System

Abstract

An object of the system according to the embodiment is to allow a company to optimally invest in renewable energy and optimize energy consumption.SOLUTION: A system according to an embodiment includes an Internet of Things (IoT) device, a generation AI, a cloud storage unit, and an investment plan providing unit. The IoT device collects energy consumption data. The generation AI analyzes the energy consumption collected by the IoT devices. The cloud storage unit stores the AI analyzed by the generation unit in the cloud. The investment proposal providing unit provides an optimal renewable energy investment proposal based on the data stored in the cloud storage unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for companies to find optimal renewable energy investment opportunities, and energy consumption was not adequately optimized.

[0005] The system according to the embodiment aims to enable companies to make optimal investments in renewable energy and optimize energy consumption. [Means for solving the problem]

[0006] A system according to an embodiment includes an IoT device, a generation AI, a cloud storage unit, and an investment proposal providing unit. The IoT device collects energy consumption data. The generation AI analyzes the energy consumption data collected by the IoT device. The cloud storage unit stores the data analyzed by the generation AI in the cloud. The investment proposal providing unit provides an optimal renewable energy investment proposal based on the data stored in the cloud storage unit. [Effects of the Invention]

[0007] The system according to the embodiment enables companies to make optimal investments in renewable energy and optimize energy consumption. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The Green Flow Strategic Advisor according to an embodiment of the present invention is a system that provides detailed information on a company's energy usage and CO2 emissions, and provides guidance on optimal renewable energy initiatives and investments. This enables the Green Flow Strategic Advisor to support companies in efficiently reducing CO2 emissions and transitioning to sustainable business models.

[0029] A green flow strategic advisor according to an embodiment includes an IoT device, a generation AI, a cloud storage unit, and an investment proposal providing unit. The IoT device collects energy consumption data. For example, sensors are attached to each machine in a factory to monitor its operating status and energy consumption. Sensors are also installed on each floor of an office building to monitor lighting and air conditioning usage. The generation AI analyzes the energy consumption data collected by the IoT device. For example, the generation AI analyzes the energy consumption data using a text generation AI (e.g., LLM) and proposes optimal energy usage methods. The generation AI can also analyze the energy consumption data using a multimodal generation AI. The cloud storage unit stores the data analyzed by the generation AI in the cloud. For example, the cloud storage unit stores the data in a database and implements security measures. The investment proposal providing unit provides optimal renewable energy investment proposals based on the data stored in the cloud storage unit. For example, the investment proposal providing unit proposes the introduction of optimal solar power generation systems or wind power generation systems based on the company's location and energy consumption patterns. The investment proposal unit also evaluates the risks and returns of investments and proposes the most effective investment strategies for companies. As a result, the Green Flow Strategic Advisor according to the embodiment can grasp the company's energy usage and CO2 emissions in detail, and provide optimal renewable energy initiatives and investment guidance.

[0030] IoT devices can be equipped with sensors on each machine in a factory to monitor its operating status and energy consumption. For example, IoT devices can be equipped with sensors on each machine in a factory to monitor its operating status and energy consumption. For example, data collected from IoT devices can be analyzed to identify peak energy consumption periods. For example, the system can find the time periods with the highest energy consumption based on the factory's operating hours and office usage. It can also propose ways to optimize energy use during peak hours. For example, it can adjust the operation of machines during peak hours to distribute energy consumption. It can also identify peak energy consumption periods and propose specific measures to optimize energy use during those periods. For example, it can limit the use of lighting and air conditioning during peak hours. This makes it possible to monitor a factory's energy consumption in detail and propose optimal energy use.

[0031] IoT devices can be used to monitor the usage of lighting and air conditioning by installing sensors on each floor of an office building. For example, IoT devices can be used to monitor the usage of lighting and air conditioning. For example, data collected from each IoT device can be integrated to develop an algorithm for detecting anomalies in energy consumption. For example, an anomaly can be detected when there is a deviation from the normal energy consumption pattern. In addition, a system can be built to notify in real time when an anomaly is detected. For example, an alert can be sent to an administrator when an abnormality occurs. In addition, an algorithm can be developed to detect anomalies in energy consumption and specific measures can be proposed to notify in real time when an abnormality is detected. For example, a system can be introduced that automatically takes measures when an abnormality occurs. This makes it possible to monitor the energy consumption of an office building in detail and propose optimal energy usage.

[0032] Generative AI can propose the introduction of solar power generation or wind power generation and simulate its effects. Generative AI, for example, proposes the introduction of solar power generation or wind power generation and simulates its effects. For example, it uses the emotion estimation function to associate employee emotion data with energy consumption data and optimize energy consumption during times when employees' stress levels are high. For example, it uses the emotion estimation function to collect employee emotion data and associate it with energy consumption data. For example, it identifies times when employees' stress levels are high. It also proposes specific measures to optimize energy consumption during times when employees' stress levels are high. For example, it adjusts the use of lighting and air conditioning during times when stress levels are high. It also uses the emotion estimation function to associate employee emotion data with energy consumption data and builds a system to optimize energy consumption during times when employees' stress levels are high. For example, it automatically adjusts energy use during times when stress levels are high. This allows the effects of introducing renewable energy to be simulated and optimal introduction proposals to be made.

[0033] Generative AI can propose specific measures to improve energy efficiency and reduce CO2 emissions. Generative AI can propose specific measures to improve energy efficiency and reduce CO2 emissions. For example, using IoT devices to simultaneously monitor not only energy consumption but also water and gas usage, thereby performing comprehensive resource management. For example, using IoT devices to build a system that simultaneously monitors not only energy consumption but also water and gas usage. For example, sensors can be installed in each facility in a factory or office building. A database can also be built to comprehensively manage energy consumption, water, and gas usage. For example, each data can be stored in the cloud and made accessible in real time. Specific measures for comprehensive resource management can also be proposed. For example, an algorithm can be developed to optimize energy consumption, water, and gas usage. This can then be used to propose specific measures to improve energy efficiency and reduce CO2 emissions.

[0034] Generative AI can propose the optimal solar power generation system or wind power generation system to be installed based on a company's location and energy consumption patterns. Generative AI can propose the optimal solar power generation system or wind power generation system to be installed based on a company's location and energy consumption patterns. For example, a mobile app can be developed to allow a company's energy consumption data to be checked in real time. For example, a mobile app can be developed to allow a company's energy consumption data to be checked in real time. For example, an app that can be accessed from a smartphone or tablet can be provided. A system can also be built to monitor energy consumption data in real time through the mobile app. For example, data from each device can be stored in the cloud and displayed in the app. Specific measures can also be proposed to enable a company's energy consumption data to be checked in real time using the mobile app. For example, an alert function can be provided within the app to notify when an abnormality occurs. This makes it possible to propose the optimal renewable energy system to be installed based on a company's location and energy consumption patterns.

[0035] Generative AI can evaluate the risks and returns of investments and propose the most effective investment strategy for a company. For example, generative AI can evaluate the risks and returns of investments and propose the most effective investment strategy for a company. For example, installing IoT devices equipped with emotion estimation functionality can optimize energy consumption according to employees' emotional states. For example, installing IoT devices equipped with emotion estimation functionality can monitor employees' emotional states in real time. For example, emotions can be estimated using facial recognition technology or voice analysis technology. It can also propose specific measures to optimize energy consumption according to employees' emotional states. For example, adjusting the use of lighting and air conditioning when stress levels are high. It can also use IoT devices equipped with emotion estimation functionality to build a system that optimizes energy consumption according to employees' emotional states. For example, it can automatically adjust energy use based on emotional data. This makes it possible to propose the most effective investment strategy for a company.

[0036] Generative AI can provide information on government subsidies and tax incentives, making it easier for companies to invest in renewable energy. Generative AI can provide information on government subsidies and tax incentives, making it easier for companies to invest in renewable energy. For example, it can clarify the specific content and eligibility conditions of government subsidies and tax incentives, such as the type of subsidy, application procedures, and eligibility conditions. It can also propose specific measures to make it easier for companies to invest in renewable energy. For example, it can support subsidy application procedures and confirm the eligibility conditions for tax incentives. This makes it possible to provide information that makes it easier for companies to invest in renewable energy.

[0037] Generative AI can reduce a company's environmental impact by improving energy efficiency and introducing renewable energy. Generative AI can reduce a company's environmental impact, for example, by improving energy efficiency and introducing renewable energy. For example, it clarifies specific methods and standards for assessing environmental impact, such as CO2 emissions, water usage, and waste volume. It can also propose specific measures to reduce a company's environmental impact, such as the use of renewable energy, improving energy efficiency, and emissions trading. This allows it to propose specific measures to reduce a company's environmental impact.

[0038] Generative AI can evaluate a company's environmental performance based on the data it provides and support the transition to a sustainable business model. Generative AI can, for example, evaluate a company's environmental performance based on the data it provides and support the transition to a sustainable business model. For example, it can clarify specific evaluation methods and criteria for environmental performance, such as energy efficiency, emission reductions, and sustainability indicators. It can also propose specific measures to support the transition to a sustainable business model, such as circular economy, green business, and social responsibility. This allows it to evaluate a company's environmental performance and support the transition to a sustainable business model.

[0039] Generative AI can visualize a company's environmental performance and report it to stakeholders in a transparent manner. Generative AI can, for example, visualize a company's environmental performance and report it to stakeholders in a transparent manner. For example, it can clarify specific visualization methods and tools, such as dashboards, graphs, and reports. It can also clarify specific transparency standards and evaluation methods, such as the scope of information disclosure, data accuracy, and reporting frequency. This makes it possible to visualize a company's environmental performance and report it to stakeholders in a transparent manner.

[0040] Generative AI can promote the sustainable use of energy by introducing renewable energy and contribute to climate change countermeasures. Generative AI can promote the sustainable use of energy by introducing renewable energy and contribute to climate change countermeasures. For example, it can clarify specific standards and evaluation methods for sustainable use, such as the proportion of renewable energy, efficient use of resources, and long-term sustainability. It can also clarify specific measures and evaluation criteria for climate change countermeasures, such as greenhouse gas reduction targets, adaptation measures, and international cooperation. This can promote the sustainable use of energy by introducing renewable energy and contribute to climate change countermeasures.

[0041] Generative AI can achieve efficient resource use and promote sustainable production and consumption by improving energy efficiency. Generative AI can achieve efficient resource use and promote sustainable production and consumption by improving energy efficiency. For example, it can clarify specific measures and evaluation criteria for efficient resource use, such as recycling rates, energy efficiency, and waste reduction. It can also clarify specific measures and evaluation criteria for sustainable production and consumption, such as eco-design, green procurement, and consumer education. This can achieve efficient resource use and promote sustainable production and consumption by improving energy efficiency.

[0042] By improving a company's environmental performance, generative AI can fulfill its social responsibility and contribute to the realization of a sustainable society. Generative AI can, for example, fulfill its social responsibility and contribute to the realization of a sustainable society by improving the company's environmental performance. For example, it can clarify the specific content and evaluation criteria of social responsibility, such as CSR activities, social contribution, and stakeholder involvement. It can also clarify the specific definition and evaluation criteria of a sustainable society, such as environmental protection, social equity, and economic sustainability. This allows a company to fulfill its social responsibility and contribute to the realization of a sustainable society by improving its environmental performance.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] Green Flow Strategic Advisor can also provide a dashboard that visualizes a company's energy usage in real time. For example, it can display energy consumption trends and peak times in graphs, allowing companies to immediately optimize their energy usage. It can also add a function that issues alerts when abnormalities in energy consumption are detected. For example, it can send a notification to the administrator if there is a deviation from normal consumption patterns. Furthermore, it can predict future energy demand based on energy consumption data and propose optimal energy management plans. This allows companies to improve the efficiency of their energy use, reduce costs, and reduce their environmental impact.

[0045] IoT devices can also add environmental sensors to collect environmental data such as temperature, humidity, and air quality within a factory. For example, a temperature sensor can be used to monitor the temperature within a factory and manage air conditioning appropriately. A humidity sensor can also be used to manage humidity and maintain product quality. Furthermore, an air quality sensor can be used to monitor the air quality within the factory and take measures to protect the health of employees. This allows for comprehensive management of the factory environment, optimizing energy consumption while maintaining the health of employees.

[0046] IoT devices can also analyze employee movement patterns based on data collected from sensors installed on each floor of an office building. For example, they can analyze employee movement patterns and propose efficient layouts. They can also identify congested areas and implement measures to provide a comfortable working environment. Furthermore, they can optimize energy consumption based on employee movement data. For example, they can automatically adjust lighting and air conditioning in less frequently used areas. This can improve the efficiency of energy consumption in office buildings and create a comfortable working environment for employees.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: IoT devices collect energy consumption data. For example, sensors can be attached to each machine in a factory to monitor its operation status and energy consumption, or sensors can be installed on each floor of an office building to monitor lighting and air conditioning usage. Step 2: Generative AI analyzes the energy consumption data collected by IoT devices. For example, text-generative AI (e.g., LLM) can be used to analyze the energy consumption data and suggest optimal energy usage methods. Multimodal generative AI can also be used to analyze the energy consumption data. Step 3: The cloud storage unit stores the data analyzed by the generating AI in the cloud. For example, the cloud storage unit stores the data in a database and implements security measures. Step 4: The investment proposal provider provides optimal renewable energy investment proposals based on the data stored in the cloud storage. For example, it proposes the optimal solar power generation system or wind power generation system installation based on the company's location and energy consumption pattern. It also evaluates the investment risks and returns and proposes the most effective investment strategy for the company.

[0049] (Example 2) The Green Flow Strategic Advisor according to an embodiment of the present invention is a system that provides detailed information on a company's energy usage and CO2 emissions, and provides guidance on optimal renewable energy initiatives and investments. This enables the Green Flow Strategic Advisor to support companies in efficiently reducing CO2 emissions and transitioning to sustainable business models.

[0050] A green flow strategic advisor according to an embodiment includes an IoT device, a generation AI, a cloud storage unit, and an investment proposal providing unit. The IoT device collects energy consumption data. For example, sensors are attached to each machine in a factory to monitor its operating status and energy consumption. Sensors are also installed on each floor of an office building to monitor lighting and air conditioning usage. The generation AI analyzes the energy consumption data collected by the IoT device. For example, the generation AI analyzes the energy consumption data using a text generation AI (e.g., LLM) and proposes optimal energy usage methods. The generation AI can also analyze the energy consumption data using a multimodal generation AI. The cloud storage unit stores the data analyzed by the generation AI in the cloud. For example, the cloud storage unit stores the data in a database and implements security measures. The investment proposal providing unit provides optimal renewable energy investment proposals based on the data stored in the cloud storage unit. For example, the investment proposal providing unit proposes the introduction of optimal solar power generation systems or wind power generation systems based on the company's location and energy consumption patterns. The investment proposal unit also evaluates the risks and returns of investments and proposes the most effective investment strategies for companies. As a result, the Green Flow Strategic Advisor according to the embodiment can grasp the company's energy usage and CO2 emissions in detail, and provide optimal renewable energy initiatives and investment guidance.

[0051] IoT devices can be equipped with sensors on each machine in a factory to monitor its operating status and energy consumption. For example, IoT devices can be equipped with sensors on each machine in a factory to monitor its operating status and energy consumption. For example, data collected from IoT devices can be analyzed to identify peak energy consumption periods. For example, the system can find the time periods with the highest energy consumption based on the factory's operating hours and office usage. It can also propose ways to optimize energy use during peak hours. For example, it can adjust the operation of machines during peak hours to distribute energy consumption. It can also identify peak energy consumption periods and propose specific measures to optimize energy use during those periods. For example, it can limit the use of lighting and air conditioning during peak hours. This makes it possible to monitor a factory's energy consumption in detail and propose optimal energy use.

[0052] IoT devices can be used to monitor the usage of lighting and air conditioning by installing sensors on each floor of an office building. For example, IoT devices can be used to monitor the usage of lighting and air conditioning. For example, data collected from each IoT device can be integrated to develop an algorithm for detecting anomalies in energy consumption. For example, an anomaly can be detected when there is a deviation from the normal energy consumption pattern. In addition, a system can be built to notify in real time when an anomaly is detected. For example, an alert can be sent to an administrator when an abnormality occurs. In addition, an algorithm can be developed to detect anomalies in energy consumption and specific measures can be proposed to notify in real time when an abnormality is detected. For example, a system can be introduced that automatically takes measures when an abnormality occurs. This makes it possible to monitor the energy consumption of an office building in detail and propose optimal energy usage.

[0053] Generative AI can propose the introduction of solar power generation or wind power generation and simulate its effects. Generative AI, for example, proposes the introduction of solar power generation or wind power generation and simulates its effects. For example, it uses the emotion estimation function to associate employee emotion data with energy consumption data and optimize energy consumption during times when employees' stress levels are high. For example, it uses the emotion estimation function to collect employee emotion data and associate it with energy consumption data. For example, it identifies times when employees' stress levels are high. It also proposes specific measures to optimize energy consumption during times when employees' stress levels are high. For example, it adjusts the use of lighting and air conditioning during times when stress levels are high. It also uses the emotion estimation function to associate employee emotion data with energy consumption data and builds a system to optimize energy consumption during times when employees' stress levels are high. For example, it automatically adjusts energy use during times when stress levels are high. This allows the effects of introducing renewable energy to be simulated and optimal introduction proposals to be made.

[0054] Generative AI can propose specific measures to improve energy efficiency and reduce CO2 emissions. Generative AI can propose specific measures to improve energy efficiency and reduce CO2 emissions. For example, using IoT devices to simultaneously monitor not only energy consumption but also water and gas usage, thereby performing comprehensive resource management. For example, using IoT devices to build a system that simultaneously monitors not only energy consumption but also water and gas usage. For example, sensors can be installed in each facility in a factory or office building. A database can also be built to comprehensively manage energy consumption, water, and gas usage. For example, each data can be stored in the cloud and made accessible in real time. Specific measures for comprehensive resource management can also be proposed. For example, an algorithm can be developed to optimize energy consumption, water, and gas usage. This can then be used to propose specific measures to improve energy efficiency and reduce CO2 emissions.

[0055] Generative AI can propose the optimal solar power generation system or wind power generation system to be installed based on a company's location and energy consumption patterns. Generative AI can propose the optimal solar power generation system or wind power generation system to be installed based on a company's location and energy consumption patterns. For example, a mobile app can be developed to allow a company's energy consumption data to be checked in real time. For example, a mobile app can be developed to allow a company's energy consumption data to be checked in real time. For example, an app that can be accessed from a smartphone or tablet can be provided. A system can also be built to monitor energy consumption data in real time through the mobile app. For example, data from each device can be stored in the cloud and displayed in the app. Specific measures can also be proposed to enable a company's energy consumption data to be checked in real time using the mobile app. For example, an alert function can be provided within the app to notify when an abnormality occurs. This makes it possible to propose the optimal renewable energy system to be installed based on a company's location and energy consumption patterns.

[0056] Generative AI can evaluate the risks and returns of investments and propose the most effective investment strategy for a company. For example, generative AI can evaluate the risks and returns of investments and propose the most effective investment strategy for a company. For example, installing IoT devices equipped with emotion estimation functionality can optimize energy consumption according to employees' emotional states. For example, installing IoT devices equipped with emotion estimation functionality can monitor employees' emotional states in real time. For example, emotions can be estimated using facial recognition technology or voice analysis technology. It can also propose specific measures to optimize energy consumption according to employees' emotional states. For example, adjusting the use of lighting and air conditioning when stress levels are high. It can also use IoT devices equipped with emotion estimation functionality to build a system that optimizes energy consumption according to employees' emotional states. For example, it can automatically adjust energy use based on emotional data. This makes it possible to propose the most effective investment strategy for a company.

[0057] Generative AI can provide information on government subsidies and tax incentives, making it easier for companies to invest in renewable energy. Generative AI can provide information on government subsidies and tax incentives, making it easier for companies to invest in renewable energy. For example, it can clarify the specific content and eligibility conditions of government subsidies and tax incentives, such as the type of subsidy, application procedures, and eligibility conditions. It can also propose specific measures to make it easier for companies to invest in renewable energy. For example, it can support subsidy application procedures and confirm the eligibility conditions for tax incentives. This makes it possible to provide information that makes it easier for companies to invest in renewable energy.

[0058] Generative AI can reduce a company's environmental impact by improving energy efficiency and introducing renewable energy. Generative AI can reduce a company's environmental impact, for example, by improving energy efficiency and introducing renewable energy. For example, it clarifies specific methods and standards for assessing environmental impact, such as CO2 emissions, water usage, and waste volume. It can also propose specific measures to reduce a company's environmental impact, such as the use of renewable energy, improving energy efficiency, and emissions trading. This allows it to propose specific measures to reduce a company's environmental impact.

[0059] Generative AI can evaluate a company's environmental performance based on the data it provides and support the transition to a sustainable business model. Generative AI can, for example, evaluate a company's environmental performance based on the data it provides and support the transition to a sustainable business model. For example, it can clarify specific evaluation methods and criteria for environmental performance, such as energy efficiency, emission reductions, and sustainability indicators. It can also propose specific measures to support the transition to a sustainable business model, such as circular economy, green business, and social responsibility. This allows it to evaluate a company's environmental performance and support the transition to a sustainable business model.

[0060] Generative AI can visualize a company's environmental performance and report it to stakeholders in a transparent manner. Generative AI can, for example, visualize a company's environmental performance and report it to stakeholders in a transparent manner. For example, it can clarify specific visualization methods and tools, such as dashboards, graphs, and reports. It can also clarify specific transparency standards and evaluation methods, such as the scope of information disclosure, data accuracy, and reporting frequency. This makes it possible to visualize a company's environmental performance and report it to stakeholders in a transparent manner.

[0061] Generative AI can promote the sustainable use of energy by introducing renewable energy and contribute to climate change countermeasures. Generative AI can promote the sustainable use of energy by introducing renewable energy and contribute to climate change countermeasures. For example, it can clarify specific standards and evaluation methods for sustainable use, such as the proportion of renewable energy, efficient use of resources, and long-term sustainability. It can also clarify specific measures and evaluation criteria for climate change countermeasures, such as greenhouse gas reduction targets, adaptation measures, and international cooperation. This can promote the sustainable use of energy by introducing renewable energy and contribute to climate change countermeasures.

[0062] Generative AI can achieve efficient resource use and promote sustainable production and consumption by improving energy efficiency. Generative AI can achieve efficient resource use and promote sustainable production and consumption by improving energy efficiency. For example, it can clarify specific measures and evaluation criteria for efficient resource use, such as recycling rates, energy efficiency, and waste reduction. It can also clarify specific measures and evaluation criteria for sustainable production and consumption, such as eco-design, green procurement, and consumer education. This can achieve efficient resource use and promote sustainable production and consumption by improving energy efficiency.

[0063] By improving a company's environmental performance, generative AI can fulfill its social responsibility and contribute to the realization of a sustainable society. Generative AI can, for example, fulfill its social responsibility and contribute to the realization of a sustainable society by improving the company's environmental performance. For example, it can clarify the specific content and evaluation criteria of social responsibility, such as CSR activities, social contribution, and stakeholder involvement. It can also clarify the specific definition and evaluation criteria of a sustainable society, such as environmental protection, social equity, and economic sustainability. This allows a company to fulfill its social responsibility and contribute to the realization of a sustainable society by improving its environmental performance.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] Green Flow Strategic Advisor can also provide a dashboard that visualizes a company's energy usage in real time. For example, it can display energy consumption trends and peak times in graphs, allowing companies to immediately optimize their energy usage. It can also add a function that issues alerts when abnormalities in energy consumption are detected. For example, it can send a notification to the administrator if there is a deviation from normal consumption patterns. Furthermore, it can predict future energy demand based on energy consumption data and propose optimal energy management plans. This allows companies to improve the efficiency of their energy use, reduce costs, and reduce their environmental impact.

[0066] IoT devices can also add environmental sensors to collect environmental data such as temperature, humidity, and air quality within a factory. For example, a temperature sensor can be used to monitor the temperature within a factory and manage air conditioning appropriately. A humidity sensor can also be used to manage humidity and maintain product quality. Furthermore, an air quality sensor can be used to monitor the air quality within the factory and take measures to protect the health of employees. This allows for comprehensive management of the factory environment, optimizing energy consumption while maintaining the health of employees.

[0067] IoT devices can also analyze employee movement patterns based on data collected from sensors installed on each floor of an office building. For example, they can analyze employee movement patterns and propose efficient layouts. They can also identify congested areas and implement measures to provide a comfortable working environment. Furthermore, they can optimize energy consumption based on employee movement data. For example, they can automatically adjust lighting and air conditioning in less frequently used areas. This can improve the efficiency of energy consumption in office buildings and create a comfortable working environment for employees.

[0068] Generative AI can also analyze employee emotional data based on a company's energy usage status and optimize energy consumption during times when stress levels are high. For example, it can collect employee emotional data and identify times when stress levels are high. It can also propose measures to reduce employee stress by adjusting lighting and air conditioning use during times when stress levels are high. Furthermore, it can build a system that automatically adjusts energy use during times when employees' stress levels are high based on emotional data. This can reduce employee stress and optimize energy consumption.

[0069] Generative AI can also analyze employee emotional data based on the company's energy usage status and optimize energy consumption according to their emotional state. For example, it can collect employee emotional data and propose specific measures to optimize energy consumption according to their emotional state. For example, it can adjust the use of lighting and air conditioning during times when employees are in a relaxed emotional state. It can also build a system that automatically adjusts energy usage according to employees' emotional state based on the emotional data. This makes it possible to optimize energy consumption according to employees' emotional state.

[0070] Generative AI can also analyze employee emotional data based on the company's energy usage status and optimize energy consumption according to their emotional state. For example, it can collect employee emotional data and propose specific measures to optimize energy consumption according to their emotional state. For example, it can adjust the use of lighting and air conditioning during times when employees are in a relaxed emotional state. It can also build a system that automatically adjusts energy usage according to employees' emotional state based on the emotional data. This makes it possible to optimize energy consumption according to employees' emotional state.

[0071] Generative AI can also analyze employee emotional data based on the company's energy usage status and optimize energy consumption according to their emotional state. For example, it can collect employee emotional data and propose specific measures to optimize energy consumption according to their emotional state. For example, it can adjust the use of lighting and air conditioning during times when employees are in a relaxed emotional state. It can also build a system that automatically adjusts energy usage according to employees' emotional state based on the emotional data. This makes it possible to optimize energy consumption according to employees' emotional state.

[0072] Generative AI can also analyze employee emotional data based on the company's energy usage status and optimize energy consumption according to their emotional state. For example, it can collect employee emotional data and propose specific measures to optimize energy consumption according to their emotional state. For example, it can adjust the use of lighting and air conditioning during times when employees are in a relaxed emotional state. It can also build a system that automatically adjusts energy usage according to employees' emotional state based on the emotional data. This makes it possible to optimize energy consumption according to employees' emotional state.

[0073] Generative AI can also analyze employee emotional data based on the company's energy usage status and optimize energy consumption according to their emotional state. For example, it can collect employee emotional data and propose specific measures to optimize energy consumption according to their emotional state. For example, it can adjust the use of lighting and air conditioning during times when employees are in a relaxed emotional state. It can also build a system that automatically adjusts energy usage according to employees' emotional state based on the emotional data. This makes it possible to optimize energy consumption according to employees' emotional state.

[0074] Generative AI can also analyze employee emotional data based on the company's energy usage status and optimize energy consumption according to their emotional state. For example, it can collect employee emotional data and propose specific measures to optimize energy consumption according to their emotional state. For example, it can adjust the use of lighting and air conditioning during times when employees are in a relaxed emotional state. It can also build a system that automatically adjusts energy usage according to employees' emotional state based on the emotional data. This makes it possible to optimize energy consumption according to employees' emotional state.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: IoT devices collect energy consumption data. For example, sensors can be attached to each machine in a factory to monitor its operation status and energy consumption, or sensors can be installed on each floor of an office building to monitor lighting and air conditioning usage. Step 2: Generative AI analyzes the energy consumption data collected by IoT devices. For example, text-generative AI (e.g., LLM) can be used to analyze the energy consumption data and suggest optimal energy usage methods. Multimodal generative AI can also be used to analyze the energy consumption data. Step 3: The cloud storage unit stores the data analyzed by the generating AI in the cloud. For example, the cloud storage unit stores the data in a database and implements security measures. Step 4: The investment proposal provider provides optimal renewable energy investment proposals based on the data stored in the cloud storage. For example, it proposes the optimal solar power generation system or wind power generation system installation based on the company's location and energy consumption pattern. It also evaluates the investment risks and returns and proposes the most effective investment strategy for the company.

[0077] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0078] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0079] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0083] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0084] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0085] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0086] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0087] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0088] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0089] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0090] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0091] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0118] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0127] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0128] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0129] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0130] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0131] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0132] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0133] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0134] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0135] 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.

[0136] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0137] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0138] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0139] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0140] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0141] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0142] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0143] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. IoT devices that collect energy consumption data, A generation AI that analyzes energy consumption data collected by the IoT device; a cloud storage unit that stores the data analyzed by the generating AI in a cloud; and an investment proposal providing unit that provides optimal renewable energy investment proposals based on the data stored in the cloud storage unit. A system characterized by:

2. The IoT device is Sensors are installed on each machine in the factory to monitor its operating status and energy consumption.

2. The system of claim 1.

3. The IoT device is Installing sensors on each floor of an office building to monitor lighting and air conditioning usage 2. The system of claim 1.

4. The generated AI is Propose the introduction of solar and wind power generation and simulate their effects 2. The system of claim 1.

5. The generated AI is Propose specific measures to improve energy efficiency and reduce CO2 emissions 2. The system of claim 1.

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