system

Abstract

The system according to this embodiment aims to propose and implement measures to optimize energy consumption and reduce environmental impact. [Solution] The system according to this embodiment comprises an AI energy-saving measure proposal unit, an environmental investment ROI calculation unit, a regulatory compliance guidance unit, a supply chain CO2 analysis unit, and a green procurement support unit. The AI ​​energy-saving measure proposal unit proposes measures to optimize energy consumption. The environmental investment ROI calculation unit calculates the return on environmental investment based on the measures proposed by the AI ​​energy-saving measure proposal unit. The regulatory compliance guidance unit provides guidance to comply with the latest environmental regulations based on the return calculated by the environmental investment ROI calculation unit. The supply chain CO2 analysis unit analyzes CO2 emissions across the entire supply chain based on the guidance provided by the regulatory compliance guidance unit. The green procurement support unit supports environmentally conscious procurement based on the CO2 emissions analyzed by the supply chain CO2 analysis unit.

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] In the conventional technology, there are problems that it is difficult to visualize CO2 emissions and measure the effects of environmental measures, and it places a burden on responding to environmental regulations and formulating implementation plans.

[0005] The system according to the embodiment aims to optimize energy consumption and propose and implement measures for reducing environmental load.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an AI energy-saving measure proposal unit, an environmental investment ROI calculation unit, a regulatory compliance guidance unit, a supply chain CO2 analysis unit, and a green procurement support unit. The AI ​​energy-saving measure proposal unit proposes measures to optimize energy consumption. The environmental investment ROI calculation unit calculates the return on environmental investment based on the measures proposed by the AI ​​energy-saving measure proposal unit. The regulatory compliance guidance unit provides guidance for complying with the latest environmental regulations based on the return calculated by the environmental investment ROI calculation unit. The supply chain CO2 analysis unit analyzes CO2 emissions across the entire supply chain based on the guidance provided by the regulatory compliance guidance unit. The green procurement support unit supports environmentally conscious procurement based on the CO2 emissions analyzed by the supply chain CO2 analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose and implement measures to optimize energy consumption and reduce environmental impact. [Brief explanation of the drawing]

[0008] [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 a data processing device and a 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]This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] 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), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] 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.

[0013] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (for example, a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The environmental load reduction support system according to an embodiment of the present invention is a system that uses an AI agent to optimize energy consumption, calculate the return on environmental investment, comply with environmental regulations, analyze CO2 emissions in the supply chain, and support green procurement. This system aims to solve the challenges faced by target companies such as manufacturers, office building managers, logistics companies, local governments, and ESG investors, and to realize both a sustainable society and economic growth. For example, the environmental load reduction support system proposes measures to optimize energy consumption. For example, in the manufacturing industry, it proposes reducing energy consumption by optimizing machine operating hours. Next, the environmental load reduction support system calculates the return on environmental investment and visualizes the effect of the investment. For example, it calculates the ROI of equipment investment for energy efficiency for office building managers and supports investment decision-making. Furthermore, the environmental load reduction support system proposes countermeasures based on the latest environmental regulations. For example, it proposes countermeasures based on the latest environmental regulations to logistics companies. Based on these proposals, the environmental load reduction support system analyzes CO2 emissions from the entire supply chain. For example, it visualizes CO2 emissions from the entire supply chain for local governments and proposes measures to reduce them. Finally, the environmental load reduction support system supports environmentally conscious procurement. For example, it can assist ESG investors in selecting environmentally conscious companies. This allows the environmental impact reduction support system to address challenges faced by target groups such as manufacturers, office building managers, logistics companies, local governments, and ESG investors, thereby achieving both sustainable society and economic growth. The system can optimize energy consumption, calculate returns on environmental investments, comply with environmental regulations, analyze CO2 emissions in supply chains, and support green procurement.

[0029] The environmental load reduction support system according to this embodiment comprises an AI energy-saving measure proposal unit, an environmental investment ROI calculation unit, a regulatory compliance guidance unit, a supply chain CO2 analysis unit, and a green procurement support unit. The AI ​​energy-saving measure proposal unit proposes measures to optimize energy consumption. For example, the AI ​​energy-saving measure proposal unit proposes reducing energy consumption by optimizing machine operating hours in the manufacturing industry. For example, the AI ​​energy-saving measure proposal unit can reduce energy consumption by scheduling machine operating hours. For example, the AI ​​energy-saving measure proposal unit can reduce energy consumption by improving machine operating efficiency. For example, the AI ​​energy-saving measure proposal unit can reduce energy consumption by using an algorithm for optimizing machine operating hours. The environmental investment ROI calculation unit calculates the return on environmental investment based on the measures proposed by the AI ​​energy-saving measure proposal unit. For example, the environmental investment ROI calculation unit calculates the ROI of equipment investment for energy efficiency for office building managers and supports investment decision-making. For example, the environmental investment ROI calculation unit can calculate the energy cost reduction effect relative to the investment amount. For example, the environmental investment ROI calculation unit can calculate the CO2 emission reduction effect relative to the investment amount. The Environmental Investment ROI Calculation Unit can, for example, calculate the effect of reducing environmental impact relative to the amount of investment. The Regulatory Compliance Guidance Unit provides guidance for complying with the latest environmental regulations based on the return calculated by the Environmental Investment ROI Calculation Unit. The Regulatory Compliance Guidance Unit can, for example, propose countermeasures to logistics companies based on the latest environmental regulations. The Regulatory Compliance Guidance Unit can, for example, propose compliance with emission standards. The Regulatory Compliance Guidance Unit can, for example, propose the preparation of environmental reports. The Regulatory Compliance Guidance Unit can, for example, provide implementation plans for countermeasures based on environmental regulations. The Supply Chain CO2 Analysis Unit analyzes CO2 emissions across the entire supply chain based on the guidance provided by the Regulatory Compliance Guidance Unit. The Supply Chain CO2 Analysis Unit can, for example, visualize CO2 emissions from the entire regional supply chain to local governments and propose measures for reduction.The Supply Chain CO2 Analysis Department can, for example, propose methods for collecting data from each company. The Supply Chain CO2 Analysis Department can, for example, propose methods for aggregating emissions. The Supply Chain CO2 Analysis Department can, for example, propose criteria for calculating CO2 emissions. The Green Procurement Support Department supports environmentally conscious procurement based on the CO2 emissions analyzed by the Supply Chain CO2 Analysis Department. The Green Procurement Support Department can, for example, assist ESG investors in selecting environmentally conscious companies. The Green Procurement Support Department can, for example, propose the use of renewable energy. The Green Procurement Support Department can, for example, propose obtaining ecolabels. The Green Procurement Support Department can, for example, evaluate the presence or absence of environmental certifications. As a result, the environmental load reduction support system according to the embodiment can optimize energy consumption, calculate returns on environmental investments, comply with environmental regulations, analyze CO2 emissions in the supply chain, and support green procurement.

[0030] The AI ​​Energy Saving Measures Proposal Department proposes measures to optimize energy consumption. Specifically, it proposes ways to reduce energy consumption in the manufacturing industry by optimizing machine operating hours. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by scheduling machine operating hours. This involves using algorithms to adjust machine operating hours according to demand and avoid peak energy consumption. Furthermore, to improve machine operating efficiency, it can optimize machine maintenance schedules to prevent breakdowns and wasted energy consumption. The AI ​​Energy Saving Measures Proposal Department monitors machine operating data in real time and analyzes energy consumption patterns to propose an optimal operating schedule. For example, the AI ​​analyzes machine operating data and identifies patterns that contribute to optimizing operating hours and reducing energy consumption. As a result, the AI ​​Energy Saving Measures Proposal Department can propose specific measures to reduce energy consumption and improve the energy efficiency of companies. In addition, the AI ​​Energy Saving Measures Proposal Department contributes not only to reducing energy consumption but also to reducing CO2 emissions. For example, by reducing energy consumption, CO2 emissions can be reduced, mitigating the environmental burden on companies. In this way, the AI ​​Energy Saving Measures Proposal Department can support the sustainable growth of companies and contribute to environmental protection.

[0031] The Environmental Investment ROI Calculation Unit calculates the return on environmental investments based on the measures proposed by the AI ​​Energy Saving Measure Proposal Unit. Specifically, it calculates the ROI of equipment investments for energy efficiency for office building managers, supporting investment decision-making. For example, the Environmental Investment ROI Calculation Unit can calculate the energy cost reduction effect relative to the investment amount. This involves calculating the energy costs reduced by equipment investments for energy efficiency and comparing that reduction effect with the investment amount. It can also calculate the CO2 emission reduction effect relative to the investment amount. For example, it calculates the CO2 emissions reduced by equipment investments for energy efficiency and compares that reduction effect with the investment amount. Furthermore, it can also calculate the environmental load reduction effect relative to the investment amount. For example, it calculates the environmental load reduced by equipment investments for energy efficiency and compares that reduction effect with the investment amount. In this way, the Environmental Investment ROI Calculation Unit can provide companies with information to accurately understand the return on environmental investments and make investment decisions. Furthermore, the Environmental Investment ROI Calculation Unit can also evaluate investment returns over the long term. For example, it can evaluate the energy cost reduction effect and CO2 emission reduction effect obtained from equipment investments over the long term and predict investment returns. This allows companies to make environmental investment decisions from a long-term perspective.

[0032] The Regulatory Compliance Guidance Department provides guidance on complying with the latest environmental regulations based on the returns calculated by the Environmental Investment ROI Calculation Department. Specifically, it proposes compliance measures to logistics companies based on the latest environmental regulations. For example, the Regulatory Compliance Guidance Department can propose compliance with emission standards. This includes providing specific measures for companies to comply with emission standards based on the latest environmental regulations. It can also propose the preparation of environmental reports. For example, it provides guidance for companies to prepare environmental reports and report on compliance measures based on environmental regulations. Furthermore, it can provide implementation plans for compliance measures based on environmental regulations. For example, it provides specific plans for companies to implement compliance measures based on environmental regulations. In this way, the Regulatory Compliance Guidance Department can provide specific guidance for companies to comply with the latest environmental regulations and reduce their environmental impact. In addition, the Regulatory Compliance Guidance Department can provide training and education programs for companies to comply with environmental regulations. For example, it can conduct training on environmental regulations for company employees and educate them on the importance of regulatory compliance and specific measures. In this way, the Regulatory Compliance Guidance Department can support companies in improving their ability to comply with environmental regulations and reducing their environmental impact.

[0033] The Supply Chain CO2 Analysis Department analyzes CO2 emissions across the entire supply chain based on guidance provided by the Regulatory Guidance Department. Specifically, it visualizes CO2 emissions across the entire regional supply chain for local governments and proposes measures for reduction. For example, the Supply Chain CO2 Analysis Department can propose methods for collecting data from each company. This includes providing specific methods for companies to collect and share data on CO2 emissions. It can also propose methods for aggregating emissions. For example, it provides a method for aggregating CO2 emission data collected by companies and calculating emissions across the entire supply chain. Furthermore, it can propose CO2 emission calculation standards. For example, it provides standards for companies to calculate CO2 emissions and helps them calculate emissions in a unified way. As a result, the Supply Chain CO2 Analysis Department can accurately grasp CO2 emissions across the entire supply chain and propose specific measures for reduction. In addition, the Supply Chain CO2 Analysis Department can evaluate the effectiveness of CO2 emission reductions. For example, it evaluates the effectiveness of CO2 emission reduction measures implemented by companies and provides feedback on the results. This allows companies to continuously improve their CO2 emission reduction efforts and strengthen their efforts to reduce their environmental impact.

[0034] The Green Procurement Support Department supports environmentally conscious procurement based on CO2 emissions analyzed by the Supply Chain CO2 Analysis Department. Specifically, it assists ESG investors in selecting environmentally conscious companies. For example, the Green Procurement Support Department can propose the use of renewable energy. This involves providing companies with specific methods for using renewable energy and supporting its implementation. It can also propose obtaining ecolabels. For example, it assists companies in obtaining ecolabels and providing environmentally friendly products and services. Furthermore, it can evaluate whether a company has environmental certifications. For example, it evaluates whether a company has obtained environmental certifications and supports environmentally conscious procurement based on the results. In this way, the Green Procurement Support Department can support companies in their efforts to conduct environmentally conscious procurement and reduce the environmental impact of the entire supply chain. In addition, the Green Procurement Support Department can provide training and education programs for companies to conduct environmentally conscious procurement. For example, it educates company procurement personnel on the importance of environmentally conscious procurement and specific methods. In this way, the Green Procurement Support Department can support companies in improving their ability to conduct environmentally conscious procurement and reducing the environmental impact of the entire supply chain.

[0035] The AI ​​Energy Saving Measures Proposal Department can propose ways to reduce energy consumption in the manufacturing industry by optimizing machine operating times. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by scheduling machine operating times. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by improving machine operating efficiency. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by using algorithms to optimize machine operating times. This makes it possible to reduce energy consumption in the manufacturing industry.

[0036] The Environmental Investment ROI Calculation Unit can help office building managers make investment decisions by calculating the ROI of equipment investments for energy efficiency. For example, the Unit can calculate the ROI of introducing high-efficiency air conditioning equipment. For example, the Unit can calculate the ROI of introducing LED lighting. For example, the Unit can calculate the ROI of introducing an energy management system. This helps office building managers make informed investment decisions.

[0037] The Regulatory Compliance Guidance Department can propose compliance measures to logistics companies based on the latest environmental regulations. For example, it can propose compliance with emission standards. For example, it can propose the preparation of environmental reports. For example, it can provide implementation plans for compliance measures based on environmental regulations. This helps logistics companies comply with environmental regulations.

[0038] The Supply Chain CO2 Analysis Department can visualize CO2 emissions from the entire supply chain of a region for local governments and propose measures for reduction. For example, the Department can propose methods for collecting data from each company. For example, the Department can propose methods for aggregating emissions. For example, the Department can propose criteria for calculating CO2 emissions. This enables local governments to visualize CO2 emissions from their entire supply chain and propose reduction measures.

[0039] The Green Procurement Support Department can assist ESG investors in selecting environmentally conscious companies. For example, the Green Procurement Support Department can propose the use of renewable energy. For example, the Green Procurement Support Department can propose the acquisition of ecolabels. For example, the Green Procurement Support Department can evaluate the presence or absence of environmental certifications. This allows the department to support ESG investors in selecting environmentally conscious companies.

[0040] The AI ​​Energy Saving Measures Proposal Department can analyze past energy consumption data and propose optimal energy saving measures. For example, based on past energy consumption data, the AI ​​Energy Saving Measures Proposal Department can propose measures to reduce peak energy consumption. For example, based on past energy consumption data, the AI ​​Energy Saving Measures Proposal Department can propose measures to reduce wasteful energy consumption. For example, based on past energy consumption data, the AI ​​Energy Saving Measures Proposal Department can propose measures that take into account seasonal energy consumption patterns. This makes it possible to propose optimal energy saving measures based on past energy consumption data.

[0041] The AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected. For example, the AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose measures to stop operation when an abnormality is detected. For example, the AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose measures to minimize energy consumption when an abnormality is detected. For example, the AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose measures to perform maintenance when an abnormality is detected. This makes it possible to monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected.

[0042] The AI ​​Energy Saving Measures Proposal Department can propose energy-saving measures while considering seasonal fluctuations in energy consumption. For example, it can propose measures to improve the efficiency of air conditioning to reduce energy consumption in the summer. For example, it can propose measures to improve the efficiency of heating to reduce energy consumption in the winter. For example, it can propose optimal measures by considering seasonal energy consumption patterns. This makes it possible to propose energy-saving measures that take into account seasonal fluctuations in energy consumption.

[0043] The AI ​​Energy Saving Measure Proposal Department can refer to successful energy saving measures from other companies and propose optimal measures. For example, it can propose similar measures based on the success stories of other companies. For example, it can analyze successful measures from other companies and propose measures suitable for one's own company. For example, it can refer to successful measures from other companies and propose customized measures. This makes it possible to propose optimal energy saving measures based on the success stories of other companies.

[0044] The Environmental Investment ROI Calculation Unit can analyze past investment data and propose optimal investment plans. For example, it can propose the investment plan with the highest return based on past investment data. For example, it can propose an investment plan that minimizes risk by analyzing past investment data. For example, it can propose an investment plan that is expected to yield long-term returns based on past investment data. This makes it possible to propose optimal investment plans based on past investment data.

[0045] The environmental investment ROI calculation unit can evaluate the environmental impact of an investment target in detail and reflect it in the ROI calculation. For example, the environmental investment ROI calculation unit can evaluate the CO2 emissions of an investment target and reflect it in the ROI calculation. For example, the environmental investment ROI calculation unit can evaluate the energy consumption of an investment target and reflect it in the ROI calculation. For example, the environmental investment ROI calculation unit can comprehensively evaluate the environmental burden of an investment target and reflect it in the ROI calculation. This makes it possible to perform ROI calculations that evaluate the environmental impact of an investment target in detail.

[0046] The Regulatory Guidance Department can collect the latest environmental regulatory information in real time and reflect it in the guidance. For example, the Regulatory Guidance Department can collect the latest environmental regulatory information in real time and reflect it in the guidance. For example, the Regulatory Guidance Department can update the guidance in real time if there are changes to environmental regulations. For example, the Regulatory Guidance Department can immediately reflect new information on environmental regulations in the guidance when it becomes available. This makes it possible to collect the latest environmental regulatory information in real time and reflect it in the guidance.

[0047] The Regulatory Compliance Guidance Department can analyze past regulatory compliance history and propose optimal countermeasures. For example, the Regulatory Compliance Guidance Department can propose optimal countermeasures based on past regulatory compliance history. For example, the Regulatory Compliance Guidance Department can analyze past regulatory compliance history and propose countermeasures that minimize risk. For example, the Regulatory Compliance Guidance Department can propose long-term countermeasures based on past regulatory compliance history. This makes it possible to propose optimal countermeasures based on past regulatory compliance history.

[0048] The Regulatory Guidance Department can provide guidance that takes into account the regional characteristics of regulatory compliance. For example, the Regulatory Guidance Department can provide optimal guidance for each region, taking into account the regional characteristics of regulatory compliance. For example, the Regulatory Guidance Department can analyze the regional characteristics of regulatory compliance and provide guidance that minimizes regional risks. For example, the Regulatory Guidance Department can provide long-term guidance for each region based on the regional characteristics of regulatory compliance. This makes it possible to provide regulatory compliance guidance that takes regional characteristics into account.

[0049] The Regulatory Compliance Guidance Department can refer to regulatory compliance case studies from other companies and propose optimal countermeasures. For example, the Regulatory Compliance Guidance Department can propose similar countermeasures based on the regulatory compliance case studies of other companies. For example, the Regulatory Compliance Guidance Department can analyze the regulatory compliance case studies of other companies and propose countermeasures suitable for the company. For example, the Regulatory Compliance Guidance Department can refer to the regulatory compliance case studies of other companies and propose customized countermeasures. This makes it possible to propose optimal countermeasures based on the regulatory compliance case studies of other companies.

[0050] The Supply Chain CO2 Analysis Department can collect detailed CO2 emission data from the entire supply chain and incorporate it into its analysis. For example, the Supply Chain CO2 Analysis Department can collect CO2 emission data from the entire supply chain and perform detailed analysis. For example, the Supply Chain CO2 Analysis Department can collect CO2 emission data from each stage of the supply chain and incorporate it into its analysis. For example, the Supply Chain CO2 Analysis Department can collect CO2 emission data from the entire supply chain in real time and incorporate it into its analysis. This makes it possible to collect detailed CO2 emission data from the entire supply chain and incorporate it into its analysis.

[0051] The Supply Chain CO2 Analysis Department can analyze historical CO2 emission data and propose optimal reduction measures. For example, the Supply Chain CO2 Analysis Department can propose optimal reduction measures based on historical CO2 emission data. For example, the Supply Chain CO2 Analysis Department can analyze historical CO2 emission data and propose reduction measures that minimize risk. For example, the Supply Chain CO2 Analysis Department can propose long-term reduction measures based on historical CO2 emission data. This makes it possible to propose optimal reduction measures based on historical CO2 emission data.

[0052] The Supply Chain CO2 Analysis Department can perform CO2 analysis while considering the geographical conditions of the supply chain. For example, the Supply Chain CO2 Analysis Department can analyze CO2 emission data for each region while considering the geographical conditions of the supply chain. For example, the Supply Chain CO2 Analysis Department can analyze the geographical conditions of the supply chain and propose CO2 reduction measures that minimize regional risks. For example, the Supply Chain CO2 Analysis Department can propose long-term CO2 reduction measures for each region based on the geographical conditions of the supply chain. This enables CO2 analysis that takes into account the geographical conditions of the supply chain.

[0053] The Supply Chain CO2 Analysis Department can refer to CO2 reduction case studies from other companies and propose optimal reduction measures. For example, the Supply Chain CO2 Analysis Department can propose similar reduction measures based on the CO2 reduction case studies of other companies. For example, the Supply Chain CO2 Analysis Department can analyze the CO2 reduction case studies of other companies and propose reduction measures suitable for one's own company. For example, the Supply Chain CO2 Analysis Department can refer to the CO2 reduction case studies of other companies and propose customized reduction measures. This makes it possible to propose optimal reduction measures based on the CO2 reduction case studies of other companies.

[0054] The Green Procurement Support Department can analyze past procurement data and propose the optimal green procurement plan. For example, based on past procurement data, the Green Procurement Support Department can propose the most environmentally friendly procurement plan. For example, based on past procurement data, the Green Procurement Support Department can propose a procurement plan that minimizes risk. For example, based on past procurement data, the Green Procurement Support Department can propose a procurement plan that anticipates long-term environmental considerations. This makes it possible to propose the optimal green procurement plan based on past procurement data.

[0055] The Green Procurement Support Department can conduct a detailed assessment of the environmental impact of the procured goods and reflect it in the proposal. For example, the Green Procurement Support Department can assess the CO2 emissions of the procured goods and reflect it in the proposal. For example, the Green Procurement Support Department can assess the energy consumption of the procured goods and reflect it in the proposal. For example, the Green Procurement Support Department can comprehensively assess the environmental burden of the procured goods and reflect it in the proposal. This makes it possible to provide proposals that have a detailed assessment of the environmental impact of the procured goods.

[0056] The Green Procurement Support Department can propose green procurement while considering the geographical conditions of the procurement target. For example, the Green Procurement Support Department can propose green procurement while considering the energy costs of the procurement target region. For example, the Green Procurement Support Department can propose green procurement while considering the environmental regulations of the procurement target region. For example, the Green Procurement Support Department can propose green procurement while considering the climatic conditions of the procurement target region. This makes it possible to propose green procurement that takes into account the geographical conditions of the procurement target.

[0057] The Green Procurement Support Department can propose optimal procurement plans by referring to green procurement case studies from other companies. For example, the Green Procurement Support Department can propose similar procurement plans based on the green procurement case studies of other companies. For example, the Green Procurement Support Department can analyze the green procurement case studies of other companies and propose procurement plans suitable for your own company. For example, the Green Procurement Support Department can propose customized procurement plans by referencing the green procurement case studies of other companies. This makes it possible to propose optimal procurement plans based on the green procurement case studies of other companies.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The AI ​​Energy Saving Measure Proposal Department can analyze past energy consumption data and propose optimal energy saving measures. For example, it can propose measures to reduce peak energy consumption based on past energy consumption data. It can propose measures to reduce wasteful energy consumption by analyzing past energy consumption data. It can propose measures that take into account seasonal energy consumption patterns based on past energy consumption data. This makes it possible to propose optimal energy saving measures based on past energy consumption data.

[0060] The AI ​​Energy Saving Measure Proposal Department can monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected. For example, it can monitor the operating status of machinery in real time and automatically propose measures to stop operation when an abnormality is detected. It can monitor the operating status of machinery in real time and automatically propose measures to minimize energy consumption when an abnormality is detected. It can monitor the operating status of machinery in real time and automatically propose measures to perform maintenance when an abnormality is detected. This makes it possible to monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected.

[0061] The Regulatory Guidance Department can collect the latest environmental regulatory information in real time and reflect it in the guidance. For example, it can collect the latest environmental regulatory information in real time and reflect it in the guidance. If there are changes to environmental regulations, the guidance can be updated in real time. If new information on environmental regulations becomes available, it can be immediately reflected in the guidance. This makes it possible to collect the latest environmental regulatory information in real time and reflect it in the guidance.

[0062] The Supply Chain CO2 Analysis Department can analyze historical CO2 emission data and propose optimal reduction measures. For example, it can propose optimal reduction measures based on historical CO2 emission data. It can analyze historical CO2 emission data and propose reduction measures that minimize risk. It can propose long-term reduction measures based on historical CO2 emission data. This makes it possible to propose optimal reduction measures based on historical CO2 emission data.

[0063] The Green Procurement Support Department can conduct detailed assessments of the environmental impact of procurement items and reflect them in proposals. For example, it can assess the CO2 emissions of procurement items and reflect them in proposals. It can assess the energy consumption of procurement items and reflect them in proposals. It can comprehensively assess the environmental burden of procurement items and reflect them in proposals. This makes it possible to make proposals that have a detailed assessment of the environmental impact of procurement items.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The AI ​​Energy Saving Measures Proposal Department proposes measures to optimize energy consumption. For example, in the manufacturing industry, it proposes reducing energy consumption by optimizing machine operating times. Specifically, energy consumption can be reduced by scheduling machine operating times or using algorithms that improve operational efficiency. Step 2: The Environmental Investment ROI Calculation Unit calculates the return on environmental investment based on the measures proposed by the AI ​​Energy Saving Measure Proposal Unit. For example, it calculates the ROI of equipment investments for energy efficiency for office building managers, supporting their investment decision-making. Specifically, it can calculate the energy cost reduction effect, CO2 emission reduction effect, and environmental load reduction effect relative to the investment amount. Step 3: The Regulatory Compliance Guidance Department provides guidance on complying with the latest environmental regulations based on the returns calculated by the Environmental Investment ROI Calculation Department. For example, it proposes compliance measures to logistics companies based on the latest environmental regulations. Specifically, it can provide compliance with emission standards, preparation of environmental reports, and implementation plans for compliance measures based on environmental regulations. Step 4: The Supply Chain CO2 Analysis Department analyzes CO2 emissions across the entire supply chain based on guidance provided by the Regulatory Compliance Guidance Department. For example, it visualizes CO2 emissions from the entire regional supply chain for local governments and proposes measures for reduction. Specifically, it can propose methods for collecting data from each company, methods for aggregating emissions, and criteria for calculating CO2 emissions. Step 5: The Green Procurement Support Department will support environmentally conscious procurement based on the CO2 emissions analyzed by the Supply Chain CO2 Analysis Department. For example, it will assist ESG investors in selecting environmentally conscious companies. Specifically, it can evaluate the use of renewable energy, acquisition of eco-labels, and the presence of environmental certifications.

[0066] (Example of form 2) The environmental load reduction support system according to an embodiment of the present invention is a system that uses an AI agent to optimize energy consumption, calculate the return on environmental investment, comply with environmental regulations, analyze CO2 emissions in the supply chain, and support green procurement. This system aims to solve the challenges faced by target companies such as manufacturers, office building managers, logistics companies, local governments, and ESG investors, and to realize both a sustainable society and economic growth. For example, the environmental load reduction support system proposes measures to optimize energy consumption. For example, in the manufacturing industry, it proposes reducing energy consumption by optimizing machine operating hours. Next, the environmental load reduction support system calculates the return on environmental investment and visualizes the effect of the investment. For example, it calculates the ROI of equipment investment for energy efficiency for office building managers and supports investment decision-making. Furthermore, the environmental load reduction support system proposes countermeasures based on the latest environmental regulations. For example, it proposes countermeasures based on the latest environmental regulations to logistics companies. Based on these proposals, the environmental load reduction support system analyzes CO2 emissions from the entire supply chain. For example, it visualizes CO2 emissions from the entire supply chain for local governments and proposes measures to reduce them. Finally, the environmental load reduction support system supports environmentally conscious procurement. For example, it can assist ESG investors in selecting environmentally conscious companies. This allows the environmental impact reduction support system to address challenges faced by target groups such as manufacturers, office building managers, logistics companies, local governments, and ESG investors, thereby achieving both sustainable society and economic growth. The system can optimize energy consumption, calculate returns on environmental investments, comply with environmental regulations, analyze CO2 emissions in supply chains, and support green procurement.

[0067] The environmental load reduction support system according to this embodiment comprises an AI energy-saving measure proposal unit, an environmental investment ROI calculation unit, a regulatory compliance guidance unit, a supply chain CO2 analysis unit, and a green procurement support unit. The AI ​​energy-saving measure proposal unit proposes measures to optimize energy consumption. For example, the AI ​​energy-saving measure proposal unit proposes reducing energy consumption by optimizing machine operating hours in the manufacturing industry. For example, the AI ​​energy-saving measure proposal unit can reduce energy consumption by scheduling machine operating hours. For example, the AI ​​energy-saving measure proposal unit can reduce energy consumption by improving machine operating efficiency. For example, the AI ​​energy-saving measure proposal unit can reduce energy consumption by using an algorithm for optimizing machine operating hours. The environmental investment ROI calculation unit calculates the return on environmental investment based on the measures proposed by the AI ​​energy-saving measure proposal unit. For example, the environmental investment ROI calculation unit calculates the ROI of equipment investment for energy efficiency for office building managers and supports investment decision-making. For example, the environmental investment ROI calculation unit can calculate the energy cost reduction effect relative to the investment amount. For example, the environmental investment ROI calculation unit can calculate the CO2 emission reduction effect relative to the investment amount. The Environmental Investment ROI Calculation Unit can, for example, calculate the effect of reducing environmental impact relative to the amount of investment. The Regulatory Compliance Guidance Unit provides guidance for complying with the latest environmental regulations based on the return calculated by the Environmental Investment ROI Calculation Unit. The Regulatory Compliance Guidance Unit can, for example, propose countermeasures to logistics companies based on the latest environmental regulations. The Regulatory Compliance Guidance Unit can, for example, propose compliance with emission standards. The Regulatory Compliance Guidance Unit can, for example, propose the preparation of environmental reports. The Regulatory Compliance Guidance Unit can, for example, provide implementation plans for countermeasures based on environmental regulations. The Supply Chain CO2 Analysis Unit analyzes CO2 emissions across the entire supply chain based on the guidance provided by the Regulatory Compliance Guidance Unit. The Supply Chain CO2 Analysis Unit can, for example, visualize CO2 emissions from the entire regional supply chain to local governments and propose measures for reduction.The Supply Chain CO2 Analysis Department can, for example, propose methods for collecting data from each company. The Supply Chain CO2 Analysis Department can, for example, propose methods for aggregating emissions. The Supply Chain CO2 Analysis Department can, for example, propose criteria for calculating CO2 emissions. The Green Procurement Support Department supports environmentally conscious procurement based on the CO2 emissions analyzed by the Supply Chain CO2 Analysis Department. The Green Procurement Support Department can, for example, assist ESG investors in selecting environmentally conscious companies. The Green Procurement Support Department can, for example, propose the use of renewable energy. The Green Procurement Support Department can, for example, propose obtaining ecolabels. The Green Procurement Support Department can, for example, evaluate the presence or absence of environmental certifications. As a result, the environmental load reduction support system according to the embodiment can optimize energy consumption, calculate returns on environmental investments, comply with environmental regulations, analyze CO2 emissions in the supply chain, and support green procurement.

[0068] The AI ​​Energy Saving Measures Proposal Department proposes measures to optimize energy consumption. Specifically, it proposes ways to reduce energy consumption in the manufacturing industry by optimizing machine operating hours. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by scheduling machine operating hours. This involves using algorithms to adjust machine operating hours according to demand and avoid peak energy consumption. Furthermore, to improve machine operating efficiency, it can optimize machine maintenance schedules to prevent breakdowns and wasted energy consumption. The AI ​​Energy Saving Measures Proposal Department monitors machine operating data in real time and analyzes energy consumption patterns to propose an optimal operating schedule. For example, the AI ​​analyzes machine operating data and identifies patterns that contribute to optimizing operating hours and reducing energy consumption. As a result, the AI ​​Energy Saving Measures Proposal Department can propose specific measures to reduce energy consumption and improve the energy efficiency of companies. In addition, the AI ​​Energy Saving Measures Proposal Department contributes not only to reducing energy consumption but also to reducing CO2 emissions. For example, by reducing energy consumption, CO2 emissions can be reduced, mitigating the environmental burden on companies. In this way, the AI ​​Energy Saving Measures Proposal Department can support the sustainable growth of companies and contribute to environmental protection.

[0069] The Environmental Investment ROI Calculation Unit calculates the return on environmental investments based on the measures proposed by the AI ​​Energy Saving Measure Proposal Unit. Specifically, it calculates the ROI of equipment investments for energy efficiency for office building managers, supporting investment decision-making. For example, the Environmental Investment ROI Calculation Unit can calculate the energy cost reduction effect relative to the investment amount. This involves calculating the energy costs reduced by equipment investments for energy efficiency and comparing that reduction effect with the investment amount. It can also calculate the CO2 emission reduction effect relative to the investment amount. For example, it calculates the CO2 emissions reduced by equipment investments for energy efficiency and compares that reduction effect with the investment amount. Furthermore, it can also calculate the environmental load reduction effect relative to the investment amount. For example, it calculates the environmental load reduced by equipment investments for energy efficiency and compares that reduction effect with the investment amount. In this way, the Environmental Investment ROI Calculation Unit can provide companies with information to accurately understand the return on environmental investments and make investment decisions. Furthermore, the Environmental Investment ROI Calculation Unit can also evaluate investment returns over the long term. For example, it can evaluate the energy cost reduction effect and CO2 emission reduction effect obtained from equipment investments over the long term and predict investment returns. This allows companies to make environmental investment decisions from a long-term perspective.

[0070] The Regulatory Compliance Guidance Department provides guidance on complying with the latest environmental regulations based on the returns calculated by the Environmental Investment ROI Calculation Department. Specifically, it proposes compliance measures to logistics companies based on the latest environmental regulations. For example, the Regulatory Compliance Guidance Department can propose compliance with emission standards. This includes providing specific measures for companies to comply with emission standards based on the latest environmental regulations. It can also propose the preparation of environmental reports. For example, it provides guidance for companies to prepare environmental reports and report on compliance measures based on environmental regulations. Furthermore, it can provide implementation plans for compliance measures based on environmental regulations. For example, it provides specific plans for companies to implement compliance measures based on environmental regulations. In this way, the Regulatory Compliance Guidance Department can provide specific guidance for companies to comply with the latest environmental regulations and reduce their environmental impact. In addition, the Regulatory Compliance Guidance Department can provide training and education programs for companies to comply with environmental regulations. For example, it can conduct training on environmental regulations for company employees and educate them on the importance of regulatory compliance and specific measures. In this way, the Regulatory Compliance Guidance Department can support companies in improving their ability to comply with environmental regulations and reducing their environmental impact.

[0071] The Supply Chain CO2 Analysis Department analyzes CO2 emissions across the entire supply chain based on guidance provided by the Regulatory Guidance Department. Specifically, it visualizes CO2 emissions across the entire regional supply chain for local governments and proposes measures for reduction. For example, the Supply Chain CO2 Analysis Department can propose methods for collecting data from each company. This includes providing specific methods for companies to collect and share data on CO2 emissions. It can also propose methods for aggregating emissions. For example, it provides a method for aggregating CO2 emission data collected by companies and calculating emissions across the entire supply chain. Furthermore, it can propose CO2 emission calculation standards. For example, it provides standards for companies to calculate CO2 emissions and helps them calculate emissions in a unified way. As a result, the Supply Chain CO2 Analysis Department can accurately grasp CO2 emissions across the entire supply chain and propose specific measures for reduction. In addition, the Supply Chain CO2 Analysis Department can evaluate the effectiveness of CO2 emission reductions. For example, it evaluates the effectiveness of CO2 emission reduction measures implemented by companies and provides feedback on the results. This allows companies to continuously improve their CO2 emission reduction efforts and strengthen their efforts to reduce their environmental impact.

[0072] The Green Procurement Support Department supports environmentally conscious procurement based on CO2 emissions analyzed by the Supply Chain CO2 Analysis Department. Specifically, it assists ESG investors in selecting environmentally conscious companies. For example, the Green Procurement Support Department can propose the use of renewable energy. This involves providing companies with specific methods for using renewable energy and supporting its implementation. It can also propose obtaining ecolabels. For example, it assists companies in obtaining ecolabels and providing environmentally friendly products and services. Furthermore, it can evaluate whether a company has environmental certifications. For example, it evaluates whether a company has obtained environmental certifications and supports environmentally conscious procurement based on the results. In this way, the Green Procurement Support Department can support companies in their efforts to conduct environmentally conscious procurement and reduce the environmental impact of the entire supply chain. In addition, the Green Procurement Support Department can provide training and education programs for companies to conduct environmentally conscious procurement. For example, it educates company procurement personnel on the importance of environmentally conscious procurement and specific methods. In this way, the Green Procurement Support Department can support companies in improving their ability to conduct environmentally conscious procurement and reducing the environmental impact of the entire supply chain.

[0073] The AI ​​Energy Saving Measures Proposal Department can propose ways to reduce energy consumption in the manufacturing industry by optimizing machine operating times. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by scheduling machine operating times. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by improving machine operating efficiency. For example, the AI ​​Energy Saving Measures Proposal Department can reduce energy consumption by using algorithms to optimize machine operating times. This makes it possible to reduce energy consumption in the manufacturing industry.

[0074] The Environmental Investment ROI Calculation Unit can help office building managers make investment decisions by calculating the ROI of equipment investments for energy efficiency. For example, the Unit can calculate the ROI of introducing high-efficiency air conditioning equipment. For example, the Unit can calculate the ROI of introducing LED lighting. For example, the Unit can calculate the ROI of introducing an energy management system. This helps office building managers make informed investment decisions.

[0075] The Regulatory Compliance Guidance Department can propose compliance measures to logistics companies based on the latest environmental regulations. For example, it can propose compliance with emission standards. For example, it can propose the preparation of environmental reports. For example, it can provide implementation plans for compliance measures based on environmental regulations. This helps logistics companies comply with environmental regulations.

[0076] The Supply Chain CO2 Analysis Department can visualize CO2 emissions from the entire supply chain of a region for local governments and propose measures for reduction. For example, the Department can propose methods for collecting data from each company. For example, the Department can propose methods for aggregating emissions. For example, the Department can propose criteria for calculating CO2 emissions. This enables local governments to visualize CO2 emissions from their entire supply chain and propose reduction measures.

[0077] The Green Procurement Support Department can assist ESG investors in selecting environmentally conscious companies. For example, the Green Procurement Support Department can propose the use of renewable energy. For example, the Green Procurement Support Department can propose the acquisition of ecolabels. For example, the Green Procurement Support Department can evaluate the presence or absence of environmental certifications. This allows the department to support ESG investors in selecting environmentally conscious companies.

[0078] The AI ​​energy-saving measure proposal unit can estimate the user's emotions and adjust the proposed energy-saving measures based on those emotions. For example, if the user is stressed, the AI ​​energy-saving measure proposal unit can propose simple and easy-to-implement energy-saving measures. For example, if the user is relaxed, the AI ​​energy-saving measure proposal unit can propose detailed energy-saving measures and provide an implementation plan. For example, if the user is in a hurry, the AI ​​energy-saving measure proposal unit can propose energy-saving measures that can be implemented quickly. This makes it possible to propose energy-saving measures that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The AI ​​Energy Saving Measures Proposal Department can analyze past energy consumption data and propose optimal energy saving measures. For example, based on past energy consumption data, the AI ​​Energy Saving Measures Proposal Department can propose measures to reduce peak energy consumption. For example, based on past energy consumption data, the AI ​​Energy Saving Measures Proposal Department can propose measures to reduce wasteful energy consumption. For example, based on past energy consumption data, the AI ​​Energy Saving Measures Proposal Department can propose measures that take into account seasonal energy consumption patterns. This makes it possible to propose optimal energy saving measures based on past energy consumption data.

[0080] The AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected. For example, the AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose measures to stop operation when an abnormality is detected. For example, the AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose measures to minimize energy consumption when an abnormality is detected. For example, the AI ​​Energy Saving Measure Proposal Unit can monitor the operating status of machinery in real time and automatically propose measures to perform maintenance when an abnormality is detected. This makes it possible to monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected.

[0081] The AI ​​energy-saving measure proposal unit can estimate the user's emotions and determine the priority of energy-saving measures based on those emotions. For example, if the user is stressed, the AI ​​energy-saving measure proposal unit can prioritize simple and easy-to-implement measures. For example, if the user is relaxed, the AI ​​energy-saving measure proposal unit can prioritize detailed measures and provide an implementation plan. For example, if the user is in a hurry, the AI ​​energy-saving measure proposal unit can prioritize measures that can be implemented quickly. This makes it possible to determine the priority of energy-saving measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The AI ​​Energy Saving Measures Proposal Department can propose energy-saving measures while considering seasonal fluctuations in energy consumption. For example, it can propose measures to improve the efficiency of air conditioning to reduce energy consumption in the summer. For example, it can propose measures to improve the efficiency of heating to reduce energy consumption in the winter. For example, it can propose optimal measures by considering seasonal energy consumption patterns. This makes it possible to propose energy-saving measures that take into account seasonal fluctuations in energy consumption.

[0083] The AI ​​Energy Saving Measure Proposal Department can refer to successful energy saving measures from other companies and propose optimal measures. For example, it can propose similar measures based on the success stories of other companies. For example, it can analyze successful measures from other companies and propose measures suitable for one's own company. For example, it can refer to successful measures from other companies and propose customized measures. This makes it possible to propose optimal energy saving measures based on the success stories of other companies.

[0084] The environmental investment ROI calculation unit can estimate the user's emotions and adjust the way the ROI calculation results are displayed based on the estimated user emotions. For example, if the user is stressed, the environmental investment ROI calculation unit can provide a simple and visually easy-to-understand display method. For example, if the user is relaxed, the environmental investment ROI calculation unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the environmental investment ROI calculation unit can provide a display method that gets straight to the point. This makes it possible to adjust the way the ROI calculation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The Environmental Investment ROI Calculation Unit can analyze past investment data and propose optimal investment plans. For example, it can propose the investment plan with the highest return based on past investment data. For example, it can propose an investment plan that minimizes risk by analyzing past investment data. For example, it can propose an investment plan that is expected to yield long-term returns based on past investment data. This makes it possible to propose optimal investment plans based on past investment data.

[0086] The environmental investment ROI calculation unit can evaluate the environmental impact of an investment target in detail and reflect it in the ROI calculation. For example, the environmental investment ROI calculation unit can evaluate the CO2 emissions of an investment target and reflect it in the ROI calculation. For example, the environmental investment ROI calculation unit can evaluate the energy consumption of an investment target and reflect it in the ROI calculation. For example, the environmental investment ROI calculation unit can comprehensively evaluate the environmental burden of an investment target and reflect it in the ROI calculation. This makes it possible to perform ROI calculations that evaluate the environmental impact of an investment target in detail.

[0087] The regulatory compliance guidance unit can estimate the user's emotions and adjust the content of the regulatory compliance guidance based on the estimated user emotions. For example, if the user is stressed, the regulatory compliance guidance unit can provide simple and easy-to-follow guidance. For example, if the user is relaxed, the regulatory compliance guidance unit can provide detailed guidance and an action plan. For example, if the user is in a hurry, the regulatory compliance guidance unit can provide guidance that can be quickly implemented. This makes it possible to adjust the content of the regulatory compliance guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The Regulatory Guidance Department can collect the latest environmental regulatory information in real time and reflect it in the guidance. For example, the Regulatory Guidance Department can collect the latest environmental regulatory information in real time and reflect it in the guidance. For example, the Regulatory Guidance Department can update the guidance in real time if there are changes to environmental regulations. For example, the Regulatory Guidance Department can immediately reflect new information on environmental regulations in the guidance when it becomes available. This makes it possible to collect the latest environmental regulatory information in real time and reflect it in the guidance.

[0089] The Regulatory Compliance Guidance Department can analyze past regulatory compliance history and propose optimal countermeasures. For example, the Regulatory Compliance Guidance Department can propose optimal countermeasures based on past regulatory compliance history. For example, the Regulatory Compliance Guidance Department can analyze past regulatory compliance history and propose countermeasures that minimize risk. For example, the Regulatory Compliance Guidance Department can propose long-term countermeasures based on past regulatory compliance history. This makes it possible to propose optimal countermeasures based on past regulatory compliance history.

[0090] The regulatory compliance guidance unit can estimate the user's emotions and prioritize regulatory compliance guidance based on those emotions. For example, if the user is stressed, the regulatory compliance guidance unit can prioritize simple and easy-to-implement countermeasures. For example, if the user is relaxed, the regulatory compliance guidance unit can prioritize detailed countermeasures and provide an action plan. For example, if the user is in a hurry, the regulatory compliance guidance unit can prioritize countermeasures that can be implemented quickly. This makes it possible to prioritize regulatory compliance guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The Regulatory Guidance Department can provide guidance that takes into account the regional characteristics of regulatory compliance. For example, the Regulatory Guidance Department can provide optimal guidance for each region, taking into account the regional characteristics of regulatory compliance. For example, the Regulatory Guidance Department can analyze the regional characteristics of regulatory compliance and provide guidance that minimizes regional risks. For example, the Regulatory Guidance Department can provide long-term guidance for each region based on the regional characteristics of regulatory compliance. This makes it possible to provide regulatory compliance guidance that takes regional characteristics into account.

[0092] The Regulatory Compliance Guidance Department can refer to regulatory compliance case studies from other companies and propose optimal countermeasures. For example, the Regulatory Compliance Guidance Department can propose similar countermeasures based on the regulatory compliance case studies of other companies. For example, the Regulatory Compliance Guidance Department can analyze the regulatory compliance case studies of other companies and propose countermeasures suitable for the company. For example, the Regulatory Compliance Guidance Department can refer to the regulatory compliance case studies of other companies and propose customized countermeasures. This makes it possible to propose optimal countermeasures based on the regulatory compliance case studies of other companies.

[0093] The supply chain CO2 analysis unit can estimate the user's emotions and adjust the display method of the CO2 analysis results based on the estimated user emotions. For example, if the user is stressed, the supply chain CO2 analysis unit can provide a simple and visually easy-to-understand display method. For example, if the user is relaxed, the supply chain CO2 analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the supply chain CO2 analysis unit can provide a display method that gets straight to the point. This makes it possible to adjust the display method of the CO2 analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The Supply Chain CO2 Analysis Department can collect detailed CO2 emission data from the entire supply chain and incorporate it into its analysis. For example, the Supply Chain CO2 Analysis Department can collect CO2 emission data from the entire supply chain and perform detailed analysis. For example, the Supply Chain CO2 Analysis Department can collect CO2 emission data from each stage of the supply chain and incorporate it into its analysis. For example, the Supply Chain CO2 Analysis Department can collect CO2 emission data from the entire supply chain in real time and incorporate it into its analysis. This makes it possible to collect detailed CO2 emission data from the entire supply chain and incorporate it into its analysis.

[0095] The Supply Chain CO2 Analysis Department can analyze historical CO2 emission data and propose optimal reduction measures. For example, the Supply Chain CO2 Analysis Department can propose optimal reduction measures based on historical CO2 emission data. For example, the Supply Chain CO2 Analysis Department can analyze historical CO2 emission data and propose reduction measures that minimize risk. For example, the Supply Chain CO2 Analysis Department can propose long-term reduction measures based on historical CO2 emission data. This makes it possible to propose optimal reduction measures based on historical CO2 emission data.

[0096] The supply chain CO2 analysis unit can estimate user emotions and prioritize CO2 analysis based on those emotions. For example, if a user is stressed, the supply chain CO2 analysis unit can prioritize simple and easy-to-implement analyses. For example, if a user is relaxed, the supply chain CO2 analysis unit can prioritize detailed analyses and provide action plans. For example, if a user is in a hurry, the supply chain CO2 analysis unit can prioritize analyses that can be quickly implemented. This makes it possible to prioritize CO2 analysis according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The Supply Chain CO2 Analysis Department can perform CO2 analysis while considering the geographical conditions of the supply chain. For example, the Supply Chain CO2 Analysis Department can analyze CO2 emission data for each region while considering the geographical conditions of the supply chain. For example, the Supply Chain CO2 Analysis Department can analyze the geographical conditions of the supply chain and propose CO2 reduction measures that minimize regional risks. For example, the Supply Chain CO2 Analysis Department can propose long-term CO2 reduction measures for each region based on the geographical conditions of the supply chain. This enables CO2 analysis that takes into account the geographical conditions of the supply chain.

[0098] The Supply Chain CO2 Analysis Department can refer to CO2 reduction case studies from other companies and propose optimal reduction measures. For example, the Supply Chain CO2 Analysis Department can propose similar reduction measures based on the CO2 reduction case studies of other companies. For example, the Supply Chain CO2 Analysis Department can analyze the CO2 reduction case studies of other companies and propose reduction measures suitable for one's own company. For example, the Supply Chain CO2 Analysis Department can refer to the CO2 reduction case studies of other companies and propose customized reduction measures. This makes it possible to propose optimal reduction measures based on the CO2 reduction case studies of other companies.

[0099] The Green Procurement Support Department can estimate the user's emotions and adjust the content of green procurement proposals based on those emotions. For example, if the user is stressed, the Green Procurement Support Department can offer simple and easy-to-implement green procurement proposals. For example, if the user is relaxed, the Green Procurement Support Department can offer detailed green procurement proposals and provide an implementation plan. For example, if the user is in a hurry, the Green Procurement Support Department can offer quickly actionable green procurement proposals. This makes it possible to adjust the content of green procurement proposals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The Green Procurement Support Department can analyze past procurement data and propose the optimal green procurement plan. For example, based on past procurement data, the Green Procurement Support Department can propose the most environmentally friendly procurement plan. For example, based on past procurement data, the Green Procurement Support Department can propose a procurement plan that minimizes risk. For example, based on past procurement data, the Green Procurement Support Department can propose a procurement plan that anticipates long-term environmental considerations. This makes it possible to propose the optimal green procurement plan based on past procurement data.

[0101] The Green Procurement Support Department can conduct a detailed assessment of the environmental impact of the procured goods and reflect it in the proposal. For example, the Green Procurement Support Department can assess the CO2 emissions of the procured goods and reflect it in the proposal. For example, the Green Procurement Support Department can assess the energy consumption of the procured goods and reflect it in the proposal. For example, the Green Procurement Support Department can comprehensively assess the environmental burden of the procured goods and reflect it in the proposal. This makes it possible to provide proposals that have a detailed assessment of the environmental impact of the procured goods.

[0102] The Green Procurement Support Department can estimate a user's emotions and determine green procurement priorities based on those emotions. For example, if a user is stressed, the Green Procurement Support Department can prioritize simple and easy-to-implement procurement plans. If a user is relaxed, the Green Procurement Support Department can prioritize detailed procurement plans and provide implementation plans. If a user is in a hurry, the Green Procurement Support Department can prioritize procurement plans that can be quickly implemented. This makes it possible to determine green procurement priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The Green Procurement Support Department can propose green procurement while considering the geographical conditions of the procurement target. For example, the Green Procurement Support Department can propose green procurement while considering the energy costs of the procurement target region. For example, the Green Procurement Support Department can propose green procurement while considering the environmental regulations of the procurement target region. For example, the Green Procurement Support Department can propose green procurement while considering the climatic conditions of the procurement target region. This makes it possible to propose green procurement that takes into account the geographical conditions of the procurement target.

[0104] The Green Procurement Support Department can propose optimal procurement plans by referring to green procurement case studies from other companies. For example, the Green Procurement Support Department can propose similar procurement plans based on the green procurement case studies of other companies. For example, the Green Procurement Support Department can analyze the green procurement case studies of other companies and propose procurement plans suitable for your own company. For example, the Green Procurement Support Department can propose customized procurement plans by referencing the green procurement case studies of other companies. This makes it possible to propose optimal procurement plans based on the green procurement case studies of other companies.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The AI ​​energy-saving measure proposal system can estimate the user's emotions and adjust the proposed energy-saving measures based on those emotions. For example, if the user is stressed, it can propose simple and easy-to-implement energy-saving measures. If the user is relaxed, it can propose detailed energy-saving measures and provide an implementation plan. If the user is in a hurry, it can propose energy-saving measures that can be implemented quickly. This makes it possible to propose energy-saving measures that are tailored to the user's emotions.

[0107] The environmental investment ROI calculation unit can estimate the user's emotions and adjust how the ROI calculation results are displayed based on those emotions. For example, if the user is stressed, a simple and visually easy-to-understand display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. This makes it possible to adjust how the ROI calculation results are displayed according to the user's emotions.

[0108] The regulatory compliance guidance unit can estimate the user's emotions and adjust the content of the guidance based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-follow guidance. If the user is relaxed, it can provide detailed guidance and an action plan. If the user is in a hurry, it can provide guidance that can be quickly implemented. This makes it possible to adjust the content of the regulatory compliance guidance according to the user's emotions.

[0109] The supply chain CO2 analysis department can estimate user emotions and adjust the display method of CO2 analysis results based on those estimated emotions. For example, if a user is stressed, a simple and visually easy-to-understand display method can be provided. If a user is relaxed, a display method including detailed information can be provided. If a user is in a hurry, a display method that gets straight to the point can be provided. This makes it possible to adjust the display method of CO2 analysis results according to the user's emotions.

[0110] The Green Procurement Support Department can estimate the user's emotions and adjust the content of green procurement proposals based on those emotions. For example, if the user is stressed, it can offer simple and easy-to-implement green procurement proposals. If the user is relaxed, it can offer detailed green procurement proposals and provide an implementation plan. If the user is in a hurry, it can offer green procurement proposals that can be quickly implemented. This makes it possible to adjust the content of green procurement proposals according to the user's emotions.

[0111] The AI ​​Energy Saving Measure Proposal Department can analyze past energy consumption data and propose optimal energy saving measures. For example, it can propose measures to reduce peak energy consumption based on past energy consumption data. It can propose measures to reduce wasteful energy consumption by analyzing past energy consumption data. It can propose measures that take into account seasonal energy consumption patterns based on past energy consumption data. This makes it possible to propose optimal energy saving measures based on past energy consumption data.

[0112] The AI ​​Energy Saving Measure Proposal Department can monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected. For example, it can monitor the operating status of machinery in real time and automatically propose measures to stop operation when an abnormality is detected. It can monitor the operating status of machinery in real time and automatically propose measures to minimize energy consumption when an abnormality is detected. It can monitor the operating status of machinery in real time and automatically propose measures to perform maintenance when an abnormality is detected. This makes it possible to monitor the operating status of machinery in real time and automatically propose energy-saving measures when an abnormality is detected.

[0113] The Regulatory Guidance Department can collect the latest environmental regulatory information in real time and reflect it in the guidance. For example, it can collect the latest environmental regulatory information in real time and reflect it in the guidance. If there are changes to environmental regulations, the guidance can be updated in real time. If new information on environmental regulations becomes available, it can be immediately reflected in the guidance. This makes it possible to collect the latest environmental regulatory information in real time and reflect it in the guidance.

[0114] The Supply Chain CO2 Analysis Department can analyze historical CO2 emission data and propose optimal reduction measures. For example, it can propose optimal reduction measures based on historical CO2 emission data. It can analyze historical CO2 emission data and propose reduction measures that minimize risk. It can propose long-term reduction measures based on historical CO2 emission data. This makes it possible to propose optimal reduction measures based on historical CO2 emission data.

[0115] The Green Procurement Support Department can conduct detailed assessments of the environmental impact of procurement items and reflect them in proposals. For example, it can assess the CO2 emissions of procurement items and reflect them in proposals. It can assess the energy consumption of procurement items and reflect them in proposals. It can comprehensively assess the environmental burden of procurement items and reflect them in proposals. This makes it possible to make proposals that have a detailed assessment of the environmental impact of procurement items.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The AI ​​Energy Saving Measures Proposal Department proposes measures to optimize energy consumption. For example, in the manufacturing industry, it proposes reducing energy consumption by optimizing machine operating times. Specifically, energy consumption can be reduced by scheduling machine operating times or using algorithms that improve operational efficiency. Step 2: The Environmental Investment ROI Calculation Unit calculates the return on environmental investment based on the measures proposed by the AI ​​Energy Saving Measure Proposal Unit. For example, it calculates the ROI of equipment investments for energy efficiency for office building managers, supporting their investment decision-making. Specifically, it can calculate the energy cost reduction effect, CO2 emission reduction effect, and environmental load reduction effect relative to the investment amount. Step 3: The Regulatory Compliance Guidance Department provides guidance on complying with the latest environmental regulations based on the returns calculated by the Environmental Investment ROI Calculation Department. For example, it proposes compliance measures to logistics companies based on the latest environmental regulations. Specifically, it can provide compliance with emission standards, preparation of environmental reports, and implementation plans for compliance measures based on environmental regulations. Step 4: The Supply Chain CO2 Analysis Department analyzes CO2 emissions across the entire supply chain based on guidance provided by the Regulatory Compliance Guidance Department. For example, it visualizes CO2 emissions from the entire regional supply chain for local governments and proposes measures for reduction. Specifically, it can propose methods for collecting data from each company, methods for aggregating emissions, and criteria for calculating CO2 emissions. Step 5: The Green Procurement Support Department will support environmentally conscious procurement based on the CO2 emissions analyzed by the Supply Chain CO2 Analysis Department. For example, it will assist ESG investors in selecting environmentally conscious companies. Specifically, it can evaluate the use of renewable energy, acquisition of eco-labels, and the presence of environmental certifications.

[0118] 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.

[0119] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0120] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the AI ​​energy-saving measure proposal unit, the environmental investment ROI calculation unit, the regulatory compliance guidance unit, the supply chain CO2 analysis unit, and the green procurement support unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the AI ​​energy-saving measure proposal unit is implemented by the control unit 46A of the smart device 14 and proposes measures to optimize energy consumption. The environmental investment ROI calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the return on environmental investment. The regulatory compliance guidance unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides guidance to comply with the latest environmental regulations. The supply chain CO2 analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes CO2 emissions across the entire supply chain. The green procurement support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports environmentally conscious procurement. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0125] 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.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0127] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] 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.

[0129] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the AI ​​energy-saving measure proposal unit, the environmental investment ROI calculation unit, the regulatory compliance guidance unit, the supply chain CO2 analysis unit, and the green procurement support unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the AI ​​energy-saving measure proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes measures to optimize energy consumption. The environmental investment ROI calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the return on environmental investment. The regulatory compliance guidance unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides guidance to comply with the latest environmental regulations. The supply chain CO2 analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes CO2 emissions across the entire supply chain. The green procurement support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports environmentally conscious procurement. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0143] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] 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.

[0145] 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.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the AI ​​energy-saving measure proposal unit, the environmental investment ROI calculation unit, the regulatory compliance guidance unit, the supply chain CO2 analysis unit, and the green procurement support unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the AI ​​energy-saving measure proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes measures to optimize energy consumption. The environmental investment ROI calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the return on environmental investment. The regulatory compliance guidance unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides guidance to comply with the latest environmental regulations. The supply chain CO2 analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes CO2 emissions across the entire supply chain. The green procurement support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports environmentally conscious procurement. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0157] 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.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0159] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] 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.

[0161] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0162] 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.

[0163] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the AI ​​energy-saving measure proposal unit, the environmental investment ROI calculation unit, the regulatory compliance guidance unit, the supply chain CO2 analysis unit, and the green procurement support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the AI ​​energy-saving measure proposal unit is implemented by the control unit 46A of the robot 414 and proposes measures to optimize energy consumption. The environmental investment ROI calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the return on environmental investment. The regulatory compliance guidance unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides guidance to comply with the latest environmental regulations. The supply chain CO2 analysis unit is implemented by, for example, the control unit 46A of the robot 414 and analyzes CO2 emissions across the entire supply chain. The green procurement support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports environmentally conscious procurement. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0171] 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.

[0172] Figure 9 shows the 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.

[0173] 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.

[0174] 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.

[0175] 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, and motorcycles, 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 based, for example, 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.

[0176] 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."

[0177] 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.

[0178] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0187] 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 other things 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.

[0188] 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.

[0189] (Note 1) The AI ​​Energy Saving Measures Proposal Department proposes measures to optimize energy consumption, An environmental investment ROI calculation unit calculates the return on environmental investment based on the measures proposed by the aforementioned AI energy-saving measure proposal unit, A regulatory compliance guidance unit provides guidance for complying with the latest environmental regulations based on the return calculated by the aforementioned environmental investment ROI calculation unit, The Supply Chain CO2 Analysis Department analyzes CO2 emissions across the entire supply chain based on the guidance provided by the Regulatory Compliance Guidance Department, The system includes a Green Procurement Support Department that supports environmentally conscious procurement based on CO2 emissions analyzed by the aforementioned Supply Chain CO2 Analysis Department. A system characterized by the following features. (Note 2) The aforementioned AI energy-saving measures proposal department, We propose reducing energy consumption in the manufacturing industry by optimizing machine operating times. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned environmental investment ROI calculation unit is: We help office building managers calculate the ROI of investments in energy efficiency equipment and support their investment decisions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned regulatory compliance guidance department, We propose countermeasures to logistics companies based on the latest environmental regulations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply chain CO2 analysis department, We will visualize CO2 emissions from the entire regional supply chain for local governments and propose measures to reduce them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Green Procurement Support Department Supporting ESG investors in selecting environmentally conscious companies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned AI energy-saving measures proposal department, The system estimates the user's emotions and adjusts the proposed energy-saving measures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned AI energy-saving measures proposal department, We analyze past energy consumption data and propose optimal energy-saving measures. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned AI energy-saving measures proposal department, The system monitors the machine's operating status in real time and automatically proposes energy-saving measures if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned AI energy-saving measures proposal department, The system estimates user emotions and prioritizes energy-saving measures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned AI energy-saving measures proposal department, We propose energy-saving measures that take into account seasonal fluctuations in energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned AI energy-saving measures proposal department, We will refer to successful energy-saving measures from other companies and propose the most suitable measures. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned environmental investment ROI calculation unit is: Adjusting how we estimate user sentiment and display ROI calculation results based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned environmental investment ROI calculation unit is: We analyze past investment data and propose the optimal investment plan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned environmental investment ROI calculation unit is: The environmental impact of the investment target will be assessed in detail and reflected in the ROI calculation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned regulatory compliance guidance department, We estimate user sentiment and adjust the content of regulatory compliance guidance based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned regulatory compliance guidance department, We collect the latest environmental regulation information in real time and reflect it in our guidance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned regulatory compliance guidance department, We analyze past regulatory compliance history and propose the optimal response. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned regulatory compliance guidance department, We estimate user sentiment and prioritize regulatory compliance guidance based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned regulatory compliance guidance department, Providing guidance that takes into account the regional characteristics of regulatory compliance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned regulatory compliance guidance department, We will refer to regulatory compliance examples from other companies and propose the most suitable countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply chain CO2 analysis department, The system estimates the user's emotions and adjusts how the CO2 analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply chain CO2 analysis department, We collect detailed CO2 emission data from the entire supply chain and incorporate it into our analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply chain CO2 analysis department, We analyze past CO2 emission data and propose optimal reduction measures. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply chain CO2 analysis department, The system estimates user sentiment and prioritizes CO2 analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply chain CO2 analysis department, CO2 analysis will be conducted taking into account the geographical conditions of the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply chain CO2 analysis department, We will refer to CO2 reduction case studies from other companies and propose the most suitable reduction measures. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Green Procurement Support Department The system estimates user sentiment and adjusts green procurement proposals based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Green Procurement Support Department We analyze past procurement data and propose the optimal green procurement plan. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Green Procurement Support Department We will thoroughly assess the environmental impact of the procurement items and reflect this in our proposals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Green Procurement Support Department We estimate user sentiment and determine green procurement priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Green Procurement Support Department We propose green procurement, taking into account the geographical conditions of the procurement target. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Green Procurement Support Department We will refer to green procurement case studies from other companies and propose the optimal procurement plan. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The AI ​​Energy Saving Measures Proposal Department proposes measures to optimize energy consumption, An environmental investment ROI calculation unit calculates the return on environmental investment based on the measures proposed by the AI ​​energy-saving measure proposal unit, A regulatory compliance guidance unit provides guidance for complying with the latest environmental regulations based on the return calculated by the aforementioned environmental investment ROI calculation unit, The Supply Chain CO2 Analysis Department analyzes CO2 emissions across the entire supply chain based on the guidance provided by the Regulatory Compliance Guidance Department, The system includes a Green Procurement Support Department that supports environmentally conscious procurement based on CO2 emissions analyzed by the aforementioned Supply Chain CO2 Analysis Department. A system characterized by the following features.

2. The aforementioned AI energy-saving measures proposal unit, We propose reducing energy consumption in the manufacturing industry by optimizing machine operating times. The system according to feature 1.

3. The aforementioned environmental investment ROI calculation unit is: We help office building managers calculate the ROI of investments in energy efficiency equipment and support their investment decisions. The system according to feature 1.

4. The aforementioned regulatory compliance guidance section, We propose countermeasures to logistics companies based on the latest environmental regulations. The system according to feature 1.

5. The aforementioned supply chain CO2 analysis department, We will visualize CO2 emissions from the entire regional supply chain for local governments and propose measures to reduce them. The system according to feature 1.

6. The aforementioned Green Procurement Support Department Supporting ESG investors in selecting environmentally conscious companies. The system according to feature 1.

7. The aforementioned AI energy-saving measures proposal unit, The system estimates the user's emotions and adjusts the proposed energy-saving measures based on those estimated emotions. The system according to feature 1.

8. The aforementioned AI energy-saving measures proposal unit, We analyze past energy consumption data and propose optimal energy-saving measures. The system according to feature 1.

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