System

Abstract

An object of the system according to the embodiment is to simplify the authentication and issuance procedure of the J credit.SOLUTION: A system includes a data analysis unit, an authentication unit, and an issuance unit. The data analysis unit analyzes data provided from a natural possessor. The certification unit certifies the emission reduction amount and the absorption amount of the CO2. The issuing unit issues a J credit based on the emission reduction amount and the absorption amount of the CO2 authenticated by the authenticating 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, the certification and issuance procedures for J-Credits were cumbersome, limiting their widespread adoption.

[0005] The system according to the embodiment aims to simplify the certification and issuance procedures for J Credits. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a certification unit, and an issuing unit. The data analysis unit analyzes data provided by the natural owner. The certification unit certifies the amount of CO2 emission reductions or absorptions based on the data analyzed by the data analysis unit. The issuing unit issues J Credits based on the amount of CO2 emission reductions or absorptions certified by the certification unit. [Effects of the Invention]

[0007] The system according to the embodiment can simplify the certification and issuance procedures for J Credits. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 J-Credit Certification and Issuance System according to an embodiment of the present invention is a system that allows natural holders to easily acquire credits and convert them into cash. In this system, a generating AI certifies and issues J-Credits. This allows the J-Credit Certification and Issuance System to contribute to Japan's carbon offsetting and improve its CO2 self-sufficiency rate.

[0029] The J-Credit certification and issuance system according to the embodiment includes a data analysis unit, a certification unit, and an issuing unit. The data analysis unit analyzes data provided by natural resource owners. For example, the data analysis unit analyzes data such as forest area, tree species, and growth rate. The data analysis unit can also analyze meteorological data and soil data. The data analysis unit can also analyze satellite images and drone aerial photography data. The certification unit certifies CO2 emission reductions and absorptions based on the data analyzed by the data analysis unit. For example, the certification unit calculates CO2 absorptions based on the analyzed data and certifies the results. The certification unit can also calculate CO2 emission reductions and certify the results. The certification unit can also evaluate the reliability of the analyzed data and use only highly reliable data. The issuing unit issues J-Credits based on the CO2 emission reductions and absorptions certified by the certification unit. For example, the issuing unit issues a certain number of credits according to the certified CO2 absorptions. The issuing unit can also issue credits according to the certified CO2 emission reductions. Furthermore, the issuing unit can provide the issued credits in a digital format. This allows the J-Credit Certification and Issuance System according to the embodiment to allow natural holders to easily acquire credits and convert them into cash. For example, natural holders can sell credits issued by the generating AI to companies or individuals through a trading platform. This allows natural holders to convert their natural assets into cash.

[0030] The data analysis unit can add satellite images and drone aerial photography data to perform more detailed calculations of CO2 absorption. For example, the data analysis unit uses satellite images to analyze the extent and density of forests and calculate CO2 absorption. For example, data obtained from satellite images can be input into the generation AI to analyze the forest area and tree density, thereby calculating a more accurate CO2 absorption. The data analysis unit can also use drone aerial photography data to analyze the detailed structure of the forest and calculate CO2 absorption. For example, drone aerial photography data can be input into the generation AI to analyze the forest height and tree types, thereby calculating a more detailed CO2 absorption. This makes it possible to calculate CO2 absorption in more detail.

[0031] The data analysis unit can add environmental data such as soil quality and moisture content to improve the accuracy of the CO2 absorption amount. The data analysis unit, for example, analyzes soil quality and calculates CO2 absorption amount. For example, the organic matter content and pH value of the soil are input into the generation AI, and the CO2 absorption amount is calculated based on this data. The data analysis unit also analyzes moisture content and calculates CO2 absorption amount. For example, moisture content data is input into the generation AI, and the CO2 absorption amount is calculated based on this data. Furthermore, the data analysis unit combines data on soil quality and moisture content to comprehensively analyze CO2 absorption amount. For example, the CO2 absorption amount is calculated based on data on the organic matter content and moisture content of the soil. This improves the accuracy of the CO2 absorption amount.

[0032] The data analysis unit can add data on urban green spaces and green roofs, making CO2 absorption in urban areas also a target for certification. For example, the data analysis unit analyzes data on urban green spaces and calculates CO2 absorption. For example, data on parks and street trees is input into the generation AI to calculate CO2 absorption in urban areas. The data analysis unit also analyzes data on green roofs and calculates CO2 absorption. For example, data on the area of ​​green roofs and the plants used is input into the generation AI to calculate CO2 absorption. Furthermore, the data analysis unit combines data on urban green spaces and green roofs to comprehensively analyze CO2 absorption. For example, CO2 absorption in urban areas is calculated based on data on parks, street trees, and green roofs. This allows CO2 absorption in urban areas to be certified, making a wider range of carbon offsets possible.

[0033] The data analysis unit can add data on marine algae and seaweed, making ocean CO2 absorption a target for certification as well. The data analysis unit, for example, analyzes marine algae data and calculates CO2 absorption. For example, the distribution and growth rate of marine algae is input into the generation AI to calculate CO2 absorption. The data analysis unit also analyzes seaweed data and calculates CO2 absorption. For example, the distribution and growth rate of seaweed is input into the generation AI to calculate CO2 absorption. The data analysis unit further combines marine algae and seaweed data to comprehensively analyze CO2 absorption. For example, the amount of CO2 absorption in the ocean is calculated based on the data on marine algae and seaweed. This allows for a wider range of carbon offsets to be certified as ocean CO2 absorption.

[0034] The generating AI can ensure transparency and reliability by using blockchain technology for credits. For example, the generating AI records the credits it issues on the blockchain to ensure transparency. For example, it records the credit issuance history on the blockchain so that anyone can check it. The generating AI also uses blockchain technology to record the credit transaction history to ensure reliability. For example, it records the credit transaction history on the blockchain to prevent tampering. The generating AI also uses blockchain technology to ensure transparency and reliability of the issuance and transactions of credits. For example, it records the credit issuance and transaction history on the blockchain to ensure transparency and reliability. This improves the transparency and reliability of credits.

[0035] The generation AI can reflect the CO2 absorption amount for each region and issue credits according to the characteristics of the region. For example, the generation AI analyzes CO2 absorption data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the forest area and tree species for each region. The generation AI also analyzes climatic conditions and land use data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the climatic conditions and land use data for each region. Furthermore, the generation AI combines CO2 absorption data for each region with climatic conditions and land use data to issue credits according to the characteristics of the region. For example, it issues credits based on the forest area, tree species, climatic conditions, and land use data for each region. This makes it possible to issue credits according to the characteristics of the region.

[0036] The generation AI can issue credits that reflect the usage status of renewable energy and promote improvements in energy efficiency. For example, the generation AI analyzes data on renewable energy usage and reflects it in credits. For example, it issues credits based on data on solar power generation and wind power generation. The generation AI also analyzes data on improvements in energy efficiency and reflects it in credits. For example, it issues credits based on data on reduced energy consumption. Furthermore, the generation AI combines data on renewable energy usage and data on improvements in energy efficiency to issue credits. For example, it issues credits based on data on solar power generation and wind power generation and data on reduced energy consumption. This makes it possible to issue credits that promote improvements in energy efficiency.

[0037] The generation AI can issue credits that reflect a company's corporate social responsibility (CSR) activities and evaluate its environmental contribution. For example, the generation AI analyzes a company's CSR activity data and reflects this in the credits. For example, it issues credits based on a company's environmental protection activities and social contribution activities. The generation AI also analyzes a company's environmental contribution data and reflects this in the credits. For example, it issues credits based on a company's CO2 reductions and the results of its environmental protection activities. Furthermore, the generation AI combines a company's CSR activity data with environmental contribution data to issue credits. For example, it issues credits based on a company's environmental protection activities, social contribution activities, and CO2 reductions. This makes it possible to issue credits that evaluate a company's environmental contribution.

[0038] Generating AI can build a platform where credits can be traded automatically using smart contracts. For example, Generating AI can incorporate issued credits into smart contracts and build a platform where they can be traded automatically. For example, it can write the terms of a credit transaction into a smart contract and automatically execute the transaction. Generating AI can also use smart contracts to record the credit transaction history and ensure transparency. For example, it can record the credit transaction history in a smart contract so that anyone can check it. Generating AI can also use smart contracts to automate credit transactions and ensure the transparency and reliability of transactions. For example, it can write the terms of a credit transaction into a smart contract, automatically execute the transaction, and record the transaction history. This automates credit transactions and improves transparency.

[0039] The generation AI can provide a system that stores credits in a digital wallet and allows them to be easily converted into cash. For example, the generation AI builds a system that stores issued credits in a digital wallet and allows them to be easily converted into cash. For example, credits are stored in a digital wallet and converted into cash as needed. The generation AI also provides a system that uses a digital wallet to easily trade credits. For example, credits are traded through a digital wallet and easily converted into cash. Furthermore, the generation AI provides a system that uses a digital wallet to centrally manage the storage and trading of credits. For example, credits are stored in a digital wallet, transaction history is recorded, and they are converted into cash as needed. This makes it easy to convert credits into cash.

[0040] The generation AI can link credits with other environmental contribution activities (e.g., tree planting) to promote compound environmental contributions. For example, the generation AI can link issued credits with tree planting activities to promote compound environmental contributions. For example, allocating part of the credits to tree planting activities increases CO2 absorption. The generation AI can also link credits with other environmental contribution activities to maximize the effect of the credits. For example, allocating credits to cleaning activities or the introduction of renewable energy improves the level of environmental contribution. Furthermore, the generation AI can link credits with multiple environmental contribution activities and comprehensively evaluate the effect of the credits. For example, allocating credits to tree planting activities, cleaning activities, and the introduction of renewable energy promotes compound environmental contributions. This promotes compound environmental contributions.

[0041] Generating AI can link credits with international carbon markets, enabling global trading. For example, Generating AI can link issued credits with international carbon markets, enabling global trading. For example, it can register credits on an international trading platform, making them available for purchase by overseas companies and individuals. Generating AI can also link with international carbon markets to promote credit trading. For example, it can register credits on an international trading platform, ensuring the transparency and reliability of transactions. Furthermore, Generating AI can link with international carbon markets and centralize credit trading. For example, it can centralize the issuance, trading, and cashing of credits on an international trading platform. This makes global trading possible.

[0042] The generation AI can aggregate CO2 absorption data for each region and formulate a carbon offset strategy for each region. For example, the generation AI aggregates CO2 absorption data for each region and formulates a carbon offset strategy for each region. For example, it formulates a carbon offset strategy based on the forest area and tree species for each region. The generation AI also aggregates climate conditions and land use data for each region and formulates a carbon offset strategy for each region. For example, it formulates a carbon offset strategy based on the climate conditions and land use data for each region. The generation AI further combines the CO2 absorption data for each region with climate conditions and land use data to formulate a carbon offset strategy for each region. For example, it formulates a carbon offset strategy based on the forest area, tree species, climate conditions, and land use data for each region. This makes it possible to formulate a carbon offset strategy for each region.

[0043] The generation AI can analyze past CO2 emission data and absorption data to predict future carbon offsets. The generation AI, for example, analyzes past CO2 emission data and absorption data to predict future carbon offsets. For example, it predicts future CO2 absorption amounts based on past data and formulates a carbon offset strategy. The generation AI also combines past CO2 emission data and absorption data to predict future carbon offsets. For example, it predicts future CO2 absorption amounts based on past CO2 emission and absorption data. The generation AI also creates future carbon offset scenarios based on past data and formulates strategies. For example, it creates future carbon offset scenarios based on past CO2 emission and absorption data and formulates strategies. This makes it possible to predict future carbon offsets.

[0044] Generative AI can improve analytical algorithms and integrate information from a wider variety of data sources. For example, generative AI can improve analytical algorithms to integrate and analyze information from a wider variety of data sources. For example, it can integrate and analyze satellite images, drone aerial data, soil data, etc. Generative AI can also improve analytical algorithms to improve data accuracy. For example, it can preprocess and filter data to improve analysis accuracy. Generative AI can also improve analytical algorithms to improve analysis speed. For example, it can introduce parallel processing and distributed processing to improve analysis speed. This makes it possible to integrate information from a wider variety of data sources.

[0045] The generative AI can provide visualization tools that allow users to intuitively understand the analysis results. For example, the generative AI provides tools that visualize the analysis results and allow users to intuitively understand them. For example, the generative AI displays the analysis results in graphs and charts. The generative AI also displays the analysis results on a map to allow users to intuitively understand them. For example, the generative AI plots the analysis results on a map to visually display the amount of CO2 absorption by region. Furthermore, the generative AI displays the analysis results interactively, allowing users to check detailed information. For example, the generative AI displays the analysis results on an interactive dashboard, allowing users to check detailed data. This allows users to intuitively understand the analysis results.

[0046] The generation AI can work in conjunction with other environmental data analysis systems to perform comprehensive environmental data analysis. The generation AI, for example, works in conjunction with other environmental data analysis systems to perform comprehensive environmental data analysis. For example, it works in conjunction with a meteorological data analysis system to calculate comprehensive CO2 absorption. The generation AI also works in conjunction with other environmental data analysis systems to mutually share data. For example, by working in conjunction with a meteorological data analysis system and mutually sharing data, the accuracy of the analysis is improved. Furthermore, the generation AI works in conjunction with other environmental data analysis systems to integrate and display the analysis results. For example, by working in conjunction with a meteorological data analysis system and integrating and displaying the analysis results, comprehensive environmental data analysis is performed. This makes comprehensive environmental data analysis possible.

[0047] Generative AI can collaborate with educational institutions and research institutes to promote research into environmental data analysis. For example, generative AI can collaborate with educational institutions to promote research into environmental data analysis. For example, it can collaborate with university laboratories to conduct analytical research into CO2 absorption. Generative AI can also collaborate with research institutes to promote research into environmental data analysis. For example, it can collaborate with research institutes to conduct analytical research into CO2 absorption. Generative AI can also collaborate with educational institutions and research institutes to share research results. For example, it can collaborate with universities and research institutes to publish research results and promote research into environmental data analysis. This will promote research into environmental data analysis.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The data analysis unit can add data on urban green spaces and green roofs, making CO2 absorption in urban areas also a target for certification. For example, the data analysis unit analyzes data on urban green spaces and calculates CO2 absorption. For example, data on parks and street trees is input into the generation AI to calculate CO2 absorption in urban areas. The data analysis unit also analyzes data on green roofs and calculates CO2 absorption. For example, data on the area of ​​green roofs and the plants used is input into the generation AI to calculate CO2 absorption. Furthermore, the data analysis unit combines data on urban green spaces and green roofs to comprehensively analyze CO2 absorption. For example, CO2 absorption in urban areas is calculated based on data on parks, street trees, and green roofs. This allows CO2 absorption in urban areas to be certified, making a wider range of carbon offsets possible.

[0050] The data analysis unit can add data on marine algae and seaweed, making ocean CO2 absorption a target for certification as well. The data analysis unit, for example, analyzes marine algae data and calculates CO2 absorption. For example, the distribution and growth rate of marine algae is input into the generation AI to calculate CO2 absorption. The data analysis unit also analyzes seaweed data and calculates CO2 absorption. For example, the distribution and growth rate of seaweed is input into the generation AI to calculate CO2 absorption. The data analysis unit further combines marine algae and seaweed data to comprehensively analyze CO2 absorption. For example, the amount of CO2 absorption in the ocean is calculated based on the data on marine algae and seaweed. This allows for a wider range of carbon offsets to be certified as ocean CO2 absorption.

[0051] The generating AI can ensure transparency and reliability by using blockchain technology for credits. For example, the generating AI records the credits it issues on the blockchain to ensure transparency. For example, it records the credit issuance history on the blockchain so that anyone can check it. The generating AI also uses blockchain technology to record the credit transaction history to ensure reliability. For example, it records the credit transaction history on the blockchain to prevent tampering. The generating AI also uses blockchain technology to ensure transparency and reliability of the issuance and transactions of credits. For example, it records the credit issuance and transaction history on the blockchain to ensure transparency and reliability. This improves the transparency and reliability of credits.

[0052] The generation AI can reflect the CO2 absorption amount for each region and issue credits according to the characteristics of the region. For example, the generation AI analyzes CO2 absorption data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the forest area and tree species for each region. The generation AI also analyzes climatic conditions and land use data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the climatic conditions and land use data for each region. Furthermore, the generation AI combines CO2 absorption data for each region with climatic conditions and land use data to issue credits according to the characteristics of the region. For example, it issues credits based on the forest area, tree species, climatic conditions, and land use data for each region. This makes it possible to issue credits according to the characteristics of the region.

[0053] The generation AI can issue credits that reflect the usage status of renewable energy and promote improvements in energy efficiency. For example, the generation AI analyzes data on renewable energy usage and reflects it in credits. For example, it issues credits based on data on solar power generation and wind power generation. The generation AI also analyzes data on improvements in energy efficiency and reflects it in credits. For example, it issues credits based on data on reduced energy consumption. Furthermore, the generation AI combines data on renewable energy usage and data on improvements in energy efficiency to issue credits. For example, it issues credits based on data on solar power generation and wind power generation and data on reduced energy consumption. This makes it possible to issue credits that promote improvements in energy efficiency.

[0054] The generation AI can issue credits that reflect a company's corporate social responsibility (CSR) activities and evaluate its environmental contribution. For example, the generation AI analyzes a company's CSR activity data and reflects this in the credits. For example, it issues credits based on a company's environmental protection activities and social contribution activities. The generation AI also analyzes a company's environmental contribution data and reflects this in the credits. For example, it issues credits based on a company's CO2 reductions and the results of its environmental protection activities. Furthermore, the generation AI combines a company's CSR activity data with environmental contribution data to issue credits. For example, it issues credits based on a company's environmental protection activities, social contribution activities, and CO2 reductions. This makes it possible to issue credits that evaluate a company's environmental contribution.

[0055] Generating AI can build a platform where credits can be traded automatically using smart contracts. For example, Generating AI can incorporate issued credits into smart contracts and build a platform where they can be traded automatically. For example, it can write the terms of a credit transaction into a smart contract and automatically execute the transaction. Generating AI can also use smart contracts to record the credit transaction history and ensure transparency. For example, it can record the credit transaction history in a smart contract so that anyone can check it. Generating AI can also use smart contracts to automate credit transactions and ensure the transparency and reliability of transactions. For example, it can write the terms of a credit transaction into a smart contract, automatically execute the transaction, and record the transaction history. This automates credit transactions and improves transparency.

[0056] The generation AI can provide a system that stores credits in a digital wallet and allows them to be easily converted into cash. For example, the generation AI builds a system that stores issued credits in a digital wallet and allows them to be easily converted into cash. For example, credits are stored in a digital wallet and converted into cash as needed. The generation AI also provides a system that uses a digital wallet to easily trade credits. For example, credits are traded through a digital wallet and easily converted into cash. Furthermore, the generation AI provides a system that uses a digital wallet to centrally manage the storage and trading of credits. For example, credits are stored in a digital wallet, transaction history is recorded, and they are converted into cash as needed. This makes it easy to convert credits into cash.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data analysis unit analyzes the data provided by the nature owner. For example, the data analysis unit analyzes data such as forest area, tree species, and growth rate. It can also analyze weather data, soil data, satellite images, and drone aerial data. Step 2: The certification unit certifies the amount of CO2 emission reduction or absorption based on the data analyzed by the data analysis unit. For example, the certification unit calculates the amount of CO2 absorption or emission reduction based on the analyzed data and certifies the results. The certification unit also evaluates the reliability of the analyzed data and uses only highly reliable data. Step 3: The issuing unit issues J-Credits based on the CO2 emission reductions or absorption amounts certified by the certification unit. For example, credits can be issued based on the certified CO2 absorption or emission reduction amounts and provided in digital form. This allows natural holders to obtain credits and sell them to companies and individuals through a trading platform.

[0059] (Example 2) The J-Credit Certification and Issuance System according to an embodiment of the present invention is a system that allows natural holders to easily acquire credits and convert them into cash. In this system, a generating AI certifies and issues J-Credits. This allows the J-Credit Certification and Issuance System to contribute to Japan's carbon offsetting and improve its CO2 self-sufficiency rate.

[0060] The J-Credit certification and issuance system according to the embodiment includes a data analysis unit, a certification unit, and an issuing unit. The data analysis unit analyzes data provided by natural resource owners. For example, the data analysis unit analyzes data such as forest area, tree species, and growth rate. The data analysis unit can also analyze meteorological data and soil data. The data analysis unit can also analyze satellite images and drone aerial photography data. The certification unit certifies CO2 emission reductions and absorptions based on the data analyzed by the data analysis unit. For example, the certification unit calculates CO2 absorptions based on the analyzed data and certifies the results. The certification unit can also calculate CO2 emission reductions and certify the results. The certification unit can also evaluate the reliability of the analyzed data and use only highly reliable data. The issuing unit issues J-Credits based on the CO2 emission reductions and absorptions certified by the certification unit. For example, the issuing unit issues a certain number of credits according to the certified CO2 absorptions. The issuing unit can also issue credits according to the certified CO2 emission reductions. Furthermore, the issuing unit can provide the issued credits in a digital format. This allows the J-Credit Certification and Issuance System according to the embodiment to allow natural holders to easily acquire credits and convert them into cash. For example, natural holders can sell credits issued by the generating AI to companies or individuals through a trading platform. This allows natural holders to convert their natural assets into cash.

[0061] The data analysis unit can add satellite images and drone aerial photography data to perform more detailed calculations of CO2 absorption. For example, the data analysis unit uses satellite images to analyze the extent and density of forests and calculate CO2 absorption. For example, data obtained from satellite images can be input into the generation AI to analyze the forest area and tree density, thereby calculating a more accurate CO2 absorption. The data analysis unit can also use drone aerial photography data to analyze the detailed structure of the forest and calculate CO2 absorption. For example, drone aerial photography data can be input into the generation AI to analyze the forest height and tree types, thereby calculating a more detailed CO2 absorption. This makes it possible to calculate CO2 absorption in more detail.

[0062] The data analysis unit can add environmental data such as soil quality and moisture content to improve the accuracy of the CO2 absorption amount. The data analysis unit, for example, analyzes soil quality and calculates CO2 absorption amount. For example, the organic matter content and pH value of the soil are input into the generation AI, and the CO2 absorption amount is calculated based on this data. The data analysis unit also analyzes moisture content and calculates CO2 absorption amount. For example, moisture content data is input into the generation AI, and the CO2 absorption amount is calculated based on this data. Furthermore, the data analysis unit combines data on soil quality and moisture content to comprehensively analyze CO2 absorption amount. For example, the CO2 absorption amount is calculated based on data on the organic matter content and moisture content of the soil. This improves the accuracy of the CO2 absorption amount.

[0063] The data analysis unit uses the emotion estimation function to evaluate the reliability of data provided by the natural owner and can use only highly reliable data. The data analysis unit, for example, uses the emotion estimation function to evaluate the reliability of the data provider. For example, it analyzes the facial expressions and voice of the data provider and inputs only highly reliable data into the generation AI. The data analysis unit also uses the emotion estimation function to evaluate the emotional state of the data provider and use only highly reliable data. For example, it selects highly reliable data based on the emotional score of the data provider. Furthermore, the data analysis unit uses the emotion estimation function to continuously monitor the reliability of the data provider and use only highly reliable data. For example, it monitors the emotional state of the data provider in real time and selects highly reliable data. This improves the accuracy of the analysis results by using only highly reliable data.

[0064] The data analysis unit can add data on urban green spaces and green roofs, making CO2 absorption in urban areas also a target for certification. For example, the data analysis unit analyzes data on urban green spaces and calculates CO2 absorption. For example, data on parks and street trees is input into the generation AI to calculate CO2 absorption in urban areas. The data analysis unit also analyzes data on green roofs and calculates CO2 absorption. For example, data on the area of ​​green roofs and the plants used is input into the generation AI to calculate CO2 absorption. Furthermore, the data analysis unit combines data on urban green spaces and green roofs to comprehensively analyze CO2 absorption. For example, CO2 absorption in urban areas is calculated based on data on parks, street trees, and green roofs. This allows CO2 absorption in urban areas to be certified, making a wider range of carbon offsets possible.

[0065] The data analysis unit can add data on marine algae and seaweed, making ocean CO2 absorption a target for certification as well. The data analysis unit, for example, analyzes marine algae data and calculates CO2 absorption. For example, the distribution and growth rate of marine algae is input into the generation AI to calculate CO2 absorption. The data analysis unit also analyzes seaweed data and calculates CO2 absorption. For example, the distribution and growth rate of seaweed is input into the generation AI to calculate CO2 absorption. The data analysis unit further combines marine algae and seaweed data to comprehensively analyze CO2 absorption. For example, the amount of CO2 absorption in the ocean is calculated based on the data on marine algae and seaweed. This allows for a wider range of carbon offsets to be certified as ocean CO2 absorption.

[0066] The data analysis unit can use the emotion estimation function to suggest incentives to increase the motivation of natural holders when providing data. The data analysis unit, for example, uses the emotion estimation function to evaluate the motivation of the data provider and suggest incentives. For example, it analyzes the emotional state of the data provider and provides a reward if the emotional state is strong and positive. The data analysis unit also uses the emotion estimation function to monitor the emotional state of the data provider in real time and suggest incentives. For example, it provides an appropriate incentive based on the emotional score of the data provider. Furthermore, the data analysis unit uses the emotion estimation function to continuously monitor the motivation of the data provider and suggest incentives. For example, it monitors the emotional state of the data provider in real time and provides an appropriate incentive. This increases the motivation of natural holders, thereby improving the quality and quantity of data provision.

[0067] The generating AI can ensure transparency and reliability by using blockchain technology for credits. For example, the generating AI records the credits it issues on the blockchain to ensure transparency. For example, it records the credit issuance history on the blockchain so that anyone can check it. The generating AI also uses blockchain technology to record the credit transaction history to ensure reliability. For example, it records the credit transaction history on the blockchain to prevent tampering. The generating AI also uses blockchain technology to ensure transparency and reliability of the issuance and transactions of credits. For example, it records the credit issuance and transaction history on the blockchain to ensure transparency and reliability. This improves the transparency and reliability of credits.

[0068] The generation AI can reflect the CO2 absorption amount for each region and issue credits according to the characteristics of the region. For example, the generation AI analyzes CO2 absorption data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the forest area and tree species for each region. The generation AI also analyzes climatic conditions and land use data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the climatic conditions and land use data for each region. Furthermore, the generation AI combines CO2 absorption data for each region with climatic conditions and land use data to issue credits according to the characteristics of the region. For example, it issues credits based on the forest area, tree species, climatic conditions, and land use data for each region. This makes it possible to issue credits according to the characteristics of the region.

[0069] The generation AI can issue credits that reflect the usage status of renewable energy and promote improvements in energy efficiency. For example, the generation AI analyzes data on renewable energy usage and reflects it in credits. For example, it issues credits based on data on solar power generation and wind power generation. The generation AI also analyzes data on improvements in energy efficiency and reflects it in credits. For example, it issues credits based on data on reduced energy consumption. Furthermore, the generation AI combines data on renewable energy usage and data on improvements in energy efficiency to issue credits. For example, it issues credits based on data on solar power generation and wind power generation and data on reduced energy consumption. This makes it possible to issue credits that promote improvements in energy efficiency.

[0070] The generation AI can issue credits that reflect a company's corporate social responsibility (CSR) activities and evaluate its environmental contribution. For example, the generation AI analyzes a company's CSR activity data and reflects this in the credits. For example, it issues credits based on a company's environmental protection activities and social contribution activities. The generation AI also analyzes a company's environmental contribution data and reflects this in the credits. For example, it issues credits based on a company's CO2 reductions and the results of its environmental protection activities. Furthermore, the generation AI combines a company's CSR activity data with environmental contribution data to issue credits. For example, it issues credits based on a company's environmental protection activities, social contribution activities, and CO2 reductions. This makes it possible to issue credits that evaluate a company's environmental contribution.

[0071] Generating AI can build a platform where credits can be traded automatically using smart contracts. For example, Generating AI can incorporate issued credits into smart contracts and build a platform where they can be traded automatically. For example, it can write the terms of a credit transaction into a smart contract and automatically execute the transaction. Generating AI can also use smart contracts to record the credit transaction history and ensure transparency. For example, it can record the credit transaction history in a smart contract so that anyone can check it. Generating AI can also use smart contracts to automate credit transactions and ensure the transparency and reliability of transactions. For example, it can write the terms of a credit transaction into a smart contract, automatically execute the transaction, and record the transaction history. This automates credit transactions and improves transparency.

[0072] The generation AI can provide a system that stores credits in a digital wallet and allows them to be easily converted into cash. For example, the generation AI builds a system that stores issued credits in a digital wallet and allows them to be easily converted into cash. For example, credits are stored in a digital wallet and converted into cash as needed. The generation AI also provides a system that uses a digital wallet to easily trade credits. For example, credits are traded through a digital wallet and easily converted into cash. Furthermore, the generation AI provides a system that uses a digital wallet to centrally manage the storage and trading of credits. For example, credits are stored in a digital wallet, transaction history is recorded, and they are converted into cash as needed. This makes it easy to convert credits into cash.

[0073] The generation AI can link credits with other environmental contribution activities (e.g., tree planting) to promote compound environmental contributions. For example, the generation AI can link issued credits with tree planting activities to promote compound environmental contributions. For example, allocating part of the credits to tree planting activities increases CO2 absorption. The generation AI can also link credits with other environmental contribution activities to maximize the effect of the credits. For example, allocating credits to cleaning activities or the introduction of renewable energy improves the level of environmental contribution. Furthermore, the generation AI can link credits with multiple environmental contribution activities and comprehensively evaluate the effect of the credits. For example, allocating credits to tree planting activities, cleaning activities, and the introduction of renewable energy promotes compound environmental contributions. This promotes compound environmental contributions.

[0074] Generating AI can link credits with international carbon markets, enabling global trading. For example, Generating AI can link issued credits with international carbon markets, enabling global trading. For example, it can register credits on an international trading platform, making them available for purchase by overseas companies and individuals. Generating AI can also link with international carbon markets to promote credit trading. For example, it can register credits on an international trading platform, ensuring the transparency and reliability of transactions. Furthermore, Generating AI can link with international carbon markets and centralize credit trading. For example, it can centralize the issuance, trading, and cashing of credits on an international trading platform. This makes global trading possible.

[0075] The generation AI can aggregate CO2 absorption data for each region and formulate a carbon offset strategy for each region. For example, the generation AI aggregates CO2 absorption data for each region and formulates a carbon offset strategy for each region. For example, it formulates a carbon offset strategy based on the forest area and tree species for each region. The generation AI also aggregates climate conditions and land use data for each region and formulates a carbon offset strategy for each region. For example, it formulates a carbon offset strategy based on the climate conditions and land use data for each region. The generation AI further combines the CO2 absorption data for each region with climate conditions and land use data to formulate a carbon offset strategy for each region. For example, it formulates a carbon offset strategy based on the forest area, tree species, climate conditions, and land use data for each region. This makes it possible to formulate a carbon offset strategy for each region.

[0076] The generation AI can analyze past CO2 emission data and absorption data to predict future carbon offsets. The generation AI, for example, analyzes past CO2 emission data and absorption data to predict future carbon offsets. For example, it predicts future CO2 absorption amounts based on past data and formulates a carbon offset strategy. The generation AI also combines past CO2 emission data and absorption data to predict future carbon offsets. For example, it predicts future CO2 absorption amounts based on past CO2 emission and absorption data. The generation AI also creates future carbon offset scenarios based on past data and formulates strategies. For example, it creates future carbon offset scenarios based on past CO2 emission and absorption data and formulates strategies. This makes it possible to predict future carbon offsets.

[0077] Generative AI can improve analytical algorithms and integrate information from a wider variety of data sources. For example, generative AI can improve analytical algorithms to integrate and analyze information from a wider variety of data sources. For example, it can integrate and analyze satellite images, drone aerial data, soil data, etc. Generative AI can also improve analytical algorithms to improve data accuracy. For example, it can preprocess and filter data to improve analysis accuracy. Generative AI can also improve analytical algorithms to improve analysis speed. For example, it can introduce parallel processing and distributed processing to improve analysis speed. This makes it possible to integrate information from a wider variety of data sources.

[0078] The generative AI can provide visualization tools that allow users to intuitively understand the analysis results. For example, the generative AI provides tools that visualize the analysis results and allow users to intuitively understand them. For example, the generative AI displays the analysis results in graphs and charts. The generative AI also displays the analysis results on a map to allow users to intuitively understand them. For example, the generative AI plots the analysis results on a map to visually display the amount of CO2 absorption by region. Furthermore, the generative AI displays the analysis results interactively, allowing users to check detailed information. For example, the generative AI displays the analysis results on an interactive dashboard, allowing users to check detailed data. This allows users to intuitively understand the analysis results.

[0079] The generation AI can work in conjunction with other environmental data analysis systems to perform comprehensive environmental data analysis. The generation AI, for example, works in conjunction with other environmental data analysis systems to perform comprehensive environmental data analysis. For example, it works in conjunction with a meteorological data analysis system to calculate comprehensive CO2 absorption. The generation AI also works in conjunction with other environmental data analysis systems to mutually share data. For example, by working in conjunction with a meteorological data analysis system and mutually sharing data, the accuracy of the analysis is improved. Furthermore, the generation AI works in conjunction with other environmental data analysis systems to integrate and display the analysis results. For example, by working in conjunction with a meteorological data analysis system and integrating and displaying the analysis results, comprehensive environmental data analysis is performed. This makes comprehensive environmental data analysis possible.

[0080] Generative AI can collaborate with educational institutions and research institutes to promote research into environmental data analysis. For example, generative AI can collaborate with educational institutions to promote research into environmental data analysis. For example, it can collaborate with university laboratories to conduct analytical research into CO2 absorption. Generative AI can also collaborate with research institutes to promote research into environmental data analysis. For example, it can collaborate with research institutes to conduct analytical research into CO2 absorption. Generative AI can also collaborate with educational institutions and research institutes to share research results. For example, it can collaborate with universities and research institutes to publish research results and promote research into environmental data analysis. This will promote research into environmental data analysis.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The data analysis unit uses the emotion estimation function to evaluate the reliability of data provided by the natural owner and can use only highly reliable data. The data analysis unit, for example, uses the emotion estimation function to evaluate the reliability of the data provider. For example, it analyzes the facial expressions and voice of the data provider and inputs only highly reliable data into the generation AI. The data analysis unit also uses the emotion estimation function to evaluate the emotional state of the data provider and use only highly reliable data. For example, it selects highly reliable data based on the emotional score of the data provider. Furthermore, the data analysis unit uses the emotion estimation function to continuously monitor the reliability of the data provider and use only highly reliable data. For example, it monitors the emotional state of the data provider in real time and selects highly reliable data. This improves the accuracy of the analysis results by using only highly reliable data.

[0083] The data analysis unit can add data on urban green spaces and green roofs, making CO2 absorption in urban areas also a target for certification. For example, the data analysis unit analyzes data on urban green spaces and calculates CO2 absorption. For example, data on parks and street trees is input into the generation AI to calculate CO2 absorption in urban areas. The data analysis unit also analyzes data on green roofs and calculates CO2 absorption. For example, data on the area of ​​green roofs and the plants used is input into the generation AI to calculate CO2 absorption. Furthermore, the data analysis unit combines data on urban green spaces and green roofs to comprehensively analyze CO2 absorption. For example, CO2 absorption in urban areas is calculated based on data on parks, street trees, and green roofs. This allows CO2 absorption in urban areas to be certified, making a wider range of carbon offsets possible.

[0084] The data analysis unit can add data on marine algae and seaweed, making ocean CO2 absorption a target for certification as well. The data analysis unit, for example, analyzes marine algae data and calculates CO2 absorption. For example, the distribution and growth rate of marine algae is input into the generation AI to calculate CO2 absorption. The data analysis unit also analyzes seaweed data and calculates CO2 absorption. For example, the distribution and growth rate of seaweed is input into the generation AI to calculate CO2 absorption. The data analysis unit further combines marine algae and seaweed data to comprehensively analyze CO2 absorption. For example, the amount of CO2 absorption in the ocean is calculated based on the data on marine algae and seaweed. This allows for a wider range of carbon offsets to be certified as ocean CO2 absorption.

[0085] The data analysis unit can use the emotion estimation function to suggest incentives to increase the motivation of natural holders when providing data. The data analysis unit, for example, uses the emotion estimation function to evaluate the motivation of the data provider and suggest incentives. For example, it analyzes the emotional state of the data provider and provides a reward if the emotional state is strong and positive. The data analysis unit also uses the emotion estimation function to monitor the emotional state of the data provider in real time and suggest incentives. For example, it provides an appropriate incentive based on the emotional score of the data provider. Furthermore, the data analysis unit uses the emotion estimation function to continuously monitor the motivation of the data provider and suggest incentives. For example, it monitors the emotional state of the data provider in real time and provides an appropriate incentive. This increases the motivation of natural holders, thereby improving the quality and quantity of data provision.

[0086] The generating AI can ensure transparency and reliability by using blockchain technology for credits. For example, the generating AI records the credits it issues on the blockchain to ensure transparency. For example, it records the credit issuance history on the blockchain so that anyone can check it. The generating AI also uses blockchain technology to record the credit transaction history to ensure reliability. For example, it records the credit transaction history on the blockchain to prevent tampering. The generating AI also uses blockchain technology to ensure transparency and reliability of the issuance and transactions of credits. For example, it records the credit issuance and transaction history on the blockchain to ensure transparency and reliability. This improves the transparency and reliability of credits.

[0087] The generation AI can reflect the CO2 absorption amount for each region and issue credits according to the characteristics of the region. For example, the generation AI analyzes CO2 absorption data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the forest area and tree species for each region. The generation AI also analyzes climatic conditions and land use data for each region and issues credits according to the characteristics of the region. For example, it issues credits based on the climatic conditions and land use data for each region. Furthermore, the generation AI combines CO2 absorption data for each region with climatic conditions and land use data to issue credits according to the characteristics of the region. For example, it issues credits based on the forest area, tree species, climatic conditions, and land use data for each region. This makes it possible to issue credits according to the characteristics of the region.

[0088] The generation AI can issue credits that reflect the usage status of renewable energy and promote improvements in energy efficiency. For example, the generation AI analyzes data on renewable energy usage and reflects it in credits. For example, it issues credits based on data on solar power generation and wind power generation. The generation AI also analyzes data on improvements in energy efficiency and reflects it in credits. For example, it issues credits based on data on reduced energy consumption. Furthermore, the generation AI combines data on renewable energy usage and data on improvements in energy efficiency to issue credits. For example, it issues credits based on data on solar power generation and wind power generation and data on reduced energy consumption. This makes it possible to issue credits that promote improvements in energy efficiency.

[0089] The generation AI can issue credits that reflect a company's corporate social responsibility (CSR) activities and evaluate its environmental contribution. For example, the generation AI analyzes a company's CSR activity data and reflects this in the credits. For example, it issues credits based on a company's environmental protection activities and social contribution activities. The generation AI also analyzes a company's environmental contribution data and reflects this in the credits. For example, it issues credits based on a company's CO2 reductions and the results of its environmental protection activities. Furthermore, the generation AI combines a company's CSR activity data with environmental contribution data to issue credits. For example, it issues credits based on a company's environmental protection activities, social contribution activities, and CO2 reductions. This makes it possible to issue credits that evaluate a company's environmental contribution.

[0090] Generating AI can build a platform where credits can be traded automatically using smart contracts. For example, Generating AI can incorporate issued credits into smart contracts and build a platform where they can be traded automatically. For example, it can write the terms of a credit transaction into a smart contract and automatically execute the transaction. Generating AI can also use smart contracts to record the credit transaction history and ensure transparency. For example, it can record the credit transaction history in a smart contract so that anyone can check it. Generating AI can also use smart contracts to automate credit transactions and ensure the transparency and reliability of transactions. For example, it can write the terms of a credit transaction into a smart contract, automatically execute the transaction, and record the transaction history. This automates credit transactions and improves transparency.

[0091] The generation AI can provide a system that stores credits in a digital wallet and allows them to be easily converted into cash. For example, the generation AI builds a system that stores issued credits in a digital wallet and allows them to be easily converted into cash. For example, credits are stored in a digital wallet and converted into cash as needed. The generation AI also provides a system that uses a digital wallet to easily trade credits. For example, credits are traded through a digital wallet and easily converted into cash. Furthermore, the generation AI provides a system that uses a digital wallet to centrally manage the storage and trading of credits. For example, credits are stored in a digital wallet, transaction history is recorded, and they are converted into cash as needed. This makes it easy to convert credits into cash.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The data analysis unit analyzes the data provided by the nature owner. For example, the data analysis unit analyzes data such as forest area, tree species, and growth rate. It can also analyze weather data, soil data, satellite images, and drone aerial data. Step 2: The certification unit certifies the amount of CO2 emission reduction or absorption based on the data analyzed by the data analysis unit. For example, the certification unit calculates the amount of CO2 absorption or emission reduction based on the analyzed data and certifies the results. The certification unit also evaluates the reliability of the analyzed data and uses only highly reliable data. Step 3: The issuing unit issues J-Credits based on the CO2 emission reductions or absorption amounts certified by the certification unit. For example, credits can be issued based on the certified CO2 absorption or emission reduction amounts and provided in digital form. This allows natural holders to obtain credits and sell them to companies and individuals through a trading platform.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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. a data analysis unit that analyzes data provided by the natural recipient; a certification unit that certifies the amount of CO2 emission reduction or absorption based on the data analyzed by the data analysis unit; and an issuing unit that issues J credits based on the amount of CO2 emission reductions or absorption certified by the certification unit. A system characterized by:

2. The data analysis unit Using an emotion estimation function, the reliability of the data provided by the natural owner is evaluated, and only reliable data is used.

2. The system of claim 1.

3. The data analysis unit Add data on marine algae and seaweed, and certify the amount of CO2 absorbed by the ocean.

2. The system of claim 1.

4. The generating AI is The credit will be secured with transparency and reliability using blockchain technology.

2. The system of claim 1.

5. The generating AI is Build a platform that can automatically trade the credits using smart contracts 2. The system of claim 1.

6. The generating AI is Aggregate CO2 absorption data by region and develop carbon offset strategies for each region 2. The system of claim 1.

7. The generating AI is Conduct comprehensive environmental data analysis in cooperation with the environmental data analysis system 2. The system of claim 1.

8. The generating AI is Monitor user sentiment towards analysis results in real time and optimize the analysis process 2. The system of claim 1.

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