392] The server applies AI algorithms to analyze data and identify patterns and trends. This allows it to build models for predicting future environmental risks.

[0393] Step 3:

[0394] The server uses an emotion engine to retrieve user emotion data. This retrieved emotion data is then used to personalize strategic recommendations.

[0395] Step 4:

[0396] The server generates suggestions to support strategic decision-making at the appropriate time and with appropriate content, based on predictive models and sentiment data.

[0397] Step 5:

[0398] The device displays data and suggestions sent from the server via a user interface. The display format is adjusted according to the user's emotional state.

[0399] Step 6:

[0400] Users make decisions based on suggestions presented through their devices. The system provides a more user-friendly operating environment through interface adjustments that reflect the user's emotions.

[0401] Step 7:

[0402] The device optimizes the automation process for routine tasks based on information from the emotion engine. The timing and content of task execution are set according to the user's emotions.

[0403] Step 8:

[0404] The server continuously monitors the system's operational status and updates the system to meet new technological and regulatory requirements.

[0405] Step 9:

[0406] Users can participate in an emotionally conscious training program and learn how to effectively operate the system.

[0407] (Example 2)

[0408] 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".

[0409] For companies to manage environmental risks and achieve sustainable operations, they need to effectively collect and analyze vast amounts of information. However, traditional systems do not consider user emotions, resulting in suggestions that are not optimized for individual users and failing to fully realize the benefits of decision-making. Furthermore, unintuitive user interfaces can lead to user stress and confusion.

[0410] 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.

[0411] In this invention, the server includes means for integrating environmental information using multiple data sets collected from companies, means for removing unnecessary data from the information, and means for extracting trends from the integrated information using an analytical algorithm. This enables personalized strategic proposals that take user emotions into account, leading to more effective decision-making.

[0412] A "business" is a social entity that engages in commercial activities and provides goods and services.

[0413] A "data collection" is a set of information that has been collected and stored for a specific purpose.

[0414] "Environmental information" refers to all information related to the natural environment and human activities.

[0415] "Integration" is the process of combining different pieces of information to form a unified whole.

[0416] "Removal" means removing unnecessary or inaccurate elements.

[0417] A "trend" is a certain direction or sign of change that can be observed as a result of analyzing data.

[0418] "Risk" refers to the possibility of undesirable outcomes occurring.

[0419] A "model" is an abstract or concrete representation used to understand and predict a particular phenomenon or process.

[0420] A "strategic proposal" refers to a planned action plan designed to achieve a specific objective.

[0421] "Display information" refers to data and instructions that are provided to the user visually.

[0422] A "routine operation" is a standard process that is repeatedly performed according to a specific procedure.

[0423] "Emotional state" refers to the temporary psychological feelings a person experiences in a particular situation.

[0424] A "generative AI model" is an artificial intelligence system that has the ability to generate new information and content based on large amounts of data.

[0425] A "prompt" refers to an instruction or question given to a system to prompt it to take a specific response or action.

[0426] "Information means" refers to a set of technologies and methods for collecting, processing, storing, and transmitting information.

[0427] "Usage" refers to the circumstances and conditions under which a system or tool is actually used.

[0428] "Updating" means bringing a system to its latest state by incorporating new data and technologies.

[0429] "Information technology literacy" refers to the ability and skills to understand and utilize information technology.

[0430] A "training program" refers to a learning process designed to acquire specific goals or skills.

[0431] This invention is a system designed to support corporate environmental management. The system aims to provide users with optimal strategic recommendations by integrating and analyzing vast amounts of data collected from both inside and outside the company. Furthermore, the system incorporates an emotion engine, enabling it to provide information that takes into account the user's emotional state.

[0432] The server collects environmental data from corporate databases and external sources. Specifically, it retrieves data using database management systems and APIs, and performs data cleansing using data preparation tools (e.g., OpenRefine). Then, it uses AI analysis software (e.g., TensorFlow) to extract trends and patterns from the data. This allows for the construction of models that predict future environmental risks and generate strategic recommendations.

[0433] The device is responsible for displaying this information through the user interface. The front-end framework used (e.g., React or Vue.js) provides an intuitive and easy-to-understand interface. Furthermore, a sentiment analysis engine analyzes the user's emotions in real time and adjusts the interface accordingly. For example, if the user is stressed, the interface is simplified, and only important information is highlighted.

[0434] Users make decisions for sustainable operations based on this displayed information. The server supports the user experience by using a generative AI model to generate specific prompts. For example, it can generate a prompt such as, "The user is feeling anxious, so restructure the high-priority suggestions into easily understandable language."

[0435] In this way, this system, which incorporates an emotion engine, can support flexible and effective environmental management decision-making optimized for the user's emotions.

[0436] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0437] Step 1:

[0438] The server collects environmental data via internal and external databases and APIs. Input is raw data obtained from various sources, and output is a unified format of this data. Specifically, the server executes queries, collects the necessary data, and then converts that data into a unified format.

[0439] Step 2:

[0440] The server performs data cleansing to organize the integrated data. The input is the data integrated in step 1, and the output is the data from which unnecessary elements have been removed and accuracy has been verified. Specifically, the server uses cleansing tools to remove missing values ​​and duplicates and correct outliers.

[0441] Step 3:

[0442] The server feeds the cleansed data into an AI analysis algorithm for analysis. The input is the cleansed data, and the output is the patterns and trends extracted from the data. Specifically, the server uses analysis software to analyze the data using regression analysis and clustering methods.

[0443] Step 4:

[0444] The server builds a model to predict environmental risks based on the analysis results. The input is the patterns and trends obtained in step 3, and the output is the model for predicting risks. Specifically, the server uses machine learning techniques to simulate future scenarios from past data.

[0445] Step 5:

[0446] The server generates and outputs strategic proposals based on a predictive model. The input is a model for predicting risk, and the output is a strategic proposal presented to the user. Specifically, the server uses a generation AI model to create proposals that reflect the analysis results.

[0447] Step 6:

[0448] The terminal displays strategic proposals sent from the server on its user interface. The input is the strategic proposals generated by the server, and the output is the information visually displayed on the screen. Specifically, the terminal provides the information with an appropriate layout and color scheme.

[0449] Step 7:

[0450] The device analyzes the user's emotional data using an emotion engine and adjusts the interface accordingly. The input is the user's real-time emotional data, and the output is the interface adjustment based on the user's emotions. Specifically, the device runs emotion analysis software and changes the layout and amount of information displayed to match the user's state.

[0451] Step 8:

[0452] The user makes a decision based on the information presented. The input is the strategic proposal displayed on the terminal, and the output is the decision chosen by the user. In terms of specific actions, the user interacts with the interface and expresses their opinion on the proposal.

[0453] Step 9:

[0454] The server collects user decisions and feedback, and uses this information to improve future suggestions. The input is user feedback, and the output is the improved suggestion. Specifically, the server accumulates feedback information and retrains its machine learning model to improve the accuracy of the suggestions.

[0455] (Application Example 2)

[0456] 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 will be referred to as the "terminal."

[0457] In modern urban environments, traffic management is complex and can amplify residents' stress and anxiety. In particular, stress caused by traffic congestion and delays significantly impacts residents' daily lives. Furthermore, corporate environmental management systems often make decisions without considering users' emotional states, resulting in proposals that don't suit the user's situation. Therefore, there is a need for a system that utilizes emotional data to support decision-making, reduce user stress, and manage optimal transportation options.

[0458] 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. In this invention, the server includes means for acquiring data from information sources, means for integrating the acquired data and supplementing or removing missing values ​​or inaccurate data, and means for performing analysis to identify patterns and trends using the integrated data. This enables optimal traffic management and decision support that takes into account the user's emotional data.

[0459] text

[0460] An "information source" refers to the fundamental devices or platforms used to provide data.

[0461] "Data integration" is the process of organizing data obtained from different sources into a single, unified format.

[0462] "Missing values" refer to a state in a dataset where information that should be present is missing.

[0463] "Inaccurate data" refers to data that contains false information or biases, and therefore lacks reliability in analysis.

[0464] "Patterns and trends" are elements that indicate recurring tendencies or movements found in data.

[0465] "Performing analysis" is the process of applying statistical methods and machine learning techniques to data to extract meaningful information.

[0466] A "risk prediction model" is a mathematical or statistical model built to predict potential problems or difficulties in the future.

[0467] "Suggestions to support decision-making" refers to information and advice generated to effectively assist users in making decisions.

[0468] A "user display device" is a digital screen device used to convey information visually.

[0469] "Automating part of a process" means minimizing human intervention and allowing machines or software to independently execute the procedures.

[0470] "Monitoring and updating" means constantly checking the functionality and performance of a system and making improvements to adapt to new technologies and regulations.

[0471] An "educational program" is a series of instructions designed to enhance the user's understanding and technical skills.

[0472] An "emotional processing device" is a system or device that analyzes a user's emotional state and takes appropriate countermeasures.

[0473] "Managing transportation in an urban environment" means optimizing elements related to transportation and movement, and making adjustments and controls to realize efficient urban living.

[0474] The system for implementing this invention supports traffic management decision-making that takes user emotions into consideration.

[0475] The server efficiently acquires data from various sources, integrates that data, and supplements or removes inaccurate values. Based on the integrated data, it analyzes patterns and trends using AI algorithms. Furthermore, it builds a risk prediction model based on this analysis and generates optimal recommendations for the user. To reflect the user's emotional state in these recommendations, it uses an emotion processing device.

[0476] The user's device receives suggestions generated by the server in real time and presents them to the user visually. The user interface reflects the user's current emotional state, and the information is adjusted to be more easily understood. This makes it easier for the user to intuitively accept suggestions such as stress reduction measures and efficient modes of transportation.

[0477] For example, if traffic congestion occurs during the morning commute, the server generates alternative route suggestions and presents them to the user without causing any inconvenience. If the emotion processing unit determines that the user is frustrated with the traffic situation, it automatically adjusts the color scheme and font size to create a more calming environment.

[0478] Example prompt: "Please consider sentiment data when designing an interface for a smart city's traffic management system to reduce resident stress."

[0479] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0480] text

[0481] Step 1:

[0482] The server collects data in real time from various information sources. Inputs include traffic sensor data, weather data, and historical traffic history. This data is stored in a database to obtain output for the next analysis step.

[0483] Step 2:

[0484] The server cleanses the collected data, correcting or removing missing values ​​and inaccurate data. This process transforms the information in the database into accurate and consistent data. As a result, integrated, cleansed data is output.

[0485] Step 3:

[0486] The server applies AI algorithms to cleansed data to identify patterns and trends. This input includes integrated data from a database. The output generates predicted congestion patterns and potential risks.

[0487] Step 4:

[0488] The server builds a model to predict future traffic risks using risk patterns generated by AI. It utilizes identified trend data as input, and the predictive model is completed as output.

[0489] Step 5:

[0490] The server generates suggestions that take into account the user's emotional state, based on a predictive model. The emotion processing unit uses the user's collected emotional data as input and outputs suggestions that optimize the content presented in the user interface.

[0491] Step 6:

[0492] The terminal displays optimized suggestions generated by the server to the user. The user interface visualizes the output information by dynamically adjusting colors and font sizes, reflecting the emotional state input from the user.

[0493] Step 7:

[0494] The user receives information from their device and determines their own transportation strategy. Optimized suggestion information from the device is used as input, and the user's next action is guided as output.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] [Third Embodiment]

[0499] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0500] 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.

[0501] 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).

[0502] 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.

[0503] 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.

[0504] 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).

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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.

[0509] 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.

[0510] 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".

[0511] This invention is an AI-powered environmental consulting system designed to help companies achieve sustainable business operations. This system operates through the collaboration of a server, terminals, and users. The roles and specific operations of each component are described below.

[0512] Server roles and operations

[0513] The server automatically collects environmental data generated daily by businesses and integrates it into a database. This data includes all environmentally relevant information, such as energy consumption, waste management, and resource utilization. The server cleanses the collected data to create an accurate dataset. Next, it analyzes the data using AI algorithms to identify patterns and trends. This makes it possible to build models that predict future environmental risks. Based on the built predictive models, the server generates recommendations to support strategic decision-making for businesses.

[0514] Terminal role and operation

[0515] The terminal displays information provided by the server on the user interface. This allows users to directly view data visualizations and analysis results. The terminal also features an interactive dashboard to support users in comparing different scenarios and considering actions based on specific suggestions. Furthermore, it automates routine tasks, efficiently handling report generation and monitoring settings.

[0516] User roles and actions

[0517] Users make decisions aimed at sustainable business operations based on information displayed via their devices. Users can also input customization requests for the system and provide feedback to the server for solutions tailored to their specific needs. Furthermore, users can improve their AI and system literacy through training programs, enabling them to utilize the system more effectively.

[0518] Specific example

[0519] For example, in the case of a company aiming to optimize energy consumption, the server analyzes past consumption data to identify peak times and factors contributing to high energy consumption. It then generates suggestions for improvements to ensure efficient energy management. The terminal displays these suggestions as interactive graphs, allowing the user to visually compare the effects of each suggestion before making a decision.

[0520] Based on the above, the present invention provides an innovative system that significantly reduces the environmental impact of companies and supports sustainable operations.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] The server collects environmental data from both internal and external data sources within the company. This includes data from smart meters, IoT devices, and existing management systems.

[0524] Step 2:

[0525] The server cleanses the collected data, correcting and removing incomplete or incorrect data. This creates a clean dataset suitable for analysis.

[0526] Step 3:

[0527] The server integrates the cleansed data into the database, making it easily accessible. This enables centralized data management and allows for rapid processing.

[0528] Step 4:

[0529] The server applies AI algorithms to analyze the integrated data and identify patterns and trends. This includes detecting increases and decreases in consumption over time and identifying anomalies.

[0530] Step 5:

[0531] The server builds a predictive model based on the analyzed data. This model predicts future environmental risks and identifies high-risk areas.

[0532] Step 6:

[0533] Based on the information identified by the predictive model, the server generates specific suggestions to support strategic decision-making. These include improvement measures and risk mitigation strategies.

[0534] Step 7:

[0535] The terminal displays suggestions and analysis results received from the server in its user interface. Interactive graphs and dashboards are also provided to make the information easy for users to understand.

[0536] Step 8:

[0537] Based on the information on their device, users evaluate the effectiveness of each proposal and decide on specific actions as needed. Different strategic scenarios can be considered to support the decision-making process.

[0538] Step 9:

[0539] The terminal automates routine tasks and monitoring based on predetermined actions, improving operational efficiency. This automation allows users to focus on higher-value tasks.

[0540] Step 10:

[0541] The server continuously monitors system performance and updates the system based on new technologies and regulations to maintain optimal functionality.

[0542] Step 11:

[0543] Users will participate in training programs on the AI ​​system and learn how to use the system effectively. This will further enhance the system's utilization.

[0544] (Example 1)

[0545] 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."

[0546] In recent years, companies have been required to operate their businesses sustainably, making the reduction of environmental impact a crucial issue. However, companies generate large amounts of environmental data on a daily basis, and effective decision-making based on this data requires a complex process of data collection, analysis, and proposal generation. Therefore, there is a need to develop systems that combine efficient data processing, highly accurate prediction, and automation of business processes.

[0547] 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.

[0548] In this invention, the server includes means for collecting and integrating environmental information from multiple data sources, means for correcting or removing outliers and missing values ​​from the collected data, and means for identifying patterns and trends by utilizing a generative AI model with the cleansed data. This enables companies to automate complex data processing and make highly accurate strategic decisions.

[0549] A "data source" refers to a location or system where various environmental information generated or acquired by a company is stored.

[0550] "Environmental information" refers to all information, including data on energy consumption, waste management, and resource utilization.

[0551] "Data cleansing" refers to the process of detecting outliers and missing values ​​from data and correcting or removing them.

[0552] "Generative AI models" refer to artificial intelligence technologies used to analyze data and predict patterns and trends.

[0553] "Patterns and trends" refer to recurring characteristics derived from past data and information that predicts future changes.

[0554] "Recommendations" refer to information that suggests specific actions or strategies that a company should take based on the analysis results.

[0555] An "interactive interface" refers to a user screen designed to allow users to intuitively operate and obtain information.

[0556] "Business process automation" refers to the process of automatically performing some tasks that were previously done manually using software or machines.

[0557] "Technological updates and compliance with new regulations" refers to the adjustments and adaptations made to ensure that a system continues to keep up with the latest technologies and changes in laws and regulations.

[0558] A "training program" refers to a program provided to users to learn how to operate the system and acquire related knowledge.

[0559] This invention is an environmental consulting system designed to support the sustainable operation of businesses. The system operates with the coordinated cooperation of its server, terminal, and user components.

[0560] The server automatically collects and integrates environmental information from various data sources within the enterprise. Hardware used includes cloud servers and local servers, enabling real-time processing of large amounts of data. Software includes database management systems (DBMS) and machine learning algorithms. The data is cleansed, correcting or removing outliers and missing values. The server then uses this cleansed data to leverage generative AI models to identify patterns and trends. This allows enterprises to predict future environmental risks and proactively develop countermeasures.

[0561] The terminal displays analysis results and suggestions provided by the server on its user interface. The hardware used is a personal computer or tablet, and the software includes a dedicated dashboard application. The interactive dashboard allows users to visually review, compare, and select proposed strategies. The terminal also includes features to automate business processes, such as automating the creation of periodic reports.

[0562] Users play a role in implementing sustainable strategies based on the information provided on their devices. For example, they can select and implement specific measures to reduce peak energy consumption. Users can also provide feedback to the system and request customizations as needed. Furthermore, users can improve their knowledge and skills to make the most of the system's capabilities by participating in training programs on system usage.

[0563] For example, if a company aims to improve energy efficiency, the server analyzes historical energy consumption data to identify peak consumption times. Based on this, it generates specific suggestions for efficient energy use and displays them on the user's device. The user can then adjust their energy strategy based on these suggestions.

[0564] An example of a prompt message would be, "Analyze energy consumption data from the past three months, identify peak times, and propose the optimal energy management strategy."

[0565] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0566] Step 1:

[0567] The server collects environmental information from various data sources within the company. Inputs include raw data on energy consumption and resource use. The server retrieves this data and integrates it into a centrally managed database on the cloud. Specifically, it periodically retrieves data from APIs and sensors and saves it to the database. The output is the integrated raw dataset.

[0568] Step 2:

[0569] The server performs data cleansing on the collected raw data. The input includes an integrated raw dataset. The server detects outliers and processes the data to correct or remove them. This results in a cleansed dataset. Specific operations include imputing missing data and smoothing spikes in data. The output is the cleansed data from which anomalies have been removed.

[0570] Step 3:

[0571] The server inputs cleansed data into a generating AI model and performs data analysis. The input is a cleaned dataset. The server performs data calculations to predict future trends by identifying patterns and trends in the AI ​​model. Specifically, this includes training and evaluating a predictive model using machine learning algorithms. The output is a model that predicts future environmental risks.

[0572] Step 4:

[0573] The server generates strategic recommendations based on a predictive model. The input includes the predictive model. The server analyzes this model and generates recommendations for environmental management and energy efficiency tailored to the company. Specifically, the process involves creating concrete action plans based on the insights gained from the model. The output is a list of strategic recommendations.

[0574] Step 5:

[0575] The terminal displays proposals sent from the server in its user interface. Input includes a generated list of strategic proposals. The terminal uses an interactive dashboard to visualize the proposals, making them easy for the user to understand. Specific actions include displaying proposals as charts and graphs to support user decision-making. Output is a visualized report that the user can view.

[0576] Step 6:

[0577] Users make decisions for sustainable operations based on the displayed suggestions. They analyze the provided information and decide on actions that meet their specific needs. These actions include considering the suggestions, developing a feasible action plan, and implementing it. The output is the determined sustainability strategy.

[0578] (Application Example 1)

[0579] 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."

[0580] In recent years, with the advancement of urbanization, optimizing energy consumption and reducing environmental impact have become crucial issues. However, individual citizens lack access to specific information regarding energy use and environmental management, making it difficult for them to take appropriate action. Therefore, real-time data visualization and concrete proposals to citizens are needed to enhance the sustainability of the entire city.

[0581] 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.

[0582] In this invention, the server includes means for collecting environmental data from multiple data sources related to a population, means for integrating the collected environmental data and supplementing or removing missing or inaccurate data, and means for performing analysis to identify trends and patterns using the integrated data. This makes it possible to visualize energy consumption and environmental management at the city level for citizens and provide them with specific information and suggestions necessary to take sustainable actions.

[0583] A "group" is a unit to which multiple individuals or organizations belong, such as a society or an organization, and is a collection of members who share a common purpose or challenge.

[0584] A "data source" is the origin of information and data, and the source of information needed by a system to collect the data it requires.

[0585] "Environmental data" refers to various types of information related to the environment, such as energy consumption, waste management, and resource utilization, and is data necessary for analysis toward realizing a sustainable society.

[0586] An "information display device" is a device that provides information to a user visually, and includes monitors, displays, tablets, and other similar devices.

[0587] A "user" is an individual or organization that uses the system to obtain information and receive suggestions.

[0588] "Technological understanding" refers to the ability to deepen one's knowledge and awareness of a particular technology and to use it more effectively.

[0589] A "training program" is an educational curriculum designed to teach technology and knowledge and improve skills.

[0590] A "trend" is a concept that indicates a certain direction or pattern observed in a series of data.

[0591] A "trend" refers to the direction of change or development observed over a specific period of time.

[0592] To realize this invention, a system is constructed in which the server, terminal, and user each play a specific role.

[0593] The server collects environmental data from multiple sources related to society and organizations. The collected data is first integrated, and missing or inaccurate data is either filled in or removed. Next, analysis is performed to identify trends and patterns based on the integrated data. AI algorithms are used for this analysis. The server uses the results of this analysis to build models that predict future environmental risks and generate suggestions to support strategic decision-making.

[0594] The terminal visually displays information provided by the server on its interface. This allows users to check environmental data and analysis results in real time. The data is visualized and displayed in an interactive format, allowing users to examine items of interest in detail and compare various scenarios. The terminal also has functions for automated report generation and monitoring settings.

[0595] Users plan and execute sustainable actions based on information obtained from their devices. By implementing specific recommended actions, they contribute to improving the management of energy consumption and environmental impact across the city. Users can also contribute to system improvements by providing feedback to the server with customization requests.

[0596] For example, consider a citizen who uses a smartphone app to check the times of day when electricity consumption is minimized on holidays. By following the app's suggestions, this citizen can adjust their laundry and cooking times to optimize energy consumption. Such support can help reduce peak energy consumption across the entire city and contribute to sustainable operations.

[0597] An example of a prompt for a generated AI model is: "Please suggest an app design to visualize energy consumption in a smart city. How can we enable citizens to view data in real time and take concrete energy-saving actions?"

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1:

[0600] The server collects environmental data from multiple data sources related to society and organizations. The input in this step is environmental data such as energy consumption, waste management, and resource use, while the output is raw data before integration. Specifically, it acquires data from sensors and external databases and prepares it for data cleansing.

[0601] Step 2:

[0602] The server cleanses the collected raw data, imputing or removing missing or inaccurate values. The input is raw environmental data, and the output is clean, prepared data. Specifically, this involves imputation using statistical methods and filtering of inconsistent data.

[0603] Step 3:

[0604] The server utilizes the cleansed data and performs analysis using AI algorithms to identify trends and patterns. The input is clean data, and the output is analyzed trend data. In this step, the data is fed into a machine learning model, and pattern analysis is performed based on the time history.

[0605] Step 4:

[0606] The server builds a model to predict future environmental risks based on the analysis results. The input is trend data, and the output is the predictive model. Specifically, it trains an AI model and generates future risk scenarios using a risk assessment algorithm.

[0607] Step 5:

[0608] The server generates proposals to support strategic decision-making from the built predictive model. The input is the predictive model, and the output is specific proposals. The generated AI model is used to output risk mitigation measures for each scenario.

[0609] Step 6:

[0610] The terminal visually displays suggestions retrieved from the server on its interface. The input is the specific suggestion content, and the output is a visualized information presentation screen. Specifically, it displays interactive graphs and charts to organize information in a way that is easily understandable to the user.

[0611] Step 7:

[0612] The user plans and executes sustainable actions based on the information presented on the device. The input is the suggested information displayed on the device, and the output is an action plan based on that information. For example, the user might select time periods to reduce electricity consumption based on the suggestions and adjust individual activities accordingly.

[0613] 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.

[0614] This invention combines an emotion engine with an enterprise's environmental management support system to assist in effective decision-making that responds to user emotions. The system operates through server, terminal, and user interaction.

[0615] Server roles and operations

[0616] The server collects and integrates environmental data from a company's diverse data sources and performs data cleansing. Furthermore, it analyzes the data using AI algorithms to identify patterns and trends. Based on this, it builds predictive models and generates strategic recommendations for environmental risks. It also incorporates an emotion engine, incorporating user sentiment data into the analysis to personalize recommendations and enable more user-oriented strategic suggestions.

[0617] Terminal role and operation

[0618] The terminal is responsible for displaying information sent from the server on the user interface. In addition to normal data display functions, it adjusts the interface according to the user's emotional state based on information from the emotion engine, making the system intuitive for the user. Furthermore, when automating routine tasks, it can select the optimal timing and format by taking emotional data into consideration.

[0619] User roles and actions

[0620] Users make decisions for sustainable business operations based on data and suggestions displayed through their devices. As users interact with the system, the emotion engine instantly recognizes emotional data, and the priority and content of suggestions are automatically adjusted accordingly. Furthermore, participating in training programs that reflect emotional information contributes to improved system utilization.

[0621] Specific example

[0622] For example, when a user is presented with a proposal for a new environmental strategy, the emotion engine may detect the user's distress. In this case, the server uses the engine's output to restructure the proposal in a more understandable way and add information to aid comprehension. The terminal then simplifies the interface on the spot and displays information to reduce the user's burden. As a result, the user can make a decision that is emotionally acceptable.

[0623] Based on the above, the present invention, which combines an emotion engine, provides a system that can support flexible and effective environmental management decision-making while taking into account the user's emotions.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The server collects and integrates environmental data from internal and external data sources within the company. After data collection, it performs data cleansing to prepare a dataset suitable for analysis.

[0627] Step 2:

[0628] The server applies AI algorithms to analyze data and identify patterns and trends. This allows it to build models for predicting future environmental risks.

[0629] Step 3:

[0630] The server uses an emotion engine to retrieve user emotion data. This retrieved emotion data is then used to personalize strategic recommendations.

[0631] Step 4:

[0632] The server generates suggestions to support strategic decision-making at the appropriate time and with appropriate content, based on predictive models and sentiment data.

[0633] Step 5:

[0634] The device displays data and suggestions sent from the server via a user interface. The display format is adjusted according to the user's emotional state.

[0635] Step 6:

[0636] Users make decisions based on suggestions presented through their devices. The system provides a more user-friendly operating environment through interface adjustments that reflect the user's emotions.

[0637] Step 7:

[0638] The device optimizes the automation process for routine tasks based on information from the emotion engine. The timing and content of task execution are set according to the user's emotions.

[0639] Step 8:

[0640] The server continuously monitors the system's operational status and updates the system to meet new technological and regulatory requirements.

[0641] Step 9:

[0642] Users can participate in an emotionally conscious training program and learn how to effectively operate the system.

[0643] (Example 2)

[0644] 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."

[0645] For companies to manage environmental risks and achieve sustainable operations, they need to effectively collect and analyze vast amounts of information. However, traditional systems do not consider user emotions, resulting in suggestions that are not optimized for individual users and failing to fully realize the benefits of decision-making. Furthermore, unintuitive user interfaces can lead to user stress and confusion.

[0646] 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.

[0647] In this invention, the server includes means for integrating environmental information using multiple data sets collected from companies, means for removing unnecessary data from the information, and means for extracting trends from the integrated information using an analytical algorithm. This enables personalized strategic proposals that take user emotions into account, leading to more effective decision-making.

[0648] A "business" is a social entity that engages in commercial activities and provides goods and services.

[0649] A "data collection" is a set of information that has been collected and stored for a specific purpose.

[0650] "Environmental information" refers to all information related to the natural environment and human activities.

[0651] "Integration" is the process of combining different pieces of information to form a unified whole.

[0652] "Removal" means removing unnecessary or inaccurate elements.

[0653] A "trend" is a certain direction or sign of change that can be observed as a result of analyzing data.

[0654] "Risk" refers to the possibility of undesirable outcomes occurring.

[0655] A "model" is an abstract or concrete representation used to understand and predict a particular phenomenon or process.

[0656] A "strategic proposal" refers to a planned action plan designed to achieve a specific objective.

[0657] "Display information" refers to data and instructions that are provided to the user visually.

[0658] A "routine operation" is a standard process that is repeatedly performed according to a specific procedure.

[0659] "Emotional state" refers to the temporary psychological feelings a person experiences in a particular situation.

[0660] A "generative AI model" is an artificial intelligence system that has the ability to generate new information and content based on large amounts of data.

[0661] A "prompt" refers to an instruction or question given to a system to prompt it to take a specific response or action.

[0662] "Information means" refers to a set of technologies and methods for collecting, processing, storing, and transmitting information.

[0663] "Usage" refers to the circumstances and conditions under which a system or tool is actually used.

[0664] "Updating" means bringing a system to its latest state by incorporating new data and technologies.

[0665] "Information technology literacy" refers to the ability and skills to understand and utilize information technology.

[0666] A "training program" refers to a learning process designed to acquire specific goals or skills.

[0667] This invention is a system designed to support corporate environmental management. The system aims to provide users with optimal strategic recommendations by integrating and analyzing vast amounts of data collected from both inside and outside the company. Furthermore, the system incorporates an emotion engine, enabling it to provide information that takes into account the user's emotional state.

[0668] The server collects environmental data from corporate databases and external sources. Specifically, it retrieves data using database management systems and APIs, and performs data cleansing using data preparation tools (e.g., OpenRefine). Then, it uses AI analysis software (e.g., TensorFlow) to extract trends and patterns from the data. This allows for the construction of models that predict future environmental risks and generate strategic recommendations.

[0669] The device is responsible for displaying this information through the user interface. The front-end framework used (e.g., React or Vue.js) provides an intuitive and easy-to-understand interface. Furthermore, a sentiment analysis engine analyzes the user's emotions in real time and adjusts the interface accordingly. For example, if the user is stressed, the interface is simplified, and only important information is highlighted.

[0670] Users make decisions for sustainable operations based on this displayed information. The server supports the user experience by using a generative AI model to generate specific prompts. For example, it can generate a prompt such as, "The user is feeling anxious, so restructure the high-priority suggestions into easily understandable language."

[0671] In this way, this system, which incorporates an emotion engine, can support flexible and effective environmental management decision-making optimized for the user's emotions.

[0672] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0673] Step 1:

[0674] The server collects environmental data via internal and external databases and APIs. Input is raw data obtained from various sources, and output is a unified format of this data. Specifically, the server executes queries, collects the necessary data, and then converts that data into a unified format.

[0675] Step 2:

[0676] The server performs data cleansing to organize the integrated data. The input is the data integrated in step 1, and the output is the data from which unnecessary elements have been removed and accuracy has been verified. Specifically, the server uses cleansing tools to remove missing values ​​and duplicates and correct outliers.

[0677] Step 3:

[0678] The server feeds the cleansed data into an AI analysis algorithm for analysis. The input is the cleansed data, and the output is the patterns and trends extracted from the data. Specifically, the server uses analysis software to analyze the data using regression analysis and clustering methods.

[0679] Step 4:

[0680] The server builds a model to predict environmental risks based on the analysis results. The input is the patterns and trends obtained in step 3, and the output is the model for predicting risks. Specifically, the server uses machine learning techniques to simulate future scenarios from past data.

[0681] Step 5:

[0682] The server generates and outputs strategic proposals based on a predictive model. The input is a model for predicting risk, and the output is a strategic proposal presented to the user. Specifically, the server uses a generation AI model to create proposals that reflect the analysis results.

[0683] Step 6:

[0684] The terminal displays strategic proposals sent from the server on its user interface. The input is the strategic proposals generated by the server, and the output is the information visually displayed on the screen. Specifically, the terminal provides the information with an appropriate layout and color scheme.

[0685] Step 7:

[0686] The device analyzes the user's emotional data using an emotion engine and adjusts the interface accordingly. The input is the user's real-time emotional data, and the output is the interface adjustment based on the user's emotions. Specifically, the device runs emotion analysis software and changes the layout and amount of information displayed to match the user's state.

[0687] Step 8:

[0688] The user makes a decision based on the information presented. The input is the strategic proposal displayed on the terminal, and the output is the decision chosen by the user. In terms of specific actions, the user interacts with the interface and expresses their opinion on the proposal.

[0689] Step 9:

[0690] The server collects user decisions and feedback, and uses this information to improve future suggestions. The input is user feedback, and the output is the improved suggestion. Specifically, the server accumulates feedback information and retrains its machine learning model to improve the accuracy of the suggestions.

[0691] (Application Example 2)

[0692] 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."

[0693] In modern urban environments, traffic management is complex and can amplify residents' stress and anxiety. In particular, stress caused by traffic congestion and delays significantly impacts residents' daily lives. Furthermore, corporate environmental management systems often make decisions without considering users' emotional states, resulting in proposals that don't suit the user's situation. Therefore, there is a need for a system that utilizes emotional data to support decision-making, reduce user stress, and manage optimal transportation options.

[0694] 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. In this invention, the server includes means for acquiring data from information sources, means for integrating the acquired data and supplementing or removing missing values ​​or inaccurate data, and means for performing analysis to identify patterns and trends using the integrated data. This enables optimal traffic management and decision support that takes into account the user's emotional data.

[0695] text

[0696] An "information source" refers to the fundamental devices or platforms used to provide data.

[0697] "Data integration" is the process of organizing data obtained from different sources into a single, unified format.

[0698] "Missing values" refer to a state in a dataset where information that should be present is missing.

[0699] "Inaccurate data" refers to data that contains false information or biases, and therefore lacks reliability in analysis.

[0700] "Patterns and trends" are elements that indicate recurring tendencies or movements found in data.

[0701] "Performing analysis" is the process of applying statistical methods and machine learning techniques to data to extract meaningful information.

[0702] A "risk prediction model" is a mathematical or statistical model built to predict potential problems or difficulties in the future.

[0703] "Suggestions to support decision-making" refers to information and advice generated to effectively assist users in making decisions.

[0704] A "user display device" is a digital screen device used to convey information visually.

[0705] "Automating part of a process" means minimizing human intervention and allowing machines or software to independently execute the procedures.

[0706] "Monitoring and updating" means constantly checking the functionality and performance of a system and making improvements to adapt to new technologies and regulations.

[0707] An "educational program" is a series of instructions designed to enhance the user's understanding and technical skills.

[0708] An "emotional processing device" is a system or device that analyzes a user's emotional state and takes appropriate countermeasures.

[0709] "Managing transportation in an urban environment" means optimizing elements related to transportation and movement, and making adjustments and controls to realize efficient urban living.

[0710] The system for implementing this invention supports traffic management decision-making that takes user emotions into consideration.

[0711] The server efficiently acquires data from various sources, integrates that data, and supplements or removes inaccurate values. Based on the integrated data, it analyzes patterns and trends using AI algorithms. Furthermore, it builds a risk prediction model based on this analysis and generates optimal recommendations for the user. To reflect the user's emotional state in these recommendations, it uses an emotion processing device.

[0712] The user's device receives suggestions generated by the server in real time and presents them to the user visually. The user interface reflects the user's current emotional state, and the information is adjusted to be more easily understood. This makes it easier for the user to intuitively accept suggestions such as stress reduction measures and efficient modes of transportation.

[0713] For example, if traffic congestion occurs during the morning commute, the server generates alternative route suggestions and presents them to the user without causing any inconvenience. If the emotion processing unit determines that the user is frustrated with the traffic situation, it automatically adjusts the color scheme and font size to create a more calming environment.

[0714] Example prompt: "Please consider sentiment data when designing an interface for a smart city's traffic management system to reduce resident stress."

[0715] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0716] text

[0717] Step 1:

[0718] The server collects data in real time from various information sources. Inputs include traffic sensor data, weather data, and historical traffic history. This data is stored in a database to obtain output for the next analysis step.

[0719] Step 2:

[0720] The server cleanses the collected data, correcting or removing missing values ​​and inaccurate data. This process transforms the information in the database into accurate and consistent data. As a result, integrated, cleansed data is output.

[0721] Step 3:

[0722] The server applies AI algorithms to cleansed data to identify patterns and trends. This input includes integrated data from a database. The output generates predicted congestion patterns and potential risks.

[0723] Step 4:

[0724] The server builds a model to predict future traffic risks using risk patterns generated by AI. It utilizes identified trend data as input, and the predictive model is completed as output.

[0725] Step 5:

[0726] The server generates suggestions that take into account the user's emotional state, based on a predictive model. The emotion processing unit uses the user's collected emotional data as input and outputs suggestions that optimize the content presented in the user interface.

[0727] Step 6:

[0728] The terminal displays optimized suggestions generated by the server to the user. The user interface visualizes the output information by dynamically adjusting colors and font sizes, reflecting the emotional state input from the user.

[0729] Step 7:

[0730] The user receives information from their device and determines their own transportation strategy. Optimized suggestion information from the device is used as input, and the user's next action is guided as output.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] [Fourth Embodiment]

[0735] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0736] 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.

[0737] 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).

[0738] 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.

[0739] 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.

[0740] 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).

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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".

[0748] This invention is an AI-powered environmental consulting system designed to help companies achieve sustainable business operations. This system operates through the collaboration of a server, terminals, and users. The roles and specific operations of each component are described below.

[0749] Server roles and operations

[0750] The server automatically collects environmental data generated daily by businesses and integrates it into a database. This data includes all environmentally relevant information, such as energy consumption, waste management, and resource utilization. The server cleanses the collected data to create an accurate dataset. Next, it analyzes the data using AI algorithms to identify patterns and trends. This makes it possible to build models that predict future environmental risks. Based on the built predictive models, the server generates recommendations to support strategic decision-making for businesses.

[0751] Terminal role and operation

[0752] The terminal displays information provided by the server on the user interface. This allows users to directly view data visualizations and analysis results. The terminal also features an interactive dashboard to support users in comparing different scenarios and considering actions based on specific suggestions. Furthermore, it automates routine tasks, efficiently handling report generation and monitoring settings.

[0753] User roles and actions

[0754] Users make decisions aimed at sustainable business operations based on information displayed via their devices. Users can also input customization requests for the system and provide feedback to the server for solutions tailored to their specific needs. Furthermore, users can improve their AI and system literacy through training programs, enabling them to utilize the system more effectively.

[0755] Specific example

[0756] For example, in the case of a company aiming to optimize energy consumption, the server analyzes past consumption data to identify peak times and factors contributing to high energy consumption. It then generates suggestions for improvements to ensure efficient energy management. The terminal displays these suggestions as interactive graphs, allowing the user to visually compare the effects of each suggestion before making a decision.

[0757] Based on the above, the present invention provides an innovative system that significantly reduces the environmental impact of companies and supports sustainable operations.

[0758] The following describes the processing flow.

[0759] Step 1:

[0760] The server collects environmental data from both internal and external data sources within the company. This includes data from smart meters, IoT devices, and existing management systems.

[0761] Step 2:

[0762] The server cleanses the collected data, correcting and removing incomplete or incorrect data. This creates a clean dataset suitable for analysis.

[0763] Step 3:

[0764] The server integrates the cleansed data into the database, making it easily accessible. This enables centralized data management and allows for rapid processing.

[0765] Step 4:

[0766] The server applies AI algorithms to analyze the integrated data and identify patterns and trends. This includes detecting increases and decreases in consumption over time and identifying anomalies.

[0767] Step 5:

[0768] The server builds a predictive model based on the analyzed data. This model predicts future environmental risks and identifies high-risk areas.

[0769] Step 6:

[0770] Based on the information identified by the predictive model, the server generates specific suggestions to support strategic decision-making. These include improvement measures and risk mitigation strategies.

[0771] Step 7:

[0772] The terminal displays suggestions and analysis results received from the server in its user interface. Interactive graphs and dashboards are also provided to make the information easy for users to understand.

[0773] Step 8:

[0774] Based on the information on their device, users evaluate the effectiveness of each proposal and decide on specific actions as needed. Different strategic scenarios can be considered to support the decision-making process.

[0775] Step 9:

[0776] The terminal automates routine tasks and monitoring based on predetermined actions, improving operational efficiency. This automation allows users to focus on higher-value tasks.

[0777] Step 10:

[0778] The server continuously monitors system performance and updates the system based on new technologies and regulations to maintain optimal functionality.

[0779] Step 11:

[0780] Users will participate in training programs on the AI ​​system and learn how to use the system effectively. This will further enhance the system's utilization.

[0781] (Example 1)

[0782] 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".

[0783] In recent years, companies have been required to operate their businesses sustainably, making the reduction of environmental impact a crucial issue. However, companies generate large amounts of environmental data on a daily basis, and effective decision-making based on this data requires a complex process of data collection, analysis, and proposal generation. Therefore, there is a need to develop systems that combine efficient data processing, highly accurate prediction, and automation of business processes.

[0784] 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.

[0785] In this invention, the server includes means for collecting and integrating environmental information from multiple data sources, means for correcting or removing outliers and missing values ​​from the collected data, and means for identifying patterns and trends by utilizing a generative AI model with the cleansed data. This enables companies to automate complex data processing and make highly accurate strategic decisions.

[0786] A "data source" refers to a location or system where various environmental information generated or acquired by a company is stored.

[0787] "Environmental information" refers to all information, including data on energy consumption, waste management, and resource utilization.

[0788] "Data cleansing" refers to the process of detecting outliers and missing values ​​from data and correcting or removing them.

[0789] "Generative AI models" refer to artificial intelligence technologies used to analyze data and predict patterns and trends.

[0790] "Patterns and trends" refer to recurring characteristics derived from past data and information that predicts future changes.

[0791] "Recommendations" refer to information that suggests specific actions or strategies that a company should take based on the analysis results.

[0792] An "interactive interface" refers to a user screen designed to allow users to intuitively operate and obtain information.

[0793] "Business process automation" refers to the process of automatically performing some tasks that were previously done manually using software or machines.

[0794] "Technological updates and compliance with new regulations" refers to the adjustments and adaptations made to ensure that a system continues to keep up with the latest technologies and changes in laws and regulations.

[0795] A "training program" refers to a program provided to users to learn how to operate the system and acquire related knowledge.

[0796] This invention is an environmental consulting system designed to support the sustainable operation of businesses. The system operates with the coordinated cooperation of its server, terminal, and user components.

[0797] The server automatically collects and integrates environmental information from various data sources within the enterprise. Hardware used includes cloud servers and local servers, enabling real-time processing of large amounts of data. Software includes database management systems (DBMS) and machine learning algorithms. The data is cleansed, correcting or removing outliers and missing values. The server then uses this cleansed data to leverage generative AI models to identify patterns and trends. This allows enterprises to predict future environmental risks and proactively develop countermeasures.

[0798] The terminal displays analysis results and suggestions provided by the server on its user interface. The hardware used is a personal computer or tablet, and the software includes a dedicated dashboard application. The interactive dashboard allows users to visually review, compare, and select proposed strategies. The terminal also includes features to automate business processes, such as automating the creation of periodic reports.

[0799] Users play a role in implementing sustainable strategies based on the information provided on their devices. For example, they can select and implement specific measures to reduce peak energy consumption. Users can also provide feedback to the system and request customizations as needed. Furthermore, users can improve their knowledge and skills to make the most of the system's capabilities by participating in training programs on system usage.

[0800] For example, if a company aims to improve energy efficiency, the server analyzes historical energy consumption data to identify peak consumption times. Based on this, it generates specific suggestions for efficient energy use and displays them on the user's device. The user can then adjust their energy strategy based on these suggestions.

[0801] An example of a prompt message would be, "Analyze energy consumption data from the past three months, identify peak times, and propose the optimal energy management strategy."

[0802] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0803] Step 1:

[0804] The server collects environmental information from various data sources within the company. Inputs include raw data on energy consumption and resource use. The server retrieves this data and integrates it into a centrally managed database on the cloud. Specifically, it periodically retrieves data from APIs and sensors and saves it to the database. The output is the integrated raw dataset.

[0805] Step 2:

[0806] The server performs data cleansing on the collected raw data. The input includes an integrated raw dataset. The server detects outliers and processes the data to correct or remove them. This results in a cleansed dataset. Specific operations include imputing missing data and smoothing spikes in data. The output is the cleansed data from which anomalies have been removed.

[0807] Step 3:

[0808] The server inputs cleansed data into a generating AI model and performs data analysis. The input is a cleaned dataset. The server performs data calculations to predict future trends by identifying patterns and trends in the AI ​​model. Specifically, this includes training and evaluating a predictive model using machine learning algorithms. The output is a model that predicts future environmental risks.

[0809] Step 4:

[0810] The server generates strategic recommendations based on a predictive model. The input includes the predictive model. The server analyzes this model and generates recommendations for environmental management and energy efficiency tailored to the company. Specifically, the process involves creating concrete action plans based on the insights gained from the model. The output is a list of strategic recommendations.

[0811] Step 5:

[0812] The terminal displays proposals sent from the server in its user interface. Input includes a generated list of strategic proposals. The terminal uses an interactive dashboard to visualize the proposals, making them easy for the user to understand. Specific actions include displaying proposals as charts and graphs to support user decision-making. Output is a visualized report that the user can view.

[0813] Step 6:

[0814] Users make decisions for sustainable operations based on the displayed suggestions. They analyze the provided information and decide on actions that meet their specific needs. These actions include considering the suggestions, developing a feasible action plan, and implementing it. The output is the determined sustainability strategy.

[0815] (Application Example 1)

[0816] 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".

[0817] In recent years, with the advancement of urbanization, optimizing energy consumption and reducing environmental impact have become crucial issues. However, individual citizens lack access to specific information regarding energy use and environmental management, making it difficult for them to take appropriate action. Therefore, real-time data visualization and concrete proposals to citizens are needed to enhance the sustainability of the entire city.

[0818] 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.

[0819] In this invention, the server includes means for collecting environmental data from multiple data sources related to a population, means for integrating the collected environmental data and supplementing or removing missing or inaccurate data, and means for performing analysis to identify trends and patterns using the integrated data. This makes it possible to visualize energy consumption and environmental management at the city level for citizens and provide them with specific information and suggestions necessary to take sustainable actions.

[0820] A "group" is a unit to which multiple individuals or organizations belong, such as a society or an organization, and is a collection of members who share a common purpose or challenge.

[0821] A "data source" is the origin of information and data, and the source of information needed by a system to collect the data it requires.

[0822] "Environmental data" refers to various types of information related to the environment, such as energy consumption, waste management, and resource utilization, and is data necessary for analysis toward realizing a sustainable society.

[0823] An "information display device" is a device that provides information to a user visually, and includes monitors, displays, tablets, and other similar devices.

[0824] A "user" is an individual or organization that uses the system to obtain information and receive suggestions.

[0825] "Technological understanding" refers to the ability to deepen one's knowledge and awareness of a particular technology and to use it more effectively.

[0826] A "training program" is an educational curriculum designed to teach technology and knowledge and improve skills.

[0827] A "trend" is a concept that indicates a certain direction or pattern observed in a series of data.

[0828] A "trend" refers to the direction of change or development observed over a specific period of time.

[0829] To realize this invention, a system is constructed in which the server, terminal, and user each play a specific role.

[0830] The server collects environmental data from multiple sources related to society and organizations. The collected data is first integrated, and missing or inaccurate data is either filled in or removed. Next, analysis is performed to identify trends and patterns based on the integrated data. AI algorithms are used for this analysis. The server uses the results of this analysis to build models that predict future environmental risks and generate suggestions to support strategic decision-making.

[0831] The terminal visually displays information provided by the server on its interface. This allows users to check environmental data and analysis results in real time. The data is visualized and displayed in an interactive format, allowing users to examine items of interest in detail and compare various scenarios. The terminal also has functions for automated report generation and monitoring settings.

[0832] Users plan and execute sustainable actions based on information obtained from their devices. By implementing specific recommended actions, they contribute to improving the management of energy consumption and environmental impact across the city. Users can also contribute to system improvements by providing feedback to the server with customization requests.

[0833] For example, consider a citizen who uses a smartphone app to check the times of day when electricity consumption is minimized on holidays. By following the app's suggestions, this citizen can adjust their laundry and cooking times to optimize energy consumption. Such support can help reduce peak energy consumption across the entire city and contribute to sustainable operations.

[0834] An example of a prompt for a generated AI model is: "Please suggest an app design to visualize energy consumption in a smart city. How can we enable citizens to view data in real time and take concrete energy-saving actions?"

[0835] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0836] Step 1:

[0837] The server collects environmental data from multiple data sources related to society and organizations. The input in this step is environmental data such as energy consumption, waste management, and resource use, while the output is raw data before integration. Specifically, it acquires data from sensors and external databases and prepares it for data cleansing.

[0838] Step 2:

[0839] The server cleanses the collected raw data, imputing or removing missing or inaccurate values. The input is raw environmental data, and the output is clean, prepared data. Specifically, this involves imputation using statistical methods and filtering of inconsistent data.

[0840] Step 3:

[0841] The server utilizes the cleansed data and performs analysis using AI algorithms to identify trends and patterns. The input is clean data, and the output is analyzed trend data. In this step, the data is fed into a machine learning model, and pattern analysis is performed based on the time history.

[0842] Step 4:

[0843] The server builds a model to predict future environmental risks based on the analysis results. The input is trend data, and the output is the predictive model. Specifically, it trains an AI model and generates future risk scenarios using a risk assessment algorithm.

[0844] Step 5:

[0845] The server generates proposals to support strategic decision-making from the built predictive model. The input is the predictive model, and the output is specific proposals. The generated AI model is used to output risk mitigation measures for each scenario.

[0846] Step 6:

[0847] The terminal visually displays suggestions retrieved from the server on its interface. The input is the specific suggestion content, and the output is a visualized information presentation screen. Specifically, it displays interactive graphs and charts to organize information in a way that is easily understandable to the user.

[0848] Step 7:

[0849] The user plans and executes sustainable actions based on the information presented on the device. The input is the suggested information displayed on the device, and the output is an action plan based on that information. For example, the user might select time periods to reduce electricity consumption based on the suggestions and adjust individual activities accordingly.

[0850] 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.

[0851] This invention combines an emotion engine with an enterprise's environmental management support system to assist in effective decision-making that responds to user emotions. The system operates through server, terminal, and user interaction.

[0852] Server roles and operations

[0853] The server collects and integrates environmental data from a company's diverse data sources and performs data cleansing. Furthermore, it analyzes the data using AI algorithms to identify patterns and trends. Based on this, it builds predictive models and generates strategic recommendations for environmental risks. It also incorporates an emotion engine, incorporating user sentiment data into the analysis to personalize recommendations and enable more user-oriented strategic suggestions.

[0854] Terminal role and operation

[0855] The terminal is responsible for displaying information sent from the server on the user interface. In addition to normal data display functions, it adjusts the interface according to the user's emotional state based on information from the emotion engine, making the system intuitive for the user. Furthermore, when automating routine tasks, it can select the optimal timing and format by taking emotional data into consideration.

[0856] User roles and actions

[0857] Users make decisions for sustainable business operations based on data and suggestions displayed through their devices. As users interact with the system, the emotion engine instantly recognizes emotional data, and the priority and content of suggestions are automatically adjusted accordingly. Furthermore, participating in training programs that reflect emotional information contributes to improved system utilization.

[0858] Specific example

[0859] For example, when a user is presented with a proposal for a new environmental strategy, the emotion engine may detect the user's distress. In this case, the server uses the engine's output to restructure the proposal in a more understandable way and add information to aid comprehension. The terminal then simplifies the interface on the spot and displays information to reduce the user's burden. As a result, the user can make a decision that is emotionally acceptable.

[0860] Based on the above, the present invention, which combines an emotion engine, provides a system that can support flexible and effective environmental management decision-making while taking into account the user's emotions.

[0861] The following describes the processing flow.

[0862] Step 1:

[0863] The server collects and integrates environmental data from internal and external data sources within the company. After data collection, it performs data cleansing to prepare a dataset suitable for analysis.

[0864] Step 2:

[0865] The server applies AI algorithms to analyze data and identify patterns and trends. This allows it to build models for predicting future environmental risks.

[0866] Step 3:

[0867] The server uses an emotion engine to retrieve user emotion data. This retrieved emotion data is then used to personalize strategic recommendations.

[0868] Step 4:

[0869] The server generates suggestions to support strategic decision-making at the appropriate time and with appropriate content, based on predictive models and sentiment data.

[0870] Step 5:

[0871] The device displays data and suggestions sent from the server via a user interface. The display format is adjusted according to the user's emotional state.

[0872] Step 6:

[0873] Users make decisions based on suggestions presented through their devices. The system provides a more user-friendly operating environment through interface adjustments that reflect the user's emotions.

[0874] Step 7:

[0875] The device optimizes the automation process for routine tasks based on information from the emotion engine. The timing and content of task execution are set according to the user's emotions.

[0876] Step 8:

[0877] The server continuously monitors the system's operational status and updates the system to meet new technological and regulatory requirements.

[0878] Step 9:

[0879] Users can participate in an emotionally conscious training program and learn how to effectively operate the system.

[0880] (Example 2)

[0881] 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".

[0882] For companies to manage environmental risks and achieve sustainable operations, they need to effectively collect and analyze vast amounts of information. However, traditional systems do not consider user emotions, resulting in suggestions that are not optimized for individual users and failing to fully realize the benefits of decision-making. Furthermore, unintuitive user interfaces can lead to user stress and confusion.

[0883] 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.

[0884] In this invention, the server includes means for integrating environmental information using multiple data sets collected from companies, means for removing unnecessary data from the information, and means for extracting trends from the integrated information using an analytical algorithm. This enables personalized strategic proposals that take user emotions into account, leading to more effective decision-making.

[0885] A "business" is a social entity that engages in commercial activities and provides goods and services.

[0886] A "data collection" is a set of information that has been collected and stored for a specific purpose.

[0887] "Environmental information" refers to all information related to the natural environment and human activities.

[0888] "Integration" is the process of combining different pieces of information to form a unified whole.

[0889] "Removal" means removing unnecessary or inaccurate elements.

[0890] A "trend" is a certain direction or sign of change that can be observed as a result of analyzing data.

[0891] "Risk" refers to the possibility of undesirable outcomes occurring.

[0892] A "model" is an abstract or concrete representation used to understand and predict a particular phenomenon or process.

[0893] A "strategic proposal" refers to a planned action plan designed to achieve a specific objective.

[0894] "Display information" refers to data and instructions that are provided to the user visually.

[0895] A "routine operation" is a standard process that is repeatedly performed according to a specific procedure.

[0896] "Emotional state" refers to the temporary psychological feelings a person experiences in a particular situation.

[0897] A "generative AI model" is an artificial intelligence system that has the ability to generate new information and content based on large amounts of data.

[0898] A "prompt" refers to an instruction or question given to a system to prompt it to take a specific response or action.

[0899] "Information means" refers to a set of technologies and methods for collecting, processing, storing, and transmitting information.

[0900] "Usage" refers to the circumstances and conditions under which a system or tool is actually used.

[0901] "Updating" means bringing a system to its latest state by incorporating new data and technologies.

[0902] "Information technology literacy" refers to the ability and skills to understand and utilize information technology.

[0903] A "training program" refers to a learning process designed to acquire specific goals or skills.

[0904] This invention is a system designed to support corporate environmental management. The system aims to provide users with optimal strategic recommendations by integrating and analyzing vast amounts of data collected from both inside and outside the company. Furthermore, the system incorporates an emotion engine, enabling it to provide information that takes into account the user's emotional state.

[0905] The server collects environmental data from corporate databases and external sources. Specifically, it retrieves data using database management systems and APIs, and performs data cleansing using data preparation tools (e.g., OpenRefine). Then, it uses AI analysis software (e.g., TensorFlow) to extract trends and patterns from the data. This allows for the construction of models that predict future environmental risks and generate strategic recommendations.

[0906] The device is responsible for displaying this information through the user interface. The front-end framework used (e.g., React or Vue.js) provides an intuitive and easy-to-understand interface. Furthermore, a sentiment analysis engine analyzes the user's emotions in real time and adjusts the interface accordingly. For example, if the user is stressed, the interface is simplified, and only important information is highlighted.

[0907] Users make decisions for sustainable operations based on this displayed information. The server supports the user experience by using a generative AI model to generate specific prompts. For example, it can generate a prompt such as, "The user is feeling anxious, so restructure the high-priority suggestions into easily understandable language."

[0908] In this way, this system, which incorporates an emotion engine, can support flexible and effective environmental management decision-making optimized for the user's emotions.

[0909] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0910] Step 1:

[0911] The server collects environmental data via internal and external databases and APIs. Input is raw data obtained from various sources, and output is a unified format of this data. Specifically, the server executes queries, collects the necessary data, and then converts that data into a unified format.

[0912] Step 2:

[0913] The server performs data cleansing to organize the integrated data. The input is the data integrated in step 1, and the output is the data from which unnecessary elements have been removed and accuracy has been verified. Specifically, the server uses cleansing tools to remove missing values ​​and duplicates and correct outliers.

[0914] Step 3:

[0915] The server feeds the cleansed data into an AI analysis algorithm for analysis. The input is the cleansed data, and the output is the patterns and trends extracted from the data. Specifically, the server uses analysis software to analyze the data using regression analysis and clustering methods.

[0916] Step 4:

[0917] The server builds a model to predict environmental risks based on the analysis results. The input is the patterns and trends obtained in step 3, and the output is the model for predicting risks. Specifically, the server uses machine learning techniques to simulate future scenarios from past data.

[0918] Step 5:

[0919] The server generates and outputs strategic proposals based on a predictive model. The input is a model for predicting risk, and the output is a strategic proposal presented to the user. Specifically, the server uses a generation AI model to create proposals that reflect the analysis results.

[0920] Step 6:

[0921] The terminal displays strategic proposals sent from the server on its user interface. The input is the strategic proposals generated by the server, and the output is the information visually displayed on the screen. Specifically, the terminal provides the information with an appropriate layout and color scheme.

[0922] Step 7:

[0923] The device analyzes the user's emotional data using an emotion engine and adjusts the interface accordingly. The input is the user's real-time emotional data, and the output is the interface adjustment based on the user's emotions. Specifically, the device runs emotion analysis software and changes the layout and amount of information displayed to match the user's state.

[0924] Step 8:

[0925] The user makes a decision based on the information presented. The input is the strategic proposal displayed on the terminal, and the output is the decision chosen by the user. In terms of specific actions, the user interacts with the interface and expresses their opinion on the proposal.

[0926] Step 9:

[0927] The server collects user decisions and feedback, and uses this information to improve future suggestions. The input is user feedback, and the output is the improved suggestion. Specifically, the server accumulates feedback information and retrains its machine learning model to improve the accuracy of the suggestions.

[0928] (Application Example 2)

[0929] 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".

[0930] In modern urban environments, traffic management is complex and can amplify residents' stress and anxiety. In particular, stress caused by traffic congestion and delays significantly impacts residents' daily lives. Furthermore, corporate environmental management systems often make decisions without considering users' emotional states, resulting in proposals that don't suit the user's situation. Therefore, there is a need for a system that utilizes emotional data to support decision-making, reduce user stress, and manage optimal transportation options.

[0931] 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. In this invention, the server includes means for acquiring data from information sources, means for integrating the acquired data and supplementing or removing missing values ​​or inaccurate data, and means for performing analysis to identify patterns and trends using the integrated data. This enables optimal traffic management and decision support that takes into account the user's emotional data.

[0932] text

[0933] An "information source" refers to the fundamental devices or platforms used to provide data.

[0934] "Data integration" is the process of organizing data obtained from different sources into a single, unified format.

[0935] "Missing values" refer to a state in a dataset where information that should be present is missing.

[0936] "Inaccurate data" refers to data that contains false information or biases, and therefore lacks reliability in analysis.

[0937] "Patterns and trends" are elements that indicate recurring tendencies or movements found in data.

[0938] "Performing analysis" is the process of applying statistical methods and machine learning techniques to data to extract meaningful information.

[0939] A "risk prediction model" is a mathematical or statistical model built to predict potential problems or difficulties in the future.

[0940] "Suggestions to support decision-making" refers to information and advice generated to effectively assist users in making decisions.

[0941] A "user display device" is a digital screen device used to convey information visually.

[0942] "Automating part of a process" means minimizing human intervention and allowing machines or software to independently execute the procedures.

[0943] "Monitoring and updating" means constantly checking the functionality and performance of a system and making improvements to adapt to new technologies and regulations.

[0944] An "educational program" is a series of instructions designed to enhance the user's understanding and technical skills.

[0945] An "emotional processing device" is a system or device that analyzes a user's emotional state and takes appropriate countermeasures.

[0946] "Managing transportation in an urban environment" means optimizing elements related to transportation and movement, and making adjustments and controls to realize efficient urban living.

[0947] The system for implementing this invention supports traffic management decision-making that takes user emotions into consideration.

[0948] The server efficiently acquires data from various sources, integrates that data, and supplements or removes inaccurate values. Based on the integrated data, it analyzes patterns and trends using AI algorithms. Furthermore, it builds a risk prediction model based on this analysis and generates optimal recommendations for the user. To reflect the user's emotional state in these recommendations, it uses an emotion processing device.

[0949] The user's device receives suggestions generated by the server in real time and presents them to the user visually. The user interface reflects the user's current emotional state, and the information is adjusted to be more easily understood. This makes it easier for the user to intuitively accept suggestions such as stress reduction measures and efficient modes of transportation.

[0950] For example, if traffic congestion occurs during the morning commute, the server generates alternative route suggestions and presents them to the user without causing any inconvenience. If the emotion processing unit determines that the user is frustrated with the traffic situation, it automatically adjusts the color scheme and font size to create a more calming environment.

[0951] Example prompt: "Please consider sentiment data when designing an interface for a smart city's traffic management system to reduce resident stress."

[0952] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0953] text

[0954] Step 1:

[0955] The server collects data in real time from various information sources. Inputs include traffic sensor data, weather data, and historical traffic history. This data is stored in a database to obtain output for the next analysis step.

[0956] Step 2:

[0957] The server cleanses the collected data, correcting or removing missing values ​​and inaccurate data. This process transforms the information in the database into accurate and consistent data. As a result, integrated, cleansed data is output.

[0958] Step 3:

[0959] The server applies AI algorithms to cleansed data to identify patterns and trends. This input includes integrated data from a database. The output generates predicted congestion patterns and potential risks.

[0960] Step 4:

[0961] The server builds a model to predict future traffic risks using risk patterns generated by AI. It utilizes identified trend data as input, and the predictive model is completed as output.

[0962] Step 5:

[0963] The server generates suggestions that take into account the user's emotional state, based on a predictive model. The emotion processing unit uses the user's collected emotional data as input and outputs suggestions that optimize the content presented in the user interface.

[0964] Step 6:

[0965] The terminal displays optimized suggestions generated by the server to the user. The user interface visualizes the output information by dynamically adjusting colors and font sizes, reflecting the emotional state input from the user.

[0966] Step 7:

[0967] The user receives information from their device and determines their own transportation strategy. Optimized suggestion information from the device is used as input, and the user's next action is guided as output.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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."

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] 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.

[0989] The following is further disclosed regarding the embodiments described above.

[0990] (Claim 1)

[0991] Means for obtaining environmental data from multiple sources related to a company,

[0992] A means of integrating acquired environmental data and supplementing or removing missing or inaccurate data,

[0993] A means of performing analysis to identify patterns and trends using integrated data,

[0994] A means of constructing a model for predicting future environmental risks based on the analysis results,

[0995] A means for generating proposals to support strategic decision-making based on predictive models,

[0996] A means of displaying the generated suggestions in the user interface and providing information to the user,

[0997] Means for automating part of business processes,

[0998] A means of continuously monitoring the system's operational status and updating the system to conform to the latest technologies and regulations,

[0999] A system that includes means for providing training programs aimed at improving user literacy.

[1000] (Claim 2)

[1001] The system according to claim 1, further comprising means for maintaining an integrated database of environmental data and for streamlining access to the data.

[1002] (Claim 3)

[1003] The system according to claim 1, further comprising means for providing customized solutions tailored to the industry and size of a company based on the system proposal.

[1004] "Example 1"

[1005] (Claim 1)

[1006] A means of collecting and integrating environmental information from multiple data sources,

[1007] A means for correcting or removing outliers and missing values ​​from the collected data,

[1008] A means of identifying patterns and trends using cleansed data and a generative AI model,

[1009] A means of constructing a model that predicts environmental risk based on identified patterns,

[1010] A means of generating improvement proposals for companies according to the constructed model,

[1011] A means of visualizing the generated suggestions to the user and providing them through an interactive interface,

[1012] A means to automate a portion of the proposed business process and improve efficiency,

[1013] A means of monitoring the system's operational status and making adjustments to comply with technological updates and new regulations,

[1014] A system that includes means for providing users with training programs to improve their system usage skills.

[1015] (Claim 2)

[1016] The system according to claim 1, further comprising means for maintaining an integrated record of environmental data and for streamlining access to the information.

[1017] (Claim 3)

[1018] The system according to claim 1, further comprising means for providing customized solutions tailored to the characteristics and scale of the industry, in accordance with the system proposal.

[1019] "Application Example 1"

[1020] (Claim 1)

[1021] A means of collecting environmental data from multiple data sources related to a population,

[1022] A means of integrating collected environmental data and supplementing or removing missing or inaccurate data,

[1023] A means of performing analysis to identify trends and patterns using integrated data,

[1024] A means of constructing a model for predicting future environmental risks based on the analysis results,

[1025] A means of generating proposals to support strategic decision-making based on predictive models,

[1026] A means of displaying the generated proposals on an information display device and communicating the information to the user,

[1027] Means for automating part of the activity process,

[1028] A means of continuously monitoring the system's operational status and updating the system to conform to the latest technologies and regulations,

[1029] A means of providing training programs aimed at improving users' understanding of technology,

[1030] A system that includes means of providing information to make energy consumption and environmental management at the city level visible to citizens.

[1031] (Claim 2)

[1032] The system according to claim 1, further comprising means for maintaining an integrated database of environmental data and optimizing access to the data.

[1033] (Claim 3)

[1034] The system according to claim 1, further comprising means for providing customized solutions based on the system's proposal, according to the type and size of the group.

[1035] "Example 2 of combining an emotion engine"

[1036] (Claim 1)

[1037] A means of integrating environmental information using multiple data sets collected from companies,

[1038] A means of removing unnecessary data from integrated information,

[1039] A means of extracting trends from integrated information using an analytical algorithm,

[1040] A means of constructing a model to estimate risk based on the analyzed results,

[1041] A means of generating strategic proposals based on an inference model,

[1042] A means of providing the generated strategic proposals to users as display information,

[1043] Means for automating routine operations,

[1044] A means of optimizing suggestions using an emotion analysis engine that analyzes the user's emotional state based on information from the user,

[1045] A means of generating prompts corresponding to emotional data using a generative AI model,

[1046] A means of adjusting information means to provide a display that is easy for users to understand intuitively,

[1047] A means to continuously monitor usage and update the system to conform to the latest technical standards,

[1048] A means of providing training programs to enhance users' information technology literacy,

[1049] A system that includes this.

[1050] (Claim 2)

[1051] The system according to claim 1, which maintains integrated information management of environmental information and streamlines information acquisition.

[1052] (Claim 3)

[1053] The system according to claim 1, comprising means for providing solutions tailored to the type and size of an organization based on the system's proposal.

[1054] "Application example 2 when combining with an emotional engine"

[1055] text

[1056] (Claim 1)

[1057] Means of obtaining data from information sources,

[1058] A means of integrating the acquired data and supplementing or removing missing or inaccurate data,

[1059] A means of performing analysis to identify patterns and trends using integrated data,

[1060] A means of constructing a model for predicting risk based on the analysis results,

[1061] A means for generating suggestions to support decision-making based on a predictive model,

[1062] A means of displaying the generated proposal on the user's display device and providing information,

[1063] Means for automating part of the process,

[1064] A means of continuously monitoring the system's operational status and updating the system to conform to the latest technologies and regulations,

[1065] A means of providing educational programs aimed at improving user literacy,

[1066] A means of analyzing the user's emotional data using an emotion processing device and adjusting the displayed information and interface to optimize it for the user,

[1067] A system that includes means to support decision-making for managing transportation in an urban environment based on emotional data.

[1068] (Claim 2)

[1069] The system according to claim 1, further comprising means for holding an integrated data recording medium and for streamlining access to the data.

[1070] (Claim 3)

[1071] The system according to claim 1, further comprising means for providing customized solutions tailored to the characteristics of an entity based on the system's proposal. [Explanation of Symbols]

[1072] 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 collecting environmental data from multiple data sources related to a population, A means of integrating collected environmental data and supplementing or removing missing or inaccurate data, A means of performing analysis to identify trends and patterns using integrated data, A means of constructing a model for predicting future environmental risks based on the analysis results, A means of generating proposals to support strategic decision-making based on predictive models, A means of displaying the generated proposals on an information display device and communicating the information to the user, Means for automating part of the activity process, A means of continuously monitoring the system's operational status and updating the system to conform to the latest technologies and regulations, A means of providing training programs aimed at improving users' understanding of technology, A system that includes means of providing information to make energy consumption and environmental management at the city level visible to citizens.

2. The system according to claim 1, further comprising means for maintaining an integrated database of environmental data and optimizing access to the data.

3. The system according to claim 1, further comprising means for providing customized solutions based on the system's proposal, according to the type and size of the group.

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