system

Abstract

The system according to the embodiment aims to effectively manage corporate emissions and green space absorption, and promote carbon neutrality. [Solution] A system according to an embodiment includes a data collection unit, an emissions analysis unit, a removal analysis unit, and an auction holding unit. The data collection unit collects emissions data from companies. The emissions analysis unit analyzes the companies' emissions based on the data collected by the data collection unit. The data collection unit collects removal data from green spaces. The removal analysis unit analyzes the removal amounts of green spaces based on the data collected by the data collection unit. The auction holding unit holds an auction based on the analysis results obtained by the emissions analysis unit and the removal analysis unit.

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] Conventional technologies have faced the challenge of lacking the means to effectively manage corporate emissions and green space absorption, and promote carbon neutrality.

[0005] The system according to the embodiment aims to effectively manage corporate emissions and green space absorption, and promote carbon neutrality. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an emission amount analysis unit, a sink analysis unit, and an auction holding unit. The data collection unit collects emission amount data from companies. The emission amount analysis unit analyzes the emissions of companies based on the data collected by the data collection unit. The data collection unit collects absorption amount data from green spaces. The absorption amount analysis unit analyzes the absorption amount of green spaces based on the data collected by the data collection unit. The auction holding unit holds an auction based on the analysis results obtained by the emission amount analysis unit and the absorption amount analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively manage the emissions of companies and the absorption capacity of green spaces, and promote carbon neutrality. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A carbon dioxide emissions and absorption visualization system according to an embodiment of the present invention uses AI to analyze a company's emissions and the amount of carbon dioxide absorbed by green spaces, such as farmland, mountains, and gardens, and then conducts an AI-based auction to sell and buy these items. This system uses AI to analyze the carbon dioxide emissions from corporate activities and the amount of carbon dioxide absorbed by green spaces such as farmland, mountains, and gardens. This allows companies to understand their emissions and green space owners to understand their absorption amounts. Next, the AI ​​holds an auction based on this data, allowing companies to purchase green space absorption amounts to reduce their emissions. This promotes carbon neutrality for companies and increases the amount of green space, which is expected to help curb global warming. For example, to analyze a company's emissions, the company's production data and energy consumption data are collected and analyzed by AI. For example, carbon dioxide emissions are calculated based on factory production and energy consumption data. Next, to analyze the amount of carbon dioxide absorbed by green spaces, satellite images and sensor data are collected and analyzed by AI. For example, carbon dioxide absorption is calculated based on vegetation data for farmland, mountains, and gardens. Based on the analysis results, the AI ​​holds an auction. At the auction, companies can purchase green space absorption amounts to reduce their emissions. For example, Company A purchases absorption capacity from Farmland B to reduce its own emissions. Through this transaction, Company A achieves carbon neutrality and the owner of Farmland B earns a profit. Auction bids are accepted by AI, and the transaction results are provided to ensure transparency of the transaction. For example, Company A ensures transparency by disclosing the amount bid and transaction details. This system is expected to encourage companies to become carbon neutral and increase green space, thereby curbing global warming. In this way, the carbon dioxide emissions and absorption visualization system makes corporate emissions and green space absorption visible, and by buying and selling at auction, it can promote carbon neutrality and curb global warming.

[0029] A carbon dioxide emission and absorption visualization system according to an embodiment includes a data collection unit, an emission analysis unit, an absorption analysis unit, and an auction hosting unit. The data collection unit collects emission data from a company. The emission data from the company includes, but is not limited to, carbon dioxide emission and methane emission. The data collection unit collects, for example, production data and energy consumption data from the company. For example, data for calculating carbon dioxide emission can be collected based on the production volume and energy consumption of a factory. The data collection unit also collects absorption data from green spaces. The absorption data from green spaces includes, but is not limited to, carbon dioxide absorption and oxygen generation. The data collection unit collects, for example, data from satellite images and sensors. For example, data for calculating carbon dioxide absorption can be collected based on vegetation data from farmland, mountains, and gardens. The emission analysis unit analyzes the company's emissions based on the data collected by the data collection unit. The emission analysis unit calculates carbon dioxide emission based on, for example, the production data and energy consumption data of the company. For example, the emission analysis unit can calculate carbon dioxide emission by analyzing the production volume and energy consumption of a factory. The emissions analysis unit can also analyze a company's emissions using AI. For example, the emissions analysis unit can analyze a company's emissions using an AI model and calculate the amount of carbon dioxide emissions. The absorption analysis unit analyzes the amount of absorption by green spaces based on data collected by the data collection unit. The absorption analysis unit calculates the amount of carbon dioxide absorption based on, for example, satellite images or data from sensors. For example, the absorption analysis unit can analyze vegetation data of farmland, mountains, and gardens and calculate the amount of carbon dioxide absorption. The absorption analysis unit can also analyze the amount of absorption by green spaces using AI. For example, the absorption analysis unit can analyze the amount of absorption by green spaces using an AI model and calculate the amount of carbon dioxide absorption. The auction hosting unit hosts an auction based on the analysis results obtained by the emissions analysis unit and the absorption analysis unit. For example, the auction hosting unit hosts an auction for a company to purchase absorption by green spaces to reduce its emissions.For example, the auction hosting unit can hold an auction for Company A to purchase absorption capacity of Farmland B in order to reduce its own emissions. The auction hosting unit can also hold an auction using AI. For example, the auction hosting unit can hold an auction using an AI model to hold an auction for a company to purchase absorption capacity of green space in order to reduce its emissions. In this way, the carbon dioxide emissions and absorption visualization system according to the embodiment can visualize a company's emissions and absorption capacity of green space and promote carbon neutrality and curb global warming by buying and selling them at auction.

[0030] The auction hosting unit may include a bid acceptance unit that accepts bids. The bid acceptance unit may efficiently accept bids for the auction. For example, the bid acceptance unit may include a system that accepts bids online. For example, the bid acceptance unit may provide an interface through which companies can submit bids online. The bid acceptance unit may also include a system that accepts bids offline. For example, the bid acceptance unit may provide a procedure through which companies can submit bids offline. Furthermore, the bid acceptance unit may include a function to ensure transparency of bids. For example, the bid acceptance unit may record details of bids so that they can be reviewed later. This may allow bids for the auction to be efficiently accepted.

[0031] The auction hosting unit may include a transaction result providing unit that provides transaction results. The transaction result providing unit may provide transaction results to ensure transaction transparency. For example, the transaction result providing unit may include a system that publishes transaction results of an auction. The transaction result providing unit may publish, for example, the amounts bid by companies in the auction and transaction details. The transaction result providing unit may also record transaction details so that they can be checked later. For example, the transaction result providing unit may save transaction histories so that companies can check transaction details later. The transaction result providing unit may also include a function to ensure transaction transparency. For example, the transaction result providing unit may publish transaction details so that companies can check the transparency of transactions. In this way, transaction results may be provided to ensure transaction transparency.

[0032] The emissions analysis unit can calculate carbon dioxide emissions based on the production data or energy consumption data of the company. The emissions analysis unit can accurately calculate carbon dioxide emissions based on the production data or energy consumption data of the company. For example, the emissions analysis unit analyzes the production volume or energy consumption of a factory to calculate carbon dioxide emissions. For example, the emissions analysis unit can calculate carbon dioxide emissions based on the production volume data of the factory. The emissions analysis unit can also calculate carbon dioxide emissions based on energy consumption data. For example, the emissions analysis unit can calculate carbon dioxide emissions based on the energy consumption data of the factory. Furthermore, the emissions analysis unit can analyze the emissions of the company using AI. For example, the emissions analysis unit can analyze the emissions of the company using an AI model to calculate the carbon dioxide emissions. This makes it possible to accurately calculate the emissions of the company.

[0033] The absorption amount analysis unit can calculate the amount of carbon dioxide absorption based on satellite images or data from sensors. The absorption amount analysis unit can accurately calculate the amount of carbon dioxide absorption based on satellite images or data from sensors. For example, the absorption amount analysis unit analyzes vegetation data of farmland, mountains, and gardens to calculate the amount of carbon dioxide absorption. For example, the absorption amount analysis unit can calculate the amount of carbon dioxide absorption of farmland based on satellite images. The absorption amount analysis unit can also calculate the amount of carbon dioxide absorption of mountains based on data from sensors. For example, the absorption amount analysis unit can calculate the amount of carbon dioxide absorption of gardens based on data from sensors. Furthermore, the absorption amount analysis unit can analyze the amount of absorption of green spaces using AI. For example, the absorption amount analysis unit can analyze the amount of absorption of green spaces using an AI model and calculate the amount of carbon dioxide absorption. This makes it possible to accurately calculate the amount of absorption of green spaces.

[0034] The trading result providing unit can publish trading results to ensure the transparency of trading. The trading result providing unit can publish trading results to ensure the transparency of trading. For example, the trading result providing unit is provided with a system that publishes the trading results of an auction. The trading result providing unit can publish, for example, the amount bid by a company in an auction and details of the transaction. The trading result providing unit can also record the details of the transaction so that they can be checked later. For example, the trading result providing unit can save the history of transactions so that companies can check the details of the transaction later. Furthermore, the trading result providing unit is provided with a function to ensure the transparency of trading. For example, the trading result providing unit can publish the details of the transaction so that companies can check the transparency of the transaction. This makes it possible to ensure the transparency of trading.

[0035] The data collection unit can analyze the company's past emissions data and select the optimal data collection method. The data collection unit can analyze the company's past emissions data and select the optimal data collection method. For example, the data collection unit can concentrate data collection on a specific time period based on the past emissions data. For example, the data collection unit can analyze the past emissions data and collect data by focusing on a specific production process. Furthermore, the data collection unit can strengthen data collection under specific seasons or weather conditions based on the past emissions data. For example, the data collection unit can strengthen data collection under specific seasons or weather conditions based on the past emissions data. This enables efficient data collection by selecting the optimal data collection method based on past data.

[0036] The data collection unit can filter data based on the company's production schedule or seasonal fluctuations when collecting data. The data collection unit can filter data taking into account the company's production schedule or seasonal fluctuations when collecting data. For example, the data collection unit prioritizes collecting data during peak periods based on the production schedule. For example, the data collection unit can take into account seasonal fluctuations and focus on collecting emission data for specific seasons. The data collection unit can also combine the production schedule and seasonal fluctuations to filter and collect data for periods with the greatest impact. For example, the data collection unit can combine the production schedule and seasonal fluctuations to filter and collect data for periods with the greatest impact. This enables more accurate data collection by taking into account the production schedule and seasonal fluctuations.

[0037] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. For example, the data collection unit prioritizes collecting region-specific emissions data based on the location of the company. For example, the data collection unit can collect comparative data with neighboring companies by taking into account the geographical location information. Furthermore, the data collection unit can prioritize collecting data under specific environmental conditions based on the geographical location information. For example, the data collection unit can prioritize collecting data under specific environmental conditions based on the geographical location information. In this way, highly relevant data can be efficiently collected by taking into account the geographical location information.

[0038] The data collection unit can analyze the social media activities of the company and collect related data when collecting data. The data collection unit can analyze the social media activities of the company and collect related data when collecting data. For example, the data collection unit analyzes the social media posts of the company and collects information related to emissions. For example, the data collection unit can collect emissions data for specific events based on the company's activities on social media. The data collection unit can also utilize social media data to collect data related to the company's environmental activities. For example, the data collection unit can utilize social media data to collect data related to the company's environmental activities. In this way, related data can be efficiently collected by analyzing social media activities.

[0039] During analysis, the emission amount analysis unit can adjust the level of detail of the analysis based on the importance of the company's production data and energy consumption data. During analysis, the emission amount analysis unit can adjust the level of detail of the analysis based on the importance of the company's production data and energy consumption data. For example, the emission amount analysis unit performs a detailed analysis based on the importance of the production data. For example, the emission amount analysis unit can perform a detailed analysis based on the importance of the energy consumption data. Furthermore, the emission amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both the production data and the energy consumption data. For example, the emission amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both the production data and the energy consumption data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0040] The emissions analysis unit can apply different analysis algorithms depending on the industry and scale of the company during analysis. The emissions analysis unit can apply different analysis algorithms depending on the industry and scale of the company during analysis. For example, the emissions analysis unit can apply an analysis algorithm specialized for the manufacturing process to a manufacturing company. For example, the emissions analysis unit can apply an analysis algorithm specialized for service provision to a service company. The emissions analysis unit can also apply different analysis algorithms to large companies and small and medium-sized companies depending on the scale of the company. For example, the emissions analysis unit can apply different analysis algorithms to large companies and small and medium-sized companies. This allows for more accurate analysis by applying an analysis algorithm depending on the industry and scale.

[0041] During analysis, the emissions analysis unit can determine the priority of analysis based on the time of submission of the company's emissions data. During analysis, the emissions analysis unit can determine the priority of analysis based on the time of submission of the company's emissions data. For example, the emissions analysis unit prioritizes analysis of the most recent emissions data. For example, the emissions analysis unit can analyze data that was submitted earlier later. Furthermore, the emissions analysis unit can adjust the analysis schedule based on the time of submission. For example, the emissions analysis unit can adjust the analysis schedule based on the time of submission. In this way, by determining the priority of analysis based on the time of submission, efficient analysis is possible.

[0042] The emissions analysis unit can improve the accuracy of the analysis based on related literature of the company during the analysis. The emissions analysis unit can improve the accuracy of the analysis based on related literature of the company during the analysis. For example, the emissions analysis unit can perform the analysis by referring to the latest research papers on the emissions of the company. For example, the emissions analysis unit can perform the analysis by referring to literature on industry standards of the company. Furthermore, the emissions analysis unit can perform the analysis by referring to past emissions reports of the company. For example, the emissions analysis unit can perform the analysis by referring to past emissions reports of the company. In this way, by referring to related literature, the accuracy of the analysis can be improved.

[0043] During analysis, the absorption amount analysis unit can adjust the level of detail of the analysis based on the importance of vegetation data of green spaces and meteorological data. During analysis, the absorption amount analysis unit can adjust the level of detail of the analysis based on the importance of vegetation data of green spaces and meteorological data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of vegetation data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of meteorological data. Furthermore, the absorption amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both vegetation data and meteorological data. For example, the absorption amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both vegetation data and meteorological data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0044] The absorption amount analysis unit can apply different analysis algorithms depending on the type and scale of the green space during analysis. The absorption amount analysis unit can apply different analysis algorithms depending on the type and scale of the green space during analysis. For example, the absorption amount analysis unit can apply an analysis algorithm specialized for the type of crop to agricultural land. For example, the absorption amount analysis unit can apply an analysis algorithm specialized for the type of forest to mountainous land. Furthermore, the absorption amount analysis unit can apply an analysis algorithm specialized for the vegetation of the garden to a garden. For example, the absorption amount analysis unit can apply an analysis algorithm specialized for the vegetation of the garden. In this way, by applying an analysis algorithm depending on the type and scale, more accurate analysis is possible.

[0045] During analysis, the absorption amount analysis unit can determine the priority of analysis based on the submission time of the absorption amount data of the green space. During analysis, the absorption amount analysis unit can determine the priority of analysis based on the submission time of the absorption amount data of the green space. For example, the absorption amount analysis unit prioritizes analysis of the most recent absorption amount data. For example, the absorption amount analysis unit can analyze data that was submitted earlier later. Furthermore, the absorption amount analysis unit can adjust the analysis schedule based on the submission time. For example, the absorption amount analysis unit can adjust the analysis schedule based on the submission time. In this way, by determining the priority of analysis based on the submission time, efficient analysis is possible.

[0046] The absorption amount analysis unit can improve the accuracy of the analysis based on literature related to green spaces during the analysis. The absorption amount analysis unit can improve the accuracy of the analysis based on literature related to green spaces during the analysis. For example, the absorption amount analysis unit can perform the analysis by referring to the latest research papers on the absorption amount of green spaces. For example, the absorption amount analysis unit can perform the analysis by referring to literature on types of green spaces. Furthermore, the absorption amount analysis unit can perform the analysis by referring to past absorption amount reports of green spaces. For example, the absorption amount analysis unit can perform the analysis by referring to past absorption amount reports of green spaces. In this way, by referring to related literature, the accuracy of the analysis can be improved.

[0047] When holding an auction, the auction hosting unit can select the optimal method of holding the auction, taking into consideration the balance between the emissions of the companies and the absorption capacity of the green spaces. When holding an auction, the auction hosting unit can select the optimal method of holding the auction, taking into consideration the balance between the emissions of the companies and the absorption capacity of the green spaces. For example, if a company has a large amount of emissions, the auction hosting unit will prioritize putting green spaces with a large absorption capacity up for auction. For example, if a green space has a large absorption capacity, the auction hosting unit will prioritize allowing companies with a large amount of emissions to participate in the auction. Furthermore, the auction hosting unit can select the optimal auction format, taking into consideration the balance between the companies and the green spaces. For example, the auction hosting unit can select the optimal auction format, taking into consideration the balance between the companies and the green spaces. In this way, an optimal auction can be held by taking into consideration the balance between emissions and absorption capacity.

[0048] The auction hosting unit can customize the auction rules taking into account the attribute information of participants when holding an auction. The auction hosting unit can customize the auction rules taking into account the attribute information of participants when holding an auction. For example, the auction hosting unit adjusts the auction bid unit depending on the size of the participant's company. For example, the auction hosting unit can customize the auction rules depending on the participant's industry. The auction hosting unit can also adjust the auction rules taking into account the participant's past transaction history. For example, the auction hosting unit can adjust the auction rules taking into account the participant's past transaction history. In this way, by customizing the auction rules based on the participant's attribute information, a more appropriate auction can be held.

[0049] When holding an auction, the auction hosting unit can select the optimal holding method by taking into consideration the geographical location information of participants. When holding an auction, the auction hosting unit can select the optimal holding method by taking into consideration the geographical location information of participants. For example, the auction hosting unit selects a region-specific auction format based on the location of the participants. For example, the auction hosting unit can give priority to nearby participants in the auction by taking into consideration the geographical location information. Furthermore, the auction hosting unit can hold an auction specialized for a specific region based on the geographical location information. For example, the auction hosting unit can hold an auction specialized for a specific region based on the geographical location information. In this way, the optimal auction can be held by taking into consideration the geographical location information.

[0050] The auction hosting unit can improve the accuracy of the auction by referring to related market data when holding an auction. The auction hosting unit can improve the accuracy of the auction by referring to related market data when holding an auction. For example, the auction hosting unit sets the starting price of the auction based on the latest market data. For example, the auction hosting unit can adjust the bidding unit of the auction by referring to the market data. Furthermore, the auction hosting unit can optimize the timing of the end of the auction by utilizing the market data. For example, the auction hosting unit can optimize the timing of the end of the auction by utilizing the market data. In this way, the accuracy of the auction can be improved by referring to the market data.

[0051] When accepting bids, the bid acceptance unit can select the optimal acceptance method by referring to the participant's past bidding history. When accepting bids, the bid acceptance unit can select the optimal acceptance method by referring to the participant's past bidding history. For example, the bid acceptance unit proposes the optimal bid acceptance method based on the participant's past bidding history. For example, the bid acceptance unit can refer to the past bidding history and preferentially propose a specific bidding method. Furthermore, the bid acceptance unit can analyze the participant's past bidding history and select the most efficient bid acceptance method. For example, the bid acceptance unit can analyze the participant's past bidding history and select the most efficient bid acceptance method. In this way, the optimal acceptance method can be selected by referring to the past bidding history.

[0052] The bid acceptance unit can select the optimal acceptance method in consideration of the participant's device information when accepting bids. The bid acceptance unit can select the optimal acceptance method in consideration of the participant's device information when accepting bids. For example, if the participant is using a smartphone, the bid acceptance unit can provide a acceptance method optimized for mobile devices. For example, if the participant is using a tablet, the bid acceptance unit can provide a acceptance method optimized for large screens. Furthermore, if the participant is using a desktop, the bid acceptance unit can provide a acceptance method including detailed information. For example, if the participant is using a desktop, the bid acceptance unit can provide a acceptance method including detailed information. In this way, the optimal acceptance method can be provided by taking device information into consideration.

[0053] When providing trading results, the trading result providing unit can select the optimal display method by referring to past trading data. When providing trading results, the trading result providing unit can select the optimal display method by referring to past trading data. For example, the trading result providing unit suggests the optimal display method based on past trading data. For example, the trading result providing unit can suggest a specific display method preferentially by referring to past trading data. Furthermore, the trading result providing unit can analyze past trading data and select the most efficient display method. For example, the trading result providing unit can analyze past trading data and select the most efficient display method. In this way, the optimal display method can be selected by referring to past trading data.

[0054] The trading result providing unit can select the optimal display method in consideration of the device information of the participant when providing trading results. The trading result providing unit can select the optimal display method in consideration of the device information of the participant when providing trading results. For example, if the participant is using a smartphone, the trading result providing unit can provide a display method optimized for mobile. For example, if the participant is using a tablet, the trading result providing unit can provide a display method optimized for a large screen. Furthermore, if the participant is using a desktop, the trading result providing unit can provide a display method including detailed information. For example, if the participant is using a desktop, the trading result providing unit can provide a display method including detailed information. In this way, the optimal display method can be provided by taking device information into consideration.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When collecting company emissions data, the data collection unit can filter the data by taking into account the company's production schedule and seasonal fluctuations. For example, the data collection unit prioritizes collecting data during peak periods based on the production schedule. For example, the data collection unit can take seasonal fluctuations into account and focus on collecting emissions data during specific seasons. Furthermore, the data collection unit can combine the production schedule and seasonal fluctuations to filter and collect data for periods with the greatest impact. This allows for more accurate data collection by taking production schedules and seasonal fluctuations into account.

[0057] When holding an auction, the auction hosting unit can select the optimal method of holding the auction, taking into consideration the balance between the emissions of the companies and the absorption capacity of the green spaces. For example, if a company has a large amount of emissions, the auction hosting unit will prioritize putting green spaces with a large absorption capacity up for auction. For example, if a green space has a large absorption capacity, the auction hosting unit can prioritize companies with a large amount of emissions to participate in the auction. The auction hosting unit can also select the optimal auction format, taking into consideration the balance between the companies and the green spaces. This makes it possible to hold an optimal auction by taking into consideration the balance between emissions and absorption capacity.

[0058] When providing trading results, the trading result providing unit can select the optimal display method by referring to past trading data. For example, the trading result providing unit can suggest the optimal display method based on the past trading data. For example, the trading result providing unit can refer to the past trading data and preferentially suggest a specific display method. Furthermore, the trading result providing unit can analyze the past trading data and select the most efficient display method. In this way, the optimal display method can be selected by referring to the past trading data.

[0059] During analysis, the emission amount analysis unit can adjust the level of detail of the analysis based on the importance of the company's production data and energy consumption data. For example, the emission amount analysis unit can perform a detailed analysis based on the importance of the production data. For example, the emission amount analysis unit can perform a detailed analysis based on the importance of the energy consumption data. The emission amount analysis unit can also adjust the level of detail of the analysis taking into account the importance of both the production data and the energy consumption data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0060] During analysis, the absorption amount analysis unit can adjust the level of detail of the analysis based on the importance of vegetation data of green spaces and meteorological data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of vegetation data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of meteorological data. Furthermore, the absorption amount analysis unit can adjust the level of detail of the analysis taking into account the importance of both vegetation data and meteorological data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The data collection unit collects company emissions data and green space absorption data. Company emissions data includes carbon dioxide emissions and methane emissions, and is collected, for example, based on company production data and energy consumption data. Green space absorption data includes carbon dioxide absorption and oxygen production, and is collected, for example, based on satellite images and sensor data. Step 2: The emissions analysis unit analyzes the company's emissions based on the company's emissions data collected by the data collection unit. For example, carbon dioxide emissions can be calculated based on the company's production data and energy consumption data, and analyzed using an AI model. Step 3: The absorption analysis unit analyzes the absorption amount of green spaces based on the absorption data of green spaces collected by the data collection unit. For example, the amount of carbon dioxide absorption can be calculated based on satellite images and data from sensors, and analyzed using an AI model. Step 4: The auction hosting unit holds an auction based on the analysis results obtained by the emissions analysis unit and the absorption analysis unit. For example, an auction can be held for a company to purchase absorption capacity in green space to reduce emissions, and the auction can be held using an AI model.

[0063] (Example 2) A carbon dioxide emissions and absorption visualization system according to an embodiment of the present invention uses AI to analyze a company's emissions and the amount of carbon dioxide absorbed by green spaces, such as farmland, mountains, and gardens, and then conducts an AI-based auction to sell and buy these items. This system uses AI to analyze the carbon dioxide emissions from corporate activities and the amount of carbon dioxide absorbed by green spaces such as farmland, mountains, and gardens. This allows companies to understand their emissions and green space owners to understand their absorption amounts. Next, the AI ​​holds an auction based on this data, allowing companies to purchase green space absorption amounts to reduce their emissions. This promotes carbon neutrality for companies and increases the amount of green space, which is expected to help curb global warming. For example, to analyze a company's emissions, the company's production data and energy consumption data are collected and analyzed by AI. For example, carbon dioxide emissions are calculated based on factory production and energy consumption data. Next, to analyze the amount of carbon dioxide absorbed by green spaces, satellite images and sensor data are collected and analyzed by AI. For example, carbon dioxide absorption is calculated based on vegetation data for farmland, mountains, and gardens. Based on the analysis results, the AI ​​holds an auction. At the auction, companies can purchase green space absorption amounts to reduce their emissions. For example, Company A purchases absorption capacity from Farmland B to reduce its own emissions. Through this transaction, Company A achieves carbon neutrality and the owner of Farmland B earns a profit. Auction bids are accepted by AI, and the transaction results are provided to ensure transparency of the transaction. For example, Company A ensures transparency by disclosing the amount bid and transaction details. This system is expected to encourage companies to become carbon neutral and increase green space, thereby curbing global warming. In this way, the carbon dioxide emissions and absorption visualization system makes corporate emissions and green space absorption visible, and by buying and selling at auction, it can promote carbon neutrality and curb global warming.

[0064] A carbon dioxide emission and absorption visualization system according to an embodiment includes a data collection unit, an emission analysis unit, an absorption analysis unit, and an auction hosting unit. The data collection unit collects emission data from a company. The emission data from the company includes, but is not limited to, carbon dioxide emission and methane emission. The data collection unit collects, for example, production data and energy consumption data from the company. For example, data for calculating carbon dioxide emission can be collected based on the production volume and energy consumption of a factory. The data collection unit also collects absorption data from green spaces. The absorption data from green spaces includes, but is not limited to, carbon dioxide absorption and oxygen generation. The data collection unit collects, for example, data from satellite images and sensors. For example, data for calculating carbon dioxide absorption can be collected based on vegetation data from farmland, mountains, and gardens. The emission analysis unit analyzes the company's emissions based on the data collected by the data collection unit. The emission analysis unit calculates carbon dioxide emission based on, for example, the production data and energy consumption data of the company. For example, the emission analysis unit can calculate carbon dioxide emission by analyzing the production volume and energy consumption of a factory. The emissions analysis unit can also analyze a company's emissions using AI. For example, the emissions analysis unit can analyze a company's emissions using an AI model and calculate the amount of carbon dioxide emissions. The absorption analysis unit analyzes the amount of absorption by green spaces based on data collected by the data collection unit. The absorption analysis unit calculates the amount of carbon dioxide absorption based on, for example, satellite images or data from sensors. For example, the absorption analysis unit can analyze vegetation data of farmland, mountains, and gardens and calculate the amount of carbon dioxide absorption. The absorption analysis unit can also analyze the amount of absorption by green spaces using AI. For example, the absorption analysis unit can analyze the amount of absorption by green spaces using an AI model and calculate the amount of carbon dioxide absorption. The auction hosting unit hosts an auction based on the analysis results obtained by the emissions analysis unit and the absorption analysis unit. For example, the auction hosting unit hosts an auction for a company to purchase absorption by green spaces to reduce its emissions.For example, the auction hosting unit can hold an auction for Company A to purchase absorption capacity of Farmland B in order to reduce its own emissions. The auction hosting unit can also hold an auction using AI. For example, the auction hosting unit can hold an auction using an AI model to hold an auction for a company to purchase absorption capacity of green space in order to reduce its emissions. In this way, the carbon dioxide emissions and absorption visualization system according to the embodiment can visualize a company's emissions and absorption capacity of green space and promote carbon neutrality and curb global warming by buying and selling them at auction.

[0065] The auction hosting unit may include a bid acceptance unit that accepts bids. The bid acceptance unit may efficiently accept bids for the auction. For example, the bid acceptance unit may include a system that accepts bids online. For example, the bid acceptance unit may provide an interface through which companies can submit bids online. The bid acceptance unit may also include a system that accepts bids offline. For example, the bid acceptance unit may provide a procedure through which companies can submit bids offline. Furthermore, the bid acceptance unit may include a function to ensure transparency of bids. For example, the bid acceptance unit may record details of bids so that they can be reviewed later. This may allow bids for the auction to be efficiently accepted.

[0066] The auction hosting unit may include a transaction result providing unit that provides transaction results. The transaction result providing unit may provide transaction results to ensure transaction transparency. For example, the transaction result providing unit may include a system that publishes transaction results of an auction. The transaction result providing unit may publish, for example, the amounts bid by companies in the auction and transaction details. The transaction result providing unit may also record transaction details so that they can be checked later. For example, the transaction result providing unit may save transaction histories so that companies can check transaction details later. The transaction result providing unit may also include a function to ensure transaction transparency. For example, the transaction result providing unit may publish transaction details so that companies can check the transparency of transactions. In this way, transaction results may be provided to ensure transaction transparency.

[0067] The emissions analysis unit can calculate carbon dioxide emissions based on the production data or energy consumption data of the company. The emissions analysis unit can accurately calculate carbon dioxide emissions based on the production data or energy consumption data of the company. For example, the emissions analysis unit analyzes the production volume or energy consumption of a factory to calculate carbon dioxide emissions. For example, the emissions analysis unit can calculate carbon dioxide emissions based on the production volume data of the factory. The emissions analysis unit can also calculate carbon dioxide emissions based on energy consumption data. For example, the emissions analysis unit can calculate carbon dioxide emissions based on the energy consumption data of the factory. Furthermore, the emissions analysis unit can analyze the emissions of the company using AI. For example, the emissions analysis unit can analyze the emissions of the company using an AI model to calculate the carbon dioxide emissions. This makes it possible to accurately calculate the emissions of the company.

[0068] The absorption amount analysis unit can calculate the amount of carbon dioxide absorption based on satellite images or data from sensors. The absorption amount analysis unit can accurately calculate the amount of carbon dioxide absorption based on satellite images or data from sensors. For example, the absorption amount analysis unit analyzes vegetation data of farmland, mountains, and gardens to calculate the amount of carbon dioxide absorption. For example, the absorption amount analysis unit can calculate the amount of carbon dioxide absorption of farmland based on satellite images. The absorption amount analysis unit can also calculate the amount of carbon dioxide absorption of mountains based on data from sensors. For example, the absorption amount analysis unit can calculate the amount of carbon dioxide absorption of gardens based on data from sensors. Furthermore, the absorption amount analysis unit can analyze the amount of absorption of green spaces using AI. For example, the absorption amount analysis unit can analyze the amount of absorption of green spaces using an AI model and calculate the amount of carbon dioxide absorption. This makes it possible to accurately calculate the amount of absorption of green spaces.

[0069] The trading result providing unit can publish trading results to ensure the transparency of trading. The trading result providing unit can publish trading results to ensure the transparency of trading. For example, the trading result providing unit is provided with a system that publishes the trading results of an auction. The trading result providing unit can publish, for example, the amount bid by a company in an auction and details of the transaction. The trading result providing unit can also record the details of the transaction so that they can be checked later. For example, the trading result providing unit can save the history of transactions so that companies can check the details of the transaction later. Furthermore, the trading result providing unit is provided with a function to ensure the transparency of trading. For example, the trading result providing unit can publish the details of the transaction so that companies can check the transparency of the transaction. This makes it possible to ensure the transparency of trading.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can reduce the frequency of data collection to reduce the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect detailed data. Furthermore, if the user is in a hurry, the data collection unit can quickly collect data to collect the minimum amount of data necessary. For example, if the user is in a hurry, the data collection unit can quickly collect data to collect the minimum amount of data necessary. This allows the user's burden to be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The data collection unit can analyze the company's past emissions data and select the optimal data collection method. The data collection unit can analyze the company's past emissions data and select the optimal data collection method. For example, the data collection unit can concentrate data collection on a specific time period based on the past emissions data. For example, the data collection unit can analyze the past emissions data and collect data by focusing on a specific production process. Furthermore, the data collection unit can strengthen data collection under specific seasons or weather conditions based on the past emissions data. For example, the data collection unit can strengthen data collection under specific seasons or weather conditions based on the past emissions data. This enables efficient data collection by selecting the optimal data collection method based on past data.

[0072] The data collection unit can filter data based on the company's production schedule or seasonal fluctuations when collecting data. The data collection unit can filter data taking into account the company's production schedule or seasonal fluctuations when collecting data. For example, the data collection unit prioritizes collecting data during peak periods based on the production schedule. For example, the data collection unit can take into account seasonal fluctuations and focus on collecting emission data for specific seasons. The data collection unit can also combine the production schedule and seasonal fluctuations to filter and collect data for periods with the greatest impact. For example, the data collection unit can combine the production schedule and seasonal fluctuations to filter and collect data for periods with the greatest impact. This enables more accurate data collection by taking into account the production schedule and seasonal fluctuations.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The data collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the data collection unit prioritizes collecting only data of high importance. For example, when the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, when the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. For example, the data collection unit prioritizes collecting region-specific emissions data based on the location of the company. For example, the data collection unit can collect comparative data with neighboring companies by taking into account the geographical location information. Furthermore, the data collection unit can prioritize collecting data under specific environmental conditions based on the geographical location information. For example, the data collection unit can prioritize collecting data under specific environmental conditions based on the geographical location information. In this way, highly relevant data can be efficiently collected by taking into account the geographical location information.

[0075] The data collection unit can analyze the social media activities of the company and collect related data when collecting data. The data collection unit can analyze the social media activities of the company and collect related data when collecting data. For example, the data collection unit analyzes the social media posts of the company and collects information related to emissions. For example, the data collection unit can collect emissions data for specific events based on the company's activities on social media. The data collection unit can also utilize social media data to collect data related to the company's environmental activities. For example, the data collection unit can utilize social media data to collect data related to the company's environmental activities. In this way, related data can be efficiently collected by analyzing social media activities.

[0076] The emission analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The emission analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the emission analysis unit can provide a simple, highly visible analysis result. For example, if the user is relaxed, the emission analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the emission analysis unit can provide a summary of the analysis result. For example, if the user is in a hurry, the emission analysis unit can provide a summary of the analysis result. In this way, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] During analysis, the emission amount analysis unit can adjust the level of detail of the analysis based on the importance of the company's production data and energy consumption data. During analysis, the emission amount analysis unit can adjust the level of detail of the analysis based on the importance of the company's production data and energy consumption data. For example, the emission amount analysis unit performs a detailed analysis based on the importance of the production data. For example, the emission amount analysis unit can perform a detailed analysis based on the importance of the energy consumption data. Furthermore, the emission amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both the production data and the energy consumption data. For example, the emission amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both the production data and the energy consumption data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0078] The emissions analysis unit can apply different analysis algorithms depending on the industry and scale of the company during analysis. The emissions analysis unit can apply different analysis algorithms depending on the industry and scale of the company during analysis. For example, the emissions analysis unit can apply an analysis algorithm specialized for the manufacturing process to a manufacturing company. For example, the emissions analysis unit can apply an analysis algorithm specialized for service provision to a service company. The emissions analysis unit can also apply different analysis algorithms to large companies and small and medium-sized companies depending on the scale of the company. For example, the emissions analysis unit can apply different analysis algorithms to large companies and small and medium-sized companies. This allows for more accurate analysis by applying an analysis algorithm depending on the industry and scale.

[0079] The emission analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The emission analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the emission analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the emission analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the emission analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the emission analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby enabling a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] During analysis, the emissions analysis unit can determine the priority of analysis based on the time of submission of the company's emissions data. During analysis, the emissions analysis unit can determine the priority of analysis based on the time of submission of the company's emissions data. For example, the emissions analysis unit prioritizes analysis of the most recent emissions data. For example, the emissions analysis unit can analyze data that was submitted earlier later. Furthermore, the emissions analysis unit can adjust the analysis schedule based on the time of submission. For example, the emissions analysis unit can adjust the analysis schedule based on the time of submission. In this way, by determining the priority of analysis based on the time of submission, efficient analysis is possible.

[0081] The emissions analysis unit can improve the accuracy of the analysis based on related literature of the company during the analysis. The emissions analysis unit can improve the accuracy of the analysis based on related literature of the company during the analysis. For example, the emissions analysis unit can perform the analysis by referring to the latest research papers on the emissions of the company. For example, the emissions analysis unit can perform the analysis by referring to literature on industry standards of the company. Furthermore, the emissions analysis unit can perform the analysis by referring to past emissions reports of the company. For example, the emissions analysis unit can perform the analysis by referring to past emissions reports of the company. In this way, by referring to related literature, the accuracy of the analysis can be improved.

[0082] The absorption amount analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The absorption amount analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the absorption amount analysis unit can provide simple, highly visible analysis results. For example, if the user is relaxed, the absorption amount analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the absorption amount analysis unit can provide analysis results that are concise. For example, if the user is in a hurry, the absorption amount analysis unit can provide analysis results that are concise. In this way, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During analysis, the absorption amount analysis unit can adjust the level of detail of the analysis based on the importance of vegetation data of green spaces and meteorological data. During analysis, the absorption amount analysis unit can adjust the level of detail of the analysis based on the importance of vegetation data of green spaces and meteorological data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of vegetation data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of meteorological data. Furthermore, the absorption amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both vegetation data and meteorological data. For example, the absorption amount analysis unit can adjust the level of detail of the analysis by taking into account the importance of both vegetation data and meteorological data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0084] The absorption amount analysis unit can apply different analysis algorithms depending on the type and scale of the green space during analysis. The absorption amount analysis unit can apply different analysis algorithms depending on the type and scale of the green space during analysis. For example, the absorption amount analysis unit can apply an analysis algorithm specialized for the type of crop to agricultural land. For example, the absorption amount analysis unit can apply an analysis algorithm specialized for the type of forest to mountainous land. Furthermore, the absorption amount analysis unit can apply an analysis algorithm specialized for the vegetation of the garden to a garden. For example, the absorption amount analysis unit can apply an analysis algorithm specialized for the vegetation of the garden. In this way, by applying an analysis algorithm depending on the type and scale, more accurate analysis is possible.

[0085] The absorption amount analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The absorption amount analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the absorption amount analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the absorption amount analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the absorption amount analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the absorption amount analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby enabling a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During analysis, the absorption amount analysis unit can determine the priority of analysis based on the submission time of the absorption amount data of the green space. During analysis, the absorption amount analysis unit can determine the priority of analysis based on the submission time of the absorption amount data of the green space. For example, the absorption amount analysis unit prioritizes analysis of the most recent absorption amount data. For example, the absorption amount analysis unit can analyze data that was submitted earlier later. Furthermore, the absorption amount analysis unit can adjust the analysis schedule based on the submission time. For example, the absorption amount analysis unit can adjust the analysis schedule based on the submission time. In this way, by determining the priority of analysis based on the submission time, efficient analysis is possible.

[0087] The absorption amount analysis unit can improve the accuracy of the analysis based on literature related to green spaces during the analysis. The absorption amount analysis unit can improve the accuracy of the analysis based on literature related to green spaces during the analysis. For example, the absorption amount analysis unit can perform the analysis by referring to the latest research papers on the absorption amount of green spaces. For example, the absorption amount analysis unit can perform the analysis by referring to literature on types of green spaces. Furthermore, the absorption amount analysis unit can perform the analysis by referring to past absorption amount reports of green spaces. For example, the absorption amount analysis unit can perform the analysis by referring to past absorption amount reports of green spaces. In this way, by referring to related literature, the accuracy of the analysis can be improved.

[0088] The auction hosting unit can estimate a user's emotions and adjust the timing of the auction based on the estimated user emotions. The auction hosting unit can estimate a user's emotions and adjust the timing of the auction based on the estimated user emotions. For example, the auction hosting unit postpones the auction if the user is stressed. For example, the auction hosting unit can expedite the auction if the user is relaxed. Furthermore, the auction hosting unit can quickly hold the auction if the user is in a hurry. For example, the auction hosting unit can quickly hold the auction if the user is in a hurry. In this way, by adjusting the timing of the auction based on the user's emotions, the auction can be held at the optimal timing for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When holding an auction, the auction hosting unit can select the optimal method of holding the auction, taking into consideration the balance between the emissions of the companies and the absorption capacity of the green spaces. When holding an auction, the auction hosting unit can select the optimal method of holding the auction, taking into consideration the balance between the emissions of the companies and the absorption capacity of the green spaces. For example, if a company has a large amount of emissions, the auction hosting unit will prioritize putting green spaces with a large absorption capacity up for auction. For example, if a green space has a large absorption capacity, the auction hosting unit will prioritize allowing companies with a large amount of emissions to participate in the auction. Furthermore, the auction hosting unit can select the optimal auction format, taking into consideration the balance between the companies and the green spaces. For example, the auction hosting unit can select the optimal auction format, taking into consideration the balance between the companies and the green spaces. In this way, an optimal auction can be held by taking into consideration the balance between emissions and absorption capacity.

[0090] The auction hosting unit can customize the auction rules taking into account the attribute information of participants when holding an auction. The auction hosting unit can customize the auction rules taking into account the attribute information of participants when holding an auction. For example, the auction hosting unit adjusts the auction bid unit depending on the size of the participant's company. For example, the auction hosting unit can customize the auction rules depending on the participant's industry. The auction hosting unit can also adjust the auction rules taking into account the participant's past transaction history. For example, the auction hosting unit can adjust the auction rules taking into account the participant's past transaction history. In this way, by customizing the auction rules based on the participant's attribute information, a more appropriate auction can be held.

[0091] The auction hosting unit can estimate a user's emotions and adjust the display method of the auction based on the estimated user emotions. The auction hosting unit can estimate a user's emotions and adjust the display method of the auction based on the estimated user emotions. For example, if the user is nervous, the auction hosting unit can provide a simple, highly visible display method. For example, if the user is relaxed, the auction hosting unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the auction hosting unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the auction hosting unit can provide a display method that focuses on the main points. This allows the auction display method to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] When holding an auction, the auction hosting unit can select the optimal holding method by taking into consideration the geographical location information of participants. When holding an auction, the auction hosting unit can select the optimal holding method by taking into consideration the geographical location information of participants. For example, the auction hosting unit selects a region-specific auction format based on the location of the participants. For example, the auction hosting unit can give priority to nearby participants in the auction by taking into consideration the geographical location information. Furthermore, the auction hosting unit can hold an auction specialized for a specific region based on the geographical location information. For example, the auction hosting unit can hold an auction specialized for a specific region based on the geographical location information. In this way, the optimal auction can be held by taking into consideration the geographical location information.

[0093] The auction hosting unit can improve the accuracy of the auction by referring to related market data when holding an auction. The auction hosting unit can improve the accuracy of the auction by referring to related market data when holding an auction. For example, the auction hosting unit sets the starting price of the auction based on the latest market data. For example, the auction hosting unit can adjust the bidding unit of the auction by referring to the market data. Furthermore, the auction hosting unit can optimize the timing of the end of the auction by utilizing the market data. For example, the auction hosting unit can optimize the timing of the end of the auction by utilizing the market data. In this way, the accuracy of the auction can be improved by referring to the market data.

[0094] The bid acceptance unit can estimate a user's emotions and adjust a bid acceptance method based on the estimated user emotions. The bid acceptance unit can estimate a user's emotions and adjust a bid acceptance method based on the estimated user emotions. For example, if a user is nervous, the bid acceptance unit can provide a simple and highly visible acceptance method. For example, if a user is relaxed, the bid acceptance unit can provide a bid acceptance method that includes detailed information. Furthermore, if a user is in a hurry, the bid acceptance unit can provide a method for quickly accepting bids. For example, if a user is in a hurry, the bid acceptance unit can provide a method for quickly accepting bids. In this way, by adjusting the bid acceptance method according to the user's emotions, it is possible to provide an optimal acceptance method for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] When accepting bids, the bid acceptance unit can select the optimal acceptance method by referring to the participant's past bidding history. When accepting bids, the bid acceptance unit can select the optimal acceptance method by referring to the participant's past bidding history. For example, the bid acceptance unit proposes the optimal bid acceptance method based on the participant's past bidding history. For example, the bid acceptance unit can refer to the past bidding history and preferentially propose a specific bidding method. Furthermore, the bid acceptance unit can analyze the participant's past bidding history and select the most efficient bid acceptance method. For example, the bid acceptance unit can analyze the participant's past bidding history and select the most efficient bid acceptance method. In this way, the optimal acceptance method can be selected by referring to the past bidding history.

[0096] The bid acceptance unit can estimate a user's emotion and determine the priority of bids based on the estimated user's emotion. The bid acceptance unit can estimate a user's emotion and determine the priority of bids based on the estimated user's emotion. For example, if a user is nervous, the bid acceptance unit can preferentially accept bids with high importance. For example, if a user is relaxed, the bid acceptance unit can preferentially accept detailed bids. Furthermore, if a user is in a hurry, the bid acceptance unit can preferentially accept bids that can be accepted quickly. For example, if a user is in a hurry, the bid acceptance unit can preferentially accept bids that can be accepted quickly. This enables efficient bid acceptance by determining the priority of bids according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The bid acceptance unit can select the optimal acceptance method in consideration of the participant's device information when accepting bids. The bid acceptance unit can select the optimal acceptance method in consideration of the participant's device information when accepting bids. For example, if the participant is using a smartphone, the bid acceptance unit can provide a acceptance method optimized for mobile devices. For example, if the participant is using a tablet, the bid acceptance unit can provide a acceptance method optimized for large screens. Furthermore, if the participant is using a desktop, the bid acceptance unit can provide a acceptance method including detailed information. For example, if the participant is using a desktop, the bid acceptance unit can provide a acceptance method including detailed information. In this way, the optimal acceptance method can be provided by taking device information into consideration.

[0098] The trading result providing unit can estimate a user's emotions and adjust the display method of trading results based on the estimated user emotions. The trading result providing unit can estimate a user's emotions and adjust the display method of trading results based on the estimated user emotions. For example, if the user is nervous, the trading result providing unit can provide a simple, highly visible display method. For example, if the user is relaxed, the trading result providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the trading result providing unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the trading result providing unit can provide a display method that focuses on the main points. This allows the display method of trading results to be adjusted according to the user's emotions, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] When providing trading results, the trading result providing unit can select the optimal display method by referring to past trading data. When providing trading results, the trading result providing unit can select the optimal display method by referring to past trading data. For example, the trading result providing unit suggests the optimal display method based on past trading data. For example, the trading result providing unit can suggest a specific display method preferentially by referring to past trading data. Furthermore, the trading result providing unit can analyze past trading data and select the most efficient display method. For example, the trading result providing unit can analyze past trading data and select the most efficient display method. In this way, the optimal display method can be selected by referring to past trading data.

[0100] The trading result providing unit can estimate the user's emotions and prioritize trading results based on the estimated user emotions. The trading result providing unit can estimate the user's emotions and prioritize trading results based on the estimated user emotions. For example, if the user is nervous, the trading result providing unit can prioritize displaying important trading results. For example, if the user is relaxed, the trading result providing unit can prioritize displaying detailed trading results. Furthermore, if the user is in a hurry, the trading result providing unit can prioritize displaying trading results that can be checked quickly. For example, if the user is in a hurry, the trading result providing unit can prioritize displaying trading results that can be checked quickly. In this way, by prioritizing trading results according to the user's emotions, important trading results can be prioritized and displayed. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The trading result providing unit can select the optimal display method in consideration of the device information of the participant when providing trading results. The trading result providing unit can select the optimal display method in consideration of the device information of the participant when providing trading results. For example, if the participant is using a smartphone, the trading result providing unit can provide a display method optimized for mobile. For example, if the participant is using a tablet, the trading result providing unit can provide a display method optimized for a large screen. Furthermore, if the participant is using a desktop, the trading result providing unit can provide a display method including detailed information. For example, if the participant is using a desktop, the trading result providing unit can provide a display method including detailed information. In this way, the optimal display method can be provided by taking device information into consideration. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, emission analysis unit, absorption analysis unit, and auction holding unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects company production data and energy consumption data using the camera 42 and sensors of the smart device 14, and processes the data using the control unit 46A. The emission analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes carbon dioxide emissions based on the collected data. The absorption analysis unit is realized, for example, by the camera 42 and sensors of the smart device 14, and processes the data using the control unit 46A. The auction holding unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and holds an auction based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, emission analysis unit, absorption analysis unit, and auction holding unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects company production data and energy consumption data using the camera 42 and sensors of the smart glasses 214, and processes the data using the control unit 46A. The emission analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes carbon dioxide emissions based on the collected data. The absorption analysis unit is realized, for example, by the camera 42 and sensors of the smart glasses 214, and processes the data using the control unit 46A. The auction holding unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and holds an auction based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, emission analysis unit, absorption analysis unit, and auction holding unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit collects production data and energy consumption data of a company using the camera 42 and sensors of the headset terminal 314, and processes the data by the control unit 46A. The emission analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes carbon dioxide emissions based on the collected data. The absorption analysis unit collects vegetation data of green spaces using the camera 42 and sensors of the headset terminal 314, and processes the data by the control unit 46A. The auction holding unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and holds an auction based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, emission analysis unit, absorption analysis unit, and auction holding unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects production data and energy consumption data of a company using the camera 42 and sensors of the robot 414, and processes the data by the control unit 46A. The emission analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes carbon dioxide emissions based on the collected data. The absorption analysis unit collects vegetation data of green spaces using the camera 42 and sensors of the robot 414, and processes the data by the control unit 46A. The auction holding unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and holds an auction based on the analysis results.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When collecting company emissions data, the data collection unit can filter the data by taking into account the company's production schedule and seasonal fluctuations. For example, the data collection unit prioritizes collecting data during peak periods based on the production schedule. For example, the data collection unit can take seasonal fluctuations into account and focus on collecting emissions data during specific seasons. Furthermore, the data collection unit can combine the production schedule and seasonal fluctuations to filter and collect data for periods with the greatest impact. This allows for more accurate data collection by taking production schedules and seasonal fluctuations into account.

[0104] When holding an auction, the auction hosting unit can select the optimal method of holding the auction, taking into consideration the balance between the emissions of the companies and the absorption capacity of the green spaces. For example, if a company has a large amount of emissions, the auction hosting unit will prioritize putting green spaces with a large absorption capacity up for auction. For example, if a green space has a large absorption capacity, the auction hosting unit can prioritize companies with a large amount of emissions to participate in the auction. The auction hosting unit can also select the optimal auction format, taking into consideration the balance between the companies and the green spaces. This makes it possible to hold an optimal auction by taking into consideration the balance between emissions and absorption capacity.

[0105] When providing trading results, the trading result providing unit can select the optimal display method by referring to past trading data. For example, the trading result providing unit can suggest the optimal display method based on the past trading data. For example, the trading result providing unit can refer to the past trading data and preferentially suggest a specific display method. Furthermore, the trading result providing unit can analyze the past trading data and select the most efficient display method. In this way, the optimal display method can be selected by referring to the past trading data.

[0106] During analysis, the emission amount analysis unit can adjust the level of detail of the analysis based on the importance of the company's production data and energy consumption data. For example, the emission amount analysis unit can perform a detailed analysis based on the importance of the production data. For example, the emission amount analysis unit can perform a detailed analysis based on the importance of the energy consumption data. The emission amount analysis unit can also adjust the level of detail of the analysis taking into account the importance of both the production data and the energy consumption data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0107] During analysis, the absorption amount analysis unit can adjust the level of detail of the analysis based on the importance of vegetation data of green spaces and meteorological data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of vegetation data. For example, the absorption amount analysis unit can perform a detailed analysis based on the importance of meteorological data. Furthermore, the absorption amount analysis unit can adjust the level of detail of the analysis taking into account the importance of both vegetation data and meteorological data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0108] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the data collection unit can reduce the frequency of data collection to reduce the burden on the user. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect detailed data. Furthermore, if the user is in a hurry, the data collection unit can quickly collect data to collect the minimum amount of data necessary. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0109] The emission amount analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the emission amount analysis unit can provide simple, highly visible analysis results. For example, if the user is relaxed, the emission amount analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the emission amount analysis unit can provide analysis results that are concise. In this way, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0110] The auction hosting unit can estimate the user's emotions and adjust the timing of the auction based on the estimated user's emotions. For example, the auction hosting unit postpones the auction if the user is feeling stressed. For example, the auction hosting unit can expedite the auction if the user is relaxed. Furthermore, the auction hosting unit can quickly hold the auction if the user is in a hurry. In this way, by adjusting the timing of the auction according to the user's emotions, the auction can be held at the optimal timing for the user.

[0111] The bid acceptance unit can estimate the user's emotions and adjust the bid acceptance method based on the estimated user's emotions. For example, if the user is nervous, the bid acceptance unit can provide a simple and highly visible acceptance method. For example, if the user is relaxed, the bid acceptance unit can provide a acceptance method that includes detailed information. Furthermore, if the user is in a hurry, the bid acceptance unit can provide a method for quickly accepting bids. In this way, by adjusting the bid acceptance method according to the user's emotions, it is possible to provide the optimal acceptance method for the user.

[0112] The trading result providing unit can estimate the user's emotions and adjust the display method of trading results based on the estimated user's emotions. For example, if the user is nervous, the trading result providing unit can provide a simple, highly visible display method. For example, if the user is relaxed, the trading result providing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the trading result providing unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of trading results according to the user's emotions, it is possible to provide a display that is easy for the user to understand.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The data collection unit collects company emissions data and green space absorption data. Company emissions data includes carbon dioxide emissions and methane emissions, and is collected, for example, based on company production data and energy consumption data. Green space absorption data includes carbon dioxide absorption and oxygen production, and is collected, for example, based on satellite images and sensor data. Step 2: The emissions analysis unit analyzes the company's emissions based on the company's emissions data collected by the data collection unit. For example, carbon dioxide emissions can be calculated based on the company's production data and energy consumption data, and analyzed using an AI model. Step 3: The absorption analysis unit analyzes the absorption amount of green spaces based on the absorption data of green spaces collected by the data collection unit. For example, the amount of carbon dioxide absorption can be calculated based on satellite images and data from sensors, and analyzed using an AI model. Step 4: The auction hosting unit holds an auction based on the analysis results obtained by the emissions analysis unit and the absorption analysis unit. For example, an auction can be held for a company to purchase absorption capacity in green space to reduce emissions, and the auction can be held using an AI model.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 collection department that collects company emissions data and green space absorption data; an emission analysis unit that analyzes the emission amount of the company based on the emission data collected by the data collection unit; an absorption amount analysis unit that analyzes the absorption amount of green spaces based on the absorption amount data collected by the data collection unit; an auction holding unit that holds an auction based on the analysis results obtained by the emission amount analysis unit and the absorption amount analysis unit; Equipped with A system characterized by:

2. The auction holding unit: Equipped with a bid reception section for accepting bids 2. The system of claim 1.

3. The auction holding unit: A trading result providing unit is provided to provide trading results.

2. The system of claim 1.

4. The emission amount analysis unit Calculate carbon dioxide emissions based on company production or energy consumption data 2. The system of claim 1.

5. The absorption amount analysis unit Calculating carbon dioxide absorption based on satellite images or sensor data 2. The system of claim 1.

6. The trading result providing unit Publish trading results to ensure transparency 4. The system of claim 3.

7. The data collection unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The data collection unit Analyze a company's historical emissions data and select the most appropriate data collection method 2. The system of claim 1.

9. The data collection unit During data collection, filter the data based on your company's production schedule or seasonal fluctuations 2. The system of claim 1.

10. The data collection unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

Images