system

Abstract

The system according to this embodiment aims to effectively link individual eco-activities to corporate carbon offsetting. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, an aggregation unit, and a sales unit. The collection unit collects data on individuals' eco-activities. The proposal unit proposes SDGs activity plans based on the data collected by the collection unit. The aggregation unit aggregates the results of the SDGs activities proposed by the proposal unit and converts them into credits. The sales unit sells the credits converted into credits by the aggregation unit to companies.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the effective connection of an individual's eco-activities to a company's carbon offset has not been fully achieved, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively connect an individual's eco-activities to a company's carbon offset.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a proposal unit, an aggregation unit, and a sales unit. The data collection unit collects data on individuals' eco-activities. The proposal unit proposes SDGs activity plans based on the data collected by the data collection unit. The aggregation unit aggregates the results of the SDGs activities proposed by the proposal unit and converts them into credits. The sales unit sells the credits converted by the aggregation unit to companies. [Effects of the Invention]

[0007] The system according to this embodiment can effectively link individual eco-activities to corporate carbon offsetting. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The SDGs support service system according to an embodiment of the present invention is a system that connects companies that utilize the "carbon offset" system, which involves investing in greenhouse gas reduction activities commensurate with emissions to cover the portion of greenhouse gas emissions that could not be fully suppressed through economic activities, with individuals who engage in eco-friendly activities on a daily basis. The SDGs support service system uses a generating AI to support individuals who are currently engaged in or wish to engage in SDGs activities. The generating AI collects data on individuals' eco-friendly activities and proposes SDGs activity plans tailored to each household's situation. Examples include updating home appliances, introducing LED lighting, installing solar power generation, and providing guidance on energy-saving techniques. It also proposes methods for reducing greenhouse gas emissions from movement, such as walking, cycling, and using public transportation. Next, companies aggregate the results of individuals' eco-friendly activities supported by the generating AI and sell them to other companies as "J-Credits." J-Credits are a system that makes it possible to buy and sell credits representing reductions and absorptions of greenhouse gas emissions such as CO2. Companies can offset their greenhouse gas emissions by purchasing these credits. Furthermore, the profits earned are returned to individual participants in the form of points. These points can be used for various purposes, including donating to SDGs activities, exchanging them for products from participating companies, purchasing energy-saving home appliances, buying locally produced food, and exchanging them for points in electronic payment systems. This system promotes individual eco-activities, helps companies reduce greenhouse gas emissions, and aims to realize a carbon-neutral society. In this way, the SDGs support service system can support individual eco-activities and promote greenhouse gas emission reductions by converting the results into credits that can be sold to companies.

[0029] The SDGs support service system according to this embodiment comprises a data collection unit, a proposal unit, an aggregation unit, and a sales unit. The data collection unit collects data on an individual's eco-activities. The data collection unit can collect data, for example, by taking pictures with a camera inside the home or by transmitting electricity bill data. For example, the data collection unit can use a camera inside the home to photograph eco-activities and collect data. The data collection unit can also obtain electricity bill data from a smart meter and evaluate the effectiveness of eco-activities. Furthermore, the data collection unit can use sensors inside the home to monitor energy consumption in real time and collect data. The proposal unit uses a generation AI to propose an SDGs activity plan based on the data collected by the data collection unit. For example, the proposal unit can propose an SDGs activity plan such as updating home appliances, introducing LED lighting, introducing solar power generation, or providing guidance on energy-saving techniques. For example, the proposal unit can evaluate the energy efficiency of home appliances and propose updating them to more energy-efficient appliances. The proposal unit can also propose the introduction of LED lighting to reduce energy consumption. Furthermore, the proposal unit can propose the introduction of solar power generation to promote the use of renewable energy. The Aggregation Department aggregates the results of SDGs activities proposed by the Proposal Department and converts them into credits. For example, the Aggregation Department can aggregate the results of individual eco-activities and convert them into J-Credits. For example, the Aggregation Department can quantify the results of eco-activities and certify them as credits. The Aggregation Department can also evaluate the results of eco-activities and calculate the value of the credits. Furthermore, the Aggregation Department can register the results of eco-activities in a database and manage the credits. The Sales Department sells the credits converted into credits by the Aggregation Department to companies. For example, the Sales Department can sell the converted J-Credits to companies to offset their greenhouse gas emissions. For example, the Sales Department can set the sales price of the credits and provide it to companies. The Sales Department can also conclude sales contracts for the credits and provide them to companies. Furthermore, the Sales Department can manage the sales performance of the credits and report it to companies.As a result, the SDGs support service system according to this embodiment can support individuals' eco-activities and promote the reduction of greenhouse gas emissions by converting the results into credits and selling them to companies.

[0030] The data collection unit collects data on individuals' eco-friendly activities. Specifically, it uses in-home cameras to film eco-friendly activities and collects data. For example, in-home cameras can record recycling practices and the use of energy-saving appliances. The data collection unit can also obtain electricity bill data from smart meters and evaluate the effectiveness of eco-friendly activities. Smart meters measure electricity consumption in real time and transmit this data to the data collection unit. This allows the data collection unit to understand the energy consumption patterns in the home in detail. Furthermore, the data collection unit can also use in-home sensors to monitor energy consumption in real time and collect data. For example, temperature and humidity sensors can be used to monitor air conditioner usage and changes in the indoor environment. This allows the data collection unit to collect data for quantitatively evaluating the specific effects of eco-friendly activities. The collected data is stored on a cloud server and used for subsequent analysis and recommendations. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The proposal department uses a generation AI to propose SDGs activity plans based on data collected by the data collection department. Specifically, the generation AI analyzes the collected data and generates an optimal eco-activity plan for each individual household. For example, it evaluates the energy efficiency of home appliances and proposes upgrading to more energy-efficient appliances. The generation AI analyzes appliance usage patterns and energy consumption data to present the most effective upgrade plan. The proposal department can also propose the introduction of LED lighting to reduce energy consumption. The generation AI creates an optimal LED lighting installation plan based on lighting usage and power consumption data. Furthermore, the proposal department can propose the introduction of solar power generation to promote the use of renewable energy. The generation AI analyzes local sunlight conditions and household energy consumption data to present an optimal solar power generation system installation plan. The proposal department presents these proposals to users in an easy-to-understand manner, explaining specific implementation methods and expected effects. In this way, the proposal department can provide users with concrete guidelines for actually implementing eco-activities and support the promotion of SDGs activities.

[0032] The aggregation department consolidates the results of SDGs activities proposed by the proposal department and converts them into credits. Specifically, it quantifies the results of individual eco-activities and converts them into J-Credits. For example, to evaluate the results of eco-activities, it analyzes collected data and calculates the amount of energy consumption reduction and the amount of renewable energy used. This allows for a quantitative evaluation of the specific results of eco-activities. Based on these results, the aggregation department calculates the value of the credits and certifies them as credits. Furthermore, the aggregation department registers the results of eco-activities in a database and manages the credits. The database records the results of each household's eco-activities and the history of credit issuance, and can be referenced as needed. In this way, the aggregation department can efficiently consolidate the results of eco-activities and convert them into credits, thereby evaluating individual eco-activities and making their results widely recognized.

[0033] The Sales Department sells the credits, which have been converted into credits by the Aggregation Department, to companies. Specifically, it sells converted J-Credits to companies, enabling them to offset their greenhouse gas emissions. The Sales Department sets the selling price of the credits and provides them to companies. For example, it calculates the amount of credits needed based on a company's greenhouse gas emissions and provides them at an appropriate price. The Sales Department can also enter into sales contracts for the credits and provide them to companies. Furthermore, the Sales Department manages and reports on the sales performance of the credits to companies. In this way, the Sales Department can provide companies with concrete means to reduce greenhouse gas emissions and support the promotion of SDGs activities. Through its credit sales activities, the Sales Department can connect the eco-activities of companies and individuals and contribute to the realization of a sustainable society.

[0034] The data collection unit can collect data through methods such as taking photos with a camera inside the home and transmitting electricity bill data. For example, the data collection unit can use a camera inside the home to photograph eco-friendly activities and collect data. For example, the data collection unit can install a camera inside the home and periodically photograph eco-friendly activities. The data collection unit can also obtain electricity bill data from a smart meter and evaluate the effectiveness of eco-friendly activities. For example, the data collection unit can obtain electricity bill data from a smart meter in real time and monitor energy consumption. Furthermore, the data collection unit can use sensors inside the home to monitor energy consumption in real time and collect data. For example, the data collection unit can install sensors inside the home and measure energy consumption in real time. This allows for a more accurate evaluation of eco-friendly activities by collecting detailed data from within the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data taken by a camera inside the home into a generating AI and analyze the eco-friendly activities.

[0035] The proposal department can propose SDGs activity plans such as updating home appliances, introducing LED lighting, installing solar power generation, and providing guidance on energy-saving techniques. For example, the proposal department can evaluate the energy efficiency of home appliances and propose updating them to more energy-efficient appliances. For example, the proposal department can evaluate the energy efficiency of home appliances and propose updating them to more energy-efficient refrigerators and air conditioners. The proposal department can also propose the introduction of LED lighting to reduce energy consumption. For example, the proposal department can propose replacing the lighting in the home with LED lighting. Furthermore, the proposal department can propose the introduction of solar power generation to promote the use of renewable energy. For example, the proposal department can propose installing solar panels on the roof of the home. The proposal department can also provide guidance on energy-saving techniques to reduce energy consumption. For example, the proposal department can propose methods for managing the usage time of electrical appliances in the home. In this way, by proposing concrete SDGs activity plans, it is possible to effectively support individuals' eco-activities. Some or all of the above processing in the proposal department is performed using a generation AI. The generation AI takes data on an individual's eco-activities as input and outputs the optimal SDGs activity plan. For example, the generating AI analyzes an individual's energy consumption data and suggests upgrading to more energy-efficient home appliances.

[0036] The proposal function can suggest methods to reduce greenhouse gas emissions from travel, such as walking, cycling, and using public transportation. For example, the proposal function might recommend walking or cycling to reduce greenhouse gas emissions from travel. For example, it might suggest using walking or cycling for commuting to work or school. The proposal function can also recommend using public transportation to reduce greenhouse gas emissions from travel. For example, it might suggest using public transportation such as buses or trains. Furthermore, the proposal function can recommend using carpooling or ridesharing to reduce greenhouse gas emissions from travel. For example, it might suggest that multiple people traveling to the same destination share a car. This allows the proposal function to suggest concrete methods for reducing greenhouse gas emissions associated with travel. Some or all of the above processing in the proposal function is performed using a generative AI. The generative AI takes individual travel data as input and outputs the optimal method of travel. For example, the generative AI analyzes an individual's commuting data and suggests walking or cycling.

[0037] The aggregation unit can aggregate the results of individuals' eco-activities and convert them into J-Credits. For example, the aggregation unit can quantify the results of individuals' eco-activities and certify them as credits. For example, the aggregation unit can quantify the results of eco-activities as CO2 reduction and certify them as credits. The aggregation unit can also evaluate the results of eco-activities and calculate the value of the credits. For example, the aggregation unit can evaluate the results of eco-activities as a reduction in energy consumption and calculate the value of the credits. Furthermore, the aggregation unit can register the results of eco-activities in a database and manage the credits. For example, the aggregation unit can register the results of eco-activities in a database and manage the credit issuance history. This makes it possible to convert the results of individuals' eco-activities into credits that can be sold to companies. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input individual eco-activity data into a generating AI and have the generating AI execute the credit conversion process.

[0038] The sales department can sell the credited J-Credits to companies. For example, the sales department can sell the credited J-Credits to companies to offset their greenhouse gas emissions. For example, the sales department can set the sales price of the credits and provide it to companies. The sales department can also conclude sales contracts for the credits and provide the credits to companies. Furthermore, the sales department can manage and report on the sales performance of the credits to companies. In this way, by selling credits to companies, the sales department can offset their greenhouse gas emissions. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input credit sales data into a generating AI and have the generating AI execute an optimal sales strategy.

[0039] The sales department can return profits earned to individual participants in the form of points. For example, the sales department can return profits earned from the sale of credits to individual participants in the form of points. For example, the sales department can award the profits from the sale of credits as points to individual accounts. The sales department can also set up a method for redeeming points and notify individual participants. For example, the sales department can inform individuals of the point redemption method via email or app notification. Furthermore, the sales department can provide ways for individual participants to use their points, enabling them to make the most of them. For example, the sales department can enable points to be used for donations to SDGs activities, exchange for products from participating companies, purchase energy-saving home appliances, purchase locally produced food, or exchange for points in electronic payment systems. This can promote further expansion of SDGs activities by returning profits to individuals. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input point redemption data into a generating AI and have the generating AI execute the optimal redemption method.

[0040] The data collection unit can analyze household energy consumption patterns and select the optimal data collection method. For example, the data collection unit can identify peak times for electricity use in the household and concentrate data collection during those times. The data collection unit can also analyze household energy consumption patterns on a weekly basis and collect data on weekends. Furthermore, the data collection unit can analyze household energy consumption patterns seasonally and select a data collection method appropriate for each season. This enables efficient data collection by selecting a data collection method based on energy consumption patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input household energy consumption data into a generating AI and have the generating AI execute the optimal data collection method.

[0041] The data collection unit can monitor the usage status of each device in the home in real time and collect data related to eco-friendly activities. For example, the data collection unit can monitor the power consumption of each device in real time through a smart meter in the home. The data collection unit can measure the power consumption of each device in real time through a smart meter in the home. The data collection unit can also acquire usage data from smart home appliances in the home and collect information related to eco-friendly activities. The data collection unit can also acquire usage data from smart home appliances in the home and collect information related to eco-friendly activities. Furthermore, the data collection unit can use sensors in the home to grasp the operating status of each device in real time and collect data. The data collection unit can use sensors in the home to grasp the operating status of each device in real time and collect data. This allows for the collection of accurate eco-friendly activity data by monitoring the usage status of each device in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input device usage data from the home into a generating AI and have the generating AI collect data related to eco-friendly activities.

[0042] The data collection unit can monitor household water usage and waste generation, and collect data related to eco-friendly activities. For example, the data collection unit can monitor water usage in real time through a household water meter. The data collection unit can measure water usage in real time through a household water meter. The data collection unit can also measure household waste generation using sensors and collect data related to eco-friendly activities. The data collection unit can also analyze household water usage patterns and collect data related to eco-friendly activities. The data collection unit can also analyze household water usage patterns and collect data related to eco-friendly activities. This allows for accurate evaluation of the effectiveness of eco-friendly activities by monitoring water usage and waste generation. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input household water usage data and waste generation data into a generating AI, and have the generating AI collect data related to eco-friendly activities.

[0043] The data collection unit can analyze users' social media activity and collect data related to eco-activities. For example, the data collection unit can extract information about eco-activities from users' social media posts. The data collection unit can also analyze users' social media follower counts and engagement rates to assess the impact of eco-activities. Furthermore, the data collection unit can analyze users' social media hashtags to grasp trends related to eco-activities. This allows for the assessment of the impact of eco-activities by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user social media data into a generating AI and have the generating AI collect data related to eco-activities.

[0044] The proposal unit can propose an optimal SDGs activity plan based on household energy consumption patterns. For example, the proposal unit can propose an energy-saving plan that avoids peak hours of household electricity use. The proposal unit can also propose an optimal appliance replacement plan based on household energy consumption patterns. Furthermore, the proposal unit can analyze household energy consumption patterns and propose an optimal solar power generation installation plan. This enables effective eco-friendly activities by proposing an optimal plan based on energy consumption patterns. Some or all of the above processing in the proposal unit is performed using a generation AI. The generation AI takes household energy consumption data as input and outputs an optimal SDGs activity plan. For example, the generation AI analyzes household energy consumption data and proposes an optimal energy-saving plan.

[0045] The suggestion unit can provide eco-friendly activity plans customized to the user's lifestyle. For example, if the user is busy, the suggestion unit can suggest eco-friendly activity plans that can be completed in a short amount of time. The suggestion unit can also suggest eco-friendly activity plans that can be completed at home if the user spends a lot of time at home. Furthermore, if the user prefers outdoor activities, the suggestion unit can suggest eco-friendly activity plans that take the natural environment into consideration. This makes it possible to implement eco-friendly activities that are easy to follow by providing plans tailored to the user's lifestyle. Some or all of the above processing in the suggestion unit is performed using a generation AI. The generation AI takes the user's lifestyle data as input and outputs a customized eco-friendly activity plan. For example, the generation AI analyzes the user's lifestyle data and proposes the optimal eco-friendly activity plan.

[0046] The suggestion unit can propose an optimal eco-activity plan considering the user's geographical location. For example, if the user lives in an urban area, the suggestion unit can propose an eco-activity plan that recommends the use of public transportation. The suggestion unit can also propose an eco-activity plan that recommends cycling or walking if the user lives in a suburban area. Furthermore, if the user lives in a rural area, the suggestion unit can propose an eco-activity plan that recommends local production and consumption. In this way, by considering geographical location information, it is possible to provide an eco-activity plan that is appropriate for the region. Some or all of the above processing in the suggestion unit is performed using a generation AI. The generation AI takes the user's geographical location information as input and outputs an optimal eco-activity plan. For example, the generation AI analyzes the user's geographical location information and proposes an optimal eco-activity plan.

[0047] The suggestion unit can provide the optimal plan by referring to the user's past eco-activity history. For example, the suggestion unit proposes a plan that can be implemented as the next step based on the results of eco-activities the user has performed in the past. The suggestion unit can also propose activities that were effective based on the user's past eco-activity history, prioritizing those that were effective. Furthermore, the suggestion unit can analyze the user's past eco-activity history and propose a plan that includes areas for improvement. This allows the suggestion unit to provide an effective eco-activity plan by referring to past history. Some or all of the above processing in the suggestion unit is performed using a generation AI. The generation AI takes the user's past eco-activity history data as input and outputs the optimal plan. For example, the generation AI analyzes the user's past eco-activity history data and proposes the optimal eco-activity plan.

[0048] The aggregation unit can, at the time of aggregation, analyze in detail the results of each household's eco-activities and select the optimal credit conversion method. For example, the aggregation unit can analyze the results of each household's eco-activities on a monthly basis and select the optimal credit conversion method. The aggregation unit can, for example, analyze the results of each household's eco-activities on a monthly basis and select the optimal credit conversion method. Furthermore, the aggregation unit can analyze the results of each household's eco-activities by energy consumption and select the optimal credit conversion method. The aggregation unit can, for example, analyze the results of each household's eco-activities by energy consumption and select the optimal credit conversion method. Furthermore, the aggregation unit can analyze the results of each household's eco-activities by region and select the optimal credit conversion method. The aggregation unit can, for example, analyze the results of each household's eco-activities by region and select the optimal credit conversion method. In this way, by analyzing the results of each household's eco-activities in detail, the optimal credit conversion method can be selected. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input eco-activity data from each household into a generating AI and have the AI ​​execute the optimal credit conversion method.

[0049] The aggregation unit can apply different crediting algorithms to each type of eco-activity during aggregation. For example, the aggregation unit can apply a dedicated crediting algorithm to household energy consumption reduction activities. The aggregation unit can also apply a dedicated crediting algorithm to household water consumption reduction activities. Furthermore, the aggregation unit can also apply a dedicated crediting algorithm to household waste reduction activities. This improves the accuracy of crediting by applying the most suitable crediting algorithm for each type of eco-activity. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input eco-activity data into a generating AI and have the generating AI execute the most suitable crediting algorithm.

[0050] The aggregation unit can perform credit generation while considering the results of eco-activities in each region. For example, the aggregation unit can analyze the results of eco-activities in each region and select the optimal credit generation method. The aggregation unit can also compare the results of eco-activities in each region and determine the priority for credit generation. Furthermore, the aggregation unit can evaluate the results of eco-activities in each region and improve the accuracy of credit generation. This makes it possible to create credits that are appropriate for each region by considering the results of eco-activities in each region. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input regional eco-activity data into a generating AI and have the generating AI execute the optimal credit generation method.

[0051] The aggregation unit can improve the accuracy of crediting by referring to relevant literature on eco-activities during aggregation. For example, the aggregation unit can refer to relevant literature on eco-activities and apply the latest crediting algorithm. The aggregation unit can also refer to relevant literature on eco-activities and apply the latest crediting algorithm. Furthermore, the aggregation unit can analyze relevant literature on eco-activities and revise the crediting criteria. The aggregation unit can also analyze relevant literature on eco-activities and revise the crediting criteria. In addition, the aggregation unit can introduce new methods to improve the accuracy of crediting based on relevant literature on eco-activities. The aggregation unit can introduce new methods to improve the accuracy of crediting based on relevant literature on eco-activities. This allows for improved accuracy of crediting by referring to relevant literature. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input eco-activity related literature data into a generating AI and have the generating AI perform the crediting accuracy improvement.

[0052] The sales department can select the optimal credit sales strategy based on the company's demand at the time of sale. The sales department can determine the optimal sales timing based on the company's demand forecast data, for example. The sales department can also adjust the price of credits according to the company's demand, for example. Furthermore, the sales department can conduct sales campaigns based on the company's demand, for example. This enables efficient credit sales by selecting the optimal sales strategy based on the company's demand. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input company demand data into a generating AI and have the generating AI execute the optimal credit sales strategy.

[0053] The sales department can analyze market trends for credits at the time of sale to determine the optimal sales timing. For example, the sales department can monitor the market price of credits in real time to determine the optimal sales timing. The sales department can also analyze market trends for credits and sell during periods of high demand. Furthermore, the sales department can predict market trends for credits and formulate optimal sales strategies. This allows the sales department to determine the optimal sales timing by analyzing market trends. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input market data for credits into a generating AI and have the generating AI execute the optimal sales timing.

[0054] The sales department can propose an optimal credit sales strategy at the time of sale, taking into account the company's geographical location. For example, the sales department can analyze regional demand based on the company's location and propose an optimal sales strategy. The sales department can also propose a sales strategy that minimizes logistics costs, taking into account the company's geographical location. Furthermore, the sales department can analyze regional market trends based on the company's geographical location and propose an optimal sales strategy. This allows the sales department to propose a sales strategy appropriate for each region by considering geographical location. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input the company's geographical location data into a generating AI and have the generating AI execute an optimal credit sales strategy.

[0055] The sales department can provide optimal sales strategies by referring to a company's past credit purchase history at the time of sale. For example, the sales department can analyze a company's past credit purchase history and propose the optimal timing for sales. The sales department can also forecast demand based on a company's past credit purchase history and formulate an optimal sales strategy. The sales department can also refer to a company's past credit purchase history and provide customized sales strategies for specific companies. This allows the sales department to provide optimal sales strategies by referring to past purchase history. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input a company's past credit purchase history data into a generating AI and have the generating AI execute an optimal sales strategy.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The SDGs support service system can also include an "Education Department." This department provides educational content on SDGs to individuals and businesses. For example, it can educate people about the importance of SDGs and specific ways to implement them through online courses and webinars. It can also provide customized SDGs training programs for businesses to raise employee awareness. Furthermore, the Education Department can collaborate with schools and local communities to hold workshops and events on SDGs for children and residents. This helps spread knowledge and awareness about SDGs and promotes eco-friendly activities by individuals and businesses.

[0058] The data collection unit can also be equipped with a "health data collection function." This unit collects individual health data and evaluates the effects of eco-friendly activities from a health perspective. For example, the unit can collect individual step counts, heart rate, and sleep data through smartwatches and fitness trackers. It can also collect data on individual diet and exercise habits to evaluate the impact of eco-friendly activities on health. Furthermore, it can collect data on individual stress levels and mental health to evaluate the impact of eco-friendly activities on mental health. This allows for a multifaceted evaluation of the effects of eco-friendly activities, enabling a balance between individual health and eco-friendly practices.

[0059] The proposal department can also be equipped with a "community collaboration function." The proposal department can propose ways to connect individual eco-activities with local communities. For example, the proposal department can suggest participation in local eco-events and workshops. It can also suggest participation in local eco-activity groups and volunteer activities, thereby supporting individual eco-activities at a community level. Furthermore, the proposal department can provide information on local eco-activities, encouraging individuals to actively participate in them. This allows individual eco-activities to connect with community-wide eco-activities, generating a greater impact.

[0060] The suggestion section can also incorporate "gamification features." This section can gamify eco-friendly activities to increase individual motivation. For example, it can award points or badges based on the level of eco-friendly activity achieved. It can also display rankings for eco-friendly activities, encouraging competition among users. Furthermore, it can offer rewards and incentives for achieving eco-friendly activities, increasing individual motivation to participate. This makes eco-friendly activities more enjoyable and sustainable.

[0061] The aggregation unit can further enhance the transparency and reliability of credit creation by utilizing blockchain technology. The aggregation unit records the results of eco-activities on the blockchain and manages them in a tamper-proof manner. For example, the aggregation unit records eco-activity data on the blockchain, enabling verification by third parties. Furthermore, the aggregation unit can ensure transparency by recording the credit issuance history on the blockchain. In addition, the aggregation unit can improve reliability by recording the credit transaction history on the blockchain. This makes the credit creation process transparent and reliable, gaining the trust of companies and individuals.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data on individuals' eco-friendly activities. For example, data is collected through methods such as taking photos with a camera in the home, transmitting electricity bill data, and real-time monitoring of energy consumption using sensors in the home. Step 2: The proposal department uses a generation AI to propose SDGs activity plans based on the data collected by the collection department. For example, it proposes SDGs activity plans such as updating home appliances, introducing LED lighting, introducing solar power generation, and providing guidance on energy-saving techniques. Step 3: The aggregation department aggregates the results of the SDGs activities proposed by the proposal department and converts them into credits. For example, the results of individual eco-activities are aggregated and converted into J-Credits. Step 4: The sales department sells the credits, which have been converted into credits by the aggregation department, to companies. For example, they sell the converted J-credits to companies to offset their greenhouse gas emissions.

[0064] (Example of form 2) The SDGs support service system according to an embodiment of the present invention is a system that connects companies that utilize the "carbon offset" system, which involves investing in greenhouse gas reduction activities commensurate with emissions to cover the portion of greenhouse gas emissions that could not be fully suppressed through economic activities, with individuals who engage in eco-friendly activities on a daily basis. The SDGs support service system uses a generating AI to support individuals who are currently engaged in or wish to engage in SDGs activities. The generating AI collects data on individuals' eco-friendly activities and proposes SDGs activity plans tailored to each household's situation. Examples include updating home appliances, introducing LED lighting, installing solar power generation, and providing guidance on energy-saving techniques. It also proposes methods for reducing greenhouse gas emissions from movement, such as walking, cycling, and using public transportation. Next, companies aggregate the results of individuals' eco-friendly activities supported by the generating AI and sell them to other companies as "J-Credits." J-Credits are a system that makes it possible to buy and sell credits representing reductions and absorptions of greenhouse gas emissions such as CO2. Companies can offset their greenhouse gas emissions by purchasing these credits. Furthermore, the profits earned are returned to individual participants in the form of points. These points can be used for various purposes, including donating to SDGs activities, exchanging them for products from participating companies, purchasing energy-saving home appliances, buying locally produced food, and exchanging them for points in electronic payment systems. This system promotes individual eco-activities, helps companies reduce greenhouse gas emissions, and aims to realize a carbon-neutral society. In this way, the SDGs support service system can support individual eco-activities and promote greenhouse gas emission reductions by converting the results into credits that can be sold to companies.

[0065] The SDGs support service system according to this embodiment comprises a data collection unit, a proposal unit, an aggregation unit, and a sales unit. The data collection unit collects data on an individual's eco-activities. The data collection unit can collect data, for example, by taking pictures with a camera inside the home or by transmitting electricity bill data. For example, the data collection unit can use a camera inside the home to photograph eco-activities and collect data. The data collection unit can also obtain electricity bill data from a smart meter and evaluate the effectiveness of eco-activities. Furthermore, the data collection unit can use sensors inside the home to monitor energy consumption in real time and collect data. The proposal unit uses a generation AI to propose an SDGs activity plan based on the data collected by the data collection unit. For example, the proposal unit can propose an SDGs activity plan such as updating home appliances, introducing LED lighting, introducing solar power generation, or providing guidance on energy-saving techniques. For example, the proposal unit can evaluate the energy efficiency of home appliances and propose updating them to more energy-efficient appliances. The proposal unit can also propose the introduction of LED lighting to reduce energy consumption. Furthermore, the proposal unit can propose the introduction of solar power generation to promote the use of renewable energy. The Aggregation Department aggregates the results of SDGs activities proposed by the Proposal Department and converts them into credits. For example, the Aggregation Department can aggregate the results of individual eco-activities and convert them into J-Credits. For example, the Aggregation Department can quantify the results of eco-activities and certify them as credits. The Aggregation Department can also evaluate the results of eco-activities and calculate the value of the credits. Furthermore, the Aggregation Department can register the results of eco-activities in a database and manage the credits. The Sales Department sells the credits converted into credits by the Aggregation Department to companies. For example, the Sales Department can sell the converted J-Credits to companies to offset their greenhouse gas emissions. For example, the Sales Department can set the sales price of the credits and provide it to companies. The Sales Department can also conclude sales contracts for the credits and provide them to companies. Furthermore, the Sales Department can manage the sales performance of the credits and report it to companies.As a result, the SDGs support service system according to this embodiment can support individuals' eco-activities and promote the reduction of greenhouse gas emissions by converting the results into credits and selling them to companies.

[0066] The data collection unit collects data on individuals' eco-friendly activities. Specifically, it uses in-home cameras to film eco-friendly activities and collects data. For example, in-home cameras can record recycling practices and the use of energy-saving appliances. The data collection unit can also obtain electricity bill data from smart meters and evaluate the effectiveness of eco-friendly activities. Smart meters measure electricity consumption in real time and transmit this data to the data collection unit. This allows the data collection unit to understand the energy consumption patterns in the home in detail. Furthermore, the data collection unit can also use in-home sensors to monitor energy consumption in real time and collect data. For example, temperature and humidity sensors can be used to monitor air conditioner usage and changes in the indoor environment. This allows the data collection unit to collect data for quantitatively evaluating the specific effects of eco-friendly activities. The collected data is stored on a cloud server and used for subsequent analysis and recommendations. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The proposal department uses a generation AI to propose SDGs activity plans based on data collected by the data collection department. Specifically, the generation AI analyzes the collected data and generates an optimal eco-activity plan for each individual household. For example, it evaluates the energy efficiency of home appliances and proposes upgrading to more energy-efficient appliances. The generation AI analyzes appliance usage patterns and energy consumption data to present the most effective upgrade plan. The proposal department can also propose the introduction of LED lighting to reduce energy consumption. The generation AI creates an optimal LED lighting installation plan based on lighting usage and power consumption data. Furthermore, the proposal department can propose the introduction of solar power generation to promote the use of renewable energy. The generation AI analyzes local sunlight conditions and household energy consumption data to present an optimal solar power generation system installation plan. The proposal department presents these proposals to users in an easy-to-understand manner, explaining specific implementation methods and expected effects. In this way, the proposal department can provide users with concrete guidelines for actually implementing eco-activities and support the promotion of SDGs activities.

[0068] The aggregation department consolidates the results of SDGs activities proposed by the proposal department and converts them into credits. Specifically, it quantifies the results of individual eco-activities and converts them into J-Credits. For example, to evaluate the results of eco-activities, it analyzes collected data and calculates the amount of energy consumption reduction and the amount of renewable energy used. This allows for a quantitative evaluation of the specific results of eco-activities. Based on these results, the aggregation department calculates the value of the credits and certifies them as credits. Furthermore, the aggregation department registers the results of eco-activities in a database and manages the credits. The database records the results of each household's eco-activities and the history of credit issuance, and can be referenced as needed. In this way, the aggregation department can efficiently consolidate the results of eco-activities and convert them into credits, thereby evaluating individual eco-activities and making their results widely recognized.

[0069] The Sales Department sells the credits, which have been converted into credits by the Aggregation Department, to companies. Specifically, it sells converted J-Credits to companies, enabling them to offset their greenhouse gas emissions. The Sales Department sets the selling price of the credits and provides them to companies. For example, it calculates the amount of credits needed based on a company's greenhouse gas emissions and provides them at an appropriate price. The Sales Department can also enter into sales contracts for the credits and provide them to companies. Furthermore, the Sales Department manages and reports on the sales performance of the credits to companies. In this way, the Sales Department can provide companies with concrete means to reduce greenhouse gas emissions and support the promotion of SDGs activities. Through its credit sales activities, the Sales Department can connect the eco-activities of companies and individuals and contribute to the realization of a sustainable society.

[0070] The data collection unit can collect data through methods such as taking photos with a camera inside the home and transmitting electricity bill data. For example, the data collection unit can use a camera inside the home to photograph eco-friendly activities and collect data. For example, the data collection unit can install a camera inside the home and periodically photograph eco-friendly activities. The data collection unit can also obtain electricity bill data from a smart meter and evaluate the effectiveness of eco-friendly activities. For example, the data collection unit can obtain electricity bill data from a smart meter in real time and monitor energy consumption. Furthermore, the data collection unit can use sensors inside the home to monitor energy consumption in real time and collect data. For example, the data collection unit can install sensors inside the home and measure energy consumption in real time. This allows for a more accurate evaluation of eco-friendly activities by collecting detailed data from within the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data taken by a camera inside the home into a generating AI and analyze the eco-friendly activities.

[0071] The proposal department can propose SDGs activity plans such as updating home appliances, introducing LED lighting, installing solar power generation, and providing guidance on energy-saving techniques. For example, the proposal department can evaluate the energy efficiency of home appliances and propose updating them to more energy-efficient appliances. For example, the proposal department can evaluate the energy efficiency of home appliances and propose updating them to more energy-efficient refrigerators and air conditioners. The proposal department can also propose the introduction of LED lighting to reduce energy consumption. For example, the proposal department can propose replacing the lighting in the home with LED lighting. Furthermore, the proposal department can propose the introduction of solar power generation to promote the use of renewable energy. For example, the proposal department can propose installing solar panels on the roof of the home. The proposal department can also provide guidance on energy-saving techniques to reduce energy consumption. For example, the proposal department can propose methods for managing the usage time of electrical appliances in the home. In this way, by proposing concrete SDGs activity plans, it is possible to effectively support individuals' eco-activities. Some or all of the above processing in the proposal department is performed using a generation AI. The generation AI takes data on an individual's eco-activities as input and outputs the optimal SDGs activity plan. For example, the generating AI analyzes an individual's energy consumption data and suggests upgrading to more energy-efficient home appliances.

[0072] The proposal function can suggest methods to reduce greenhouse gas emissions from travel, such as walking, cycling, and using public transportation. For example, the proposal function might recommend walking or cycling to reduce greenhouse gas emissions from travel. For example, it might suggest using walking or cycling for commuting to work or school. The proposal function can also recommend using public transportation to reduce greenhouse gas emissions from travel. For example, it might suggest using public transportation such as buses or trains. Furthermore, the proposal function can recommend using carpooling or ridesharing to reduce greenhouse gas emissions from travel. For example, it might suggest that multiple people traveling to the same destination share a car. This allows the proposal function to suggest concrete methods for reducing greenhouse gas emissions associated with travel. Some or all of the above processing in the proposal function is performed using a generative AI. The generative AI takes individual travel data as input and outputs the optimal method of travel. For example, the generative AI analyzes an individual's commuting data and suggests walking or cycling.

[0073] The aggregation unit can aggregate the results of individuals' eco-activities and convert them into J-Credits. For example, the aggregation unit can quantify the results of individuals' eco-activities and certify them as credits. For example, the aggregation unit can quantify the results of eco-activities as CO2 reduction and certify them as credits. The aggregation unit can also evaluate the results of eco-activities and calculate the value of the credits. For example, the aggregation unit can evaluate the results of eco-activities as a reduction in energy consumption and calculate the value of the credits. Furthermore, the aggregation unit can register the results of eco-activities in a database and manage the credits. For example, the aggregation unit can register the results of eco-activities in a database and manage the credit issuance history. This makes it possible to convert the results of individuals' eco-activities into credits that can be sold to companies. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input individual eco-activity data into a generating AI and have the generating AI execute the credit conversion process.

[0074] The sales department can sell the credited J-Credits to companies. For example, the sales department can sell the credited J-Credits to companies to offset their greenhouse gas emissions. For example, the sales department can set the sales price of the credits and provide it to companies. The sales department can also conclude sales contracts for the credits and provide the credits to companies. Furthermore, the sales department can manage and report on the sales performance of the credits to companies. In this way, by selling credits to companies, the sales department can offset their greenhouse gas emissions. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input credit sales data into a generating AI and have the generating AI execute an optimal sales strategy.

[0075] The sales department can return profits earned to individual participants in the form of points. For example, the sales department can return profits earned from the sale of credits to individual participants in the form of points. For example, the sales department can award the profits from the sale of credits as points to individual accounts. The sales department can also set up a method for redeeming points and notify individual participants. For example, the sales department can inform individuals of the point redemption method via email or app notification. Furthermore, the sales department can provide ways for individual participants to use their points, enabling them to make the most of them. For example, the sales department can enable points to be used for donations to SDGs activities, exchange for products from participating companies, purchase energy-saving home appliances, purchase locally produced food, or exchange for points in electronic payment systems. This can promote further expansion of SDGs activities by returning profits to individuals. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input point redemption data into a generating AI and have the generating AI execute the optimal redemption method.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect data frequently to obtain detailed eco-activity data. For example, the data collection unit will collect data during times when the user is relaxed. The data collection unit can also reduce the frequency of data collection if the user is stressed, thereby reducing the burden on the user. For example, the data collection unit will refrain from collecting data during times when the user is stressed. Furthermore, if the user is busy, the data collection unit can take into consideration the user's lifestyle by collecting data at night or on weekends. For example, the data collection unit will collect data while avoiding times when the user is busy. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI execute the data collection process at the appropriate time.

[0077] The data collection unit can analyze household energy consumption patterns and select the optimal data collection method. For example, the data collection unit can identify peak times for electricity use in the household and concentrate data collection during those times. The data collection unit can also analyze household energy consumption patterns on a weekly basis and collect data on weekends. Furthermore, the data collection unit can analyze household energy consumption patterns seasonally and select a data collection method appropriate for each season. This enables efficient data collection by selecting a data collection method based on energy consumption patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input household energy consumption data into a generating AI and have the generating AI execute the optimal data collection method.

[0078] The data collection unit can monitor the usage status of each device in the home in real time and collect data related to eco-friendly activities. For example, the data collection unit can monitor the power consumption of each device in real time through a smart meter in the home. The data collection unit can measure the power consumption of each device in real time through a smart meter in the home. The data collection unit can also acquire usage data from smart home appliances in the home and collect information related to eco-friendly activities. The data collection unit can also acquire usage data from smart home appliances in the home and collect information related to eco-friendly activities. Furthermore, the data collection unit can use sensors in the home to grasp the operating status of each device in real time and collect data. The data collection unit can use sensors in the home to grasp the operating status of each device in real time and collect data. This allows for the collection of accurate eco-friendly activity data by monitoring the usage status of each device in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input device usage data from the home into a generating AI and have the generating AI collect data related to eco-friendly activities.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed eco-activity data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed eco-activity data during times when the user is relaxed. The data collection unit can also collect only basic eco-activity data if the user is stressed. For example, if the user is stressed, the data collection unit will prioritize collecting only basic eco-activity data during times when the user is stressed. Furthermore, if the user is busy, the data collection unit can also prioritize collecting only important data. For example, if the user is busy, the data collection unit will prioritize collecting only important data during times when the user is busy. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI prioritize the data to be collected.

[0080] The data collection unit can monitor household water usage and waste generation, and collect data related to eco-friendly activities. For example, the data collection unit can monitor water usage in real time through a household water meter. The data collection unit can measure water usage in real time through a household water meter. The data collection unit can also measure household waste generation using sensors and collect data related to eco-friendly activities. The data collection unit can also analyze household water usage patterns and collect data related to eco-friendly activities. The data collection unit can also analyze household water usage patterns and collect data related to eco-friendly activities. This allows for accurate evaluation of the effectiveness of eco-friendly activities by monitoring water usage and waste generation. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input household water usage data and waste generation data into a generating AI, and have the generating AI collect data related to eco-friendly activities.

[0081] The data collection unit can analyze users' social media activity and collect data related to eco-activities. For example, the data collection unit can extract information about eco-activities from users' social media posts. The data collection unit can also analyze users' social media follower counts and engagement rates to assess the impact of eco-activities. Furthermore, the data collection unit can analyze users' social media hashtags to grasp trends related to eco-activities. This allows for the assessment of the impact of eco-activities by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user social media data into a generating AI and have the generating AI collect data related to eco-activities.

[0082] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions with detailed explanations. The suggestion unit can also provide suggestions with detailed explanations during times when the user is relaxed. Furthermore, if the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. The suggestion unit can also provide suggestions that can be acted upon quickly if the user is in a hurry. The suggestion unit can also provide suggestions that can be acted upon quickly during times when the user is in a hurry. By adjusting the way suggestions are presented according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit is performed using generative AI. The generative AI takes user emotion data as input and outputs the way suggestions are presented. For example, generative AI analyzes user emotion data and suggests the most appropriate way to express suggestions.

[0083] The proposal unit can propose an optimal SDGs activity plan based on household energy consumption patterns. For example, the proposal unit can propose an energy-saving plan that avoids peak hours of household electricity use. The proposal unit can also propose an optimal appliance replacement plan based on household energy consumption patterns. Furthermore, the proposal unit can analyze household energy consumption patterns and propose an optimal solar power generation installation plan. This enables effective eco-friendly activities by proposing an optimal plan based on energy consumption patterns. Some or all of the above processing in the proposal unit is performed using a generation AI. The generation AI takes household energy consumption data as input and outputs an optimal SDGs activity plan. For example, the generation AI analyzes household energy consumption data and proposes an optimal energy-saving plan.

[0084] The suggestion unit can provide eco-friendly activity plans customized to the user's lifestyle. For example, if the user is busy, the suggestion unit can suggest eco-friendly activity plans that can be completed in a short amount of time. The suggestion unit can also suggest eco-friendly activity plans that can be completed at home if the user spends a lot of time at home. Furthermore, if the user prefers outdoor activities, the suggestion unit can suggest eco-friendly activity plans that take the natural environment into consideration. This makes it possible to implement eco-friendly activities that are easy to follow by providing plans tailored to the user's lifestyle. Some or all of the above processing in the suggestion unit is performed using a generation AI. The generation AI takes the user's lifestyle data as input and outputs a customized eco-friendly activity plan. For example, the generation AI analyzes the user's lifestyle data and proposes the optimal eco-friendly activity plan.

[0085] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will prioritize suggesting a detailed eco-activity plan. For example, if the user is relaxed, the suggestion unit will prioritize suggesting a detailed eco-activity plan. For example, if the user is stressed, the suggestion unit will prioritize suggesting a simple and easy-to-implement eco-activity plan. For example, if the user is stressed, the suggestion unit will prioritize suggesting a simple and easy-to-implement eco-activity plan. Furthermore, if the user is in a hurry, the suggestion unit will prioritize suggesting an eco-activity plan that can be implemented quickly. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting an eco-activity plan that can be implemented quickly. This allows for efficient suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit is performed using generative AI. The generative AI takes user sentiment data as input and outputs a priority list of suggestions. For example, the generative AI analyzes user sentiment data to determine the optimal priority list of suggestions.

[0086] The suggestion unit can propose an optimal eco-activity plan considering the user's geographical location. For example, if the user lives in an urban area, the suggestion unit can propose an eco-activity plan that recommends the use of public transportation. The suggestion unit can also propose an eco-activity plan that recommends cycling or walking if the user lives in a suburban area. Furthermore, if the user lives in a rural area, the suggestion unit can propose an eco-activity plan that recommends local production and consumption. In this way, by considering geographical location information, it is possible to provide an eco-activity plan that is appropriate for the region. Some or all of the above processing in the suggestion unit is performed using a generation AI. The generation AI takes the user's geographical location information as input and outputs an optimal eco-activity plan. For example, the generation AI analyzes the user's geographical location information and proposes an optimal eco-activity plan.

[0087] The suggestion unit can provide the optimal plan by referring to the user's past eco-activity history. For example, the suggestion unit proposes a plan that can be implemented as the next step based on the results of eco-activities the user has performed in the past. The suggestion unit can also propose activities that were effective based on the user's past eco-activity history, prioritizing those that were effective. Furthermore, the suggestion unit can analyze the user's past eco-activity history and propose a plan that includes areas for improvement. This allows the suggestion unit to provide an effective eco-activity plan by referring to past history. Some or all of the above processing in the suggestion unit is performed using a generation AI. The generation AI takes the user's past eco-activity history data as input and outputs the optimal plan. For example, the generation AI analyzes the user's past eco-activity history data and proposes the optimal eco-activity plan.

[0088] The aggregation unit can estimate the user's emotions and adjust the aggregation method based on the estimated emotions. For example, if the user is relaxed, the aggregation unit aggregates detailed data and performs highly accurate crediting. For example, if the user is relaxed, the aggregation unit aggregates detailed data and performs highly accurate crediting. Furthermore, if the user is stressed, the aggregation unit can aggregate only basic data and perform rapid crediting. For example, if the user is stressed, the aggregation unit aggregates only basic data and performs rapid crediting. Furthermore, if the user is in a hurry, the aggregation unit can prioritize aggregating only important data and perform rapid crediting. For example, if the user is in a hurry, the aggregation unit prioritizes aggregating only important data and performs rapid crediting. This allows for efficient crediting by adjusting the aggregation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input user emotion data into a generating AI and have the generating AI execute the aggregation method.

[0089] The aggregation unit can, at the time of aggregation, analyze in detail the results of each household's eco-activities and select the optimal credit conversion method. For example, the aggregation unit can analyze the results of each household's eco-activities on a monthly basis and select the optimal credit conversion method. The aggregation unit can, for example, analyze the results of each household's eco-activities on a monthly basis and select the optimal credit conversion method. Furthermore, the aggregation unit can analyze the results of each household's eco-activities by energy consumption and select the optimal credit conversion method. The aggregation unit can, for example, analyze the results of each household's eco-activities by energy consumption and select the optimal credit conversion method. Furthermore, the aggregation unit can analyze the results of each household's eco-activities by region and select the optimal credit conversion method. The aggregation unit can, for example, analyze the results of each household's eco-activities by region and select the optimal credit conversion method. In this way, by analyzing the results of each household's eco-activities in detail, the optimal credit conversion method can be selected. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input eco-activity data from each household into a generating AI and have the AI ​​execute the optimal credit conversion method.

[0090] The aggregation unit can apply different crediting algorithms to each type of eco-activity during aggregation. For example, the aggregation unit can apply a dedicated crediting algorithm to household energy consumption reduction activities. The aggregation unit can also apply a dedicated crediting algorithm to household water consumption reduction activities. Furthermore, the aggregation unit can also apply a dedicated crediting algorithm to household waste reduction activities. This improves the accuracy of crediting by applying the most suitable crediting algorithm for each type of eco-activity. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input eco-activity data into a generating AI and have the generating AI execute the most suitable crediting algorithm.

[0091] The aggregation unit can estimate the user's emotions and determine aggregation priorities based on the estimated emotions. For example, if the user is relaxed, the aggregation unit will prioritize aggregating detailed data. For example, if the user is relaxed, the aggregation unit will prioritize aggregating detailed data during times when the user is relaxed. The aggregation unit can also prioritize aggregating only basic data if the user is stressed. For example, if the user is stressed, the aggregation unit will prioritize aggregating only important data during times when the user is in a hurry. In addition, if the user is in a hurry, the aggregation unit will prioritize aggregating only important data. For example, if the aggregation unit prioritizes aggregating only important data during times when the user is in a hurry. This enables efficient data aggregation by determining aggregation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input user emotion data into a generating AI and have the generating AI prioritize the aggregation process.

[0092] The aggregation unit can perform credit generation while considering the results of eco-activities in each region. For example, the aggregation unit can analyze the results of eco-activities in each region and select the optimal credit generation method. The aggregation unit can also compare the results of eco-activities in each region and determine the priority for credit generation. Furthermore, the aggregation unit can evaluate the results of eco-activities in each region and improve the accuracy of credit generation. This makes it possible to create credits that are appropriate for each region by considering the results of eco-activities in each region. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input regional eco-activity data into a generating AI and have the generating AI execute the optimal credit generation method.

[0093] The aggregation unit can improve the accuracy of crediting by referring to relevant literature on eco-activities during aggregation. For example, the aggregation unit can refer to relevant literature on eco-activities and apply the latest crediting algorithm. The aggregation unit can also refer to relevant literature on eco-activities and apply the latest crediting algorithm. Furthermore, the aggregation unit can analyze relevant literature on eco-activities and revise the crediting criteria. The aggregation unit can also analyze relevant literature on eco-activities and revise the crediting criteria. In addition, the aggregation unit can introduce new methods to improve the accuracy of crediting based on relevant literature on eco-activities. The aggregation unit can introduce new methods to improve the accuracy of crediting based on relevant literature on eco-activities. This allows for improved accuracy of crediting by referring to relevant literature. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input eco-activity related literature data into a generating AI and have the generating AI perform the crediting accuracy improvement.

[0094] The sales department can estimate the user's emotions and adjust its sales approach based on those emotions. For example, if the user is relaxed, the sales department can provide a sales approach that includes detailed explanations. The sales department can also provide a sales approach that includes detailed explanations during times when the user is relaxed. Furthermore, if the user is stressed, the sales department can provide a sales approach that allows for quick purchases. The sales department can also provide a sales approach that allows for quick purchases during times when the user is in a hurry. By adjusting the sales approach according to the user's emotions, effective sales become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sales department may be performed using AI or not. For example, the sales department can input user emotion data into a generating AI and have the AI ​​execute sales strategies.

[0095] The sales department can select the optimal credit sales strategy based on the company's demand at the time of sale. The sales department can determine the optimal sales timing based on the company's demand forecast data, for example. The sales department can also adjust the price of credits according to the company's demand, for example. Furthermore, the sales department can conduct sales campaigns based on the company's demand, for example. This enables efficient credit sales by selecting the optimal sales strategy based on the company's demand. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input company demand data into a generating AI and have the generating AI execute the optimal credit sales strategy.

[0096] The sales department can analyze market trends for credits at the time of sale to determine the optimal sales timing. For example, the sales department can monitor the market price of credits in real time to determine the optimal sales timing. The sales department can also analyze market trends for credits and sell during periods of high demand. Furthermore, the sales department can predict market trends for credits and formulate optimal sales strategies. This allows the sales department to determine the optimal sales timing by analyzing market trends. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input market data for credits into a generating AI and have the generating AI execute the optimal sales timing.

[0097] The sales department can estimate the user's emotions and prioritize sales based on those emotions. For example, if the user is relaxed, the sales department can prioritize sales methods that include detailed explanations. The sales department can also prioritize sales methods that include detailed explanations during times when the user is relaxed. Furthermore, if the user is stressed, the sales department can prioritize sales methods that allow for quick purchases. The sales department can also prioritize sales methods that allow for quick purchases during times when the user is in a hurry. This enables efficient sales by prioritizing sales according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the sales department may be performed using AI or not. For example, the sales department can input user sentiment data into a generating AI and have the generating AI prioritize sales.

[0098] The sales department can propose an optimal credit sales strategy at the time of sale, taking into account the company's geographical location. For example, the sales department can analyze regional demand based on the company's location and propose an optimal sales strategy. The sales department can also propose a sales strategy that minimizes logistics costs, taking into account the company's geographical location. Furthermore, the sales department can analyze regional market trends based on the company's geographical location and propose an optimal sales strategy. This allows the sales department to propose a sales strategy appropriate for each region by considering geographical location. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input the company's geographical location data into a generating AI and have the generating AI execute an optimal credit sales strategy.

[0099] The sales department can provide optimal sales strategies by referring to a company's past credit purchase history at the time of sale. For example, the sales department can analyze a company's past credit purchase history and propose the optimal timing for sales. The sales department can also forecast demand based on a company's past credit purchase history and formulate an optimal sales strategy. The sales department can also refer to a company's past credit purchase history and provide customized sales strategies for specific companies. This allows the sales department to provide optimal sales strategies by referring to past purchase history. Some or all of the above processes in the sales department may be performed using AI or not. For example, the sales department can input a company's past credit purchase history data into a generating AI and have the generating AI execute an optimal sales strategy.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The SDGs support service system can also include an "Education Department." This department provides educational content on SDGs to individuals and businesses. For example, it can educate people about the importance of SDGs and specific ways to implement them through online courses and webinars. It can also provide customized SDGs training programs for businesses to raise employee awareness. Furthermore, the Education Department can collaborate with schools and local communities to hold workshops and events on SDGs for children and residents. This helps spread knowledge and awareness about SDGs and promotes eco-friendly activities by individuals and businesses.

[0102] The data collection unit can also be equipped with a "health data collection function." This unit collects individual health data and evaluates the effects of eco-friendly activities from a health perspective. For example, the unit can collect individual step counts, heart rate, and sleep data through smartwatches and fitness trackers. It can also collect data on individual diet and exercise habits to evaluate the impact of eco-friendly activities on health. Furthermore, it can collect data on individual stress levels and mental health to evaluate the impact of eco-friendly activities on mental health. This allows for a multifaceted evaluation of the effects of eco-friendly activities, enabling a balance between individual health and eco-friendly practices.

[0103] The proposal department can also be equipped with a "community collaboration function." The proposal department can propose ways to connect individual eco-activities with local communities. For example, the proposal department can suggest participation in local eco-events and workshops. It can also suggest participation in local eco-activity groups and volunteer activities, thereby supporting individual eco-activities at a community level. Furthermore, the proposal department can provide information on local eco-activities, encouraging individuals to actively participate in them. This allows individual eco-activities to connect with community-wide eco-activities, generating a greater impact.

[0104] The suggestion section can also incorporate "gamification features." This section can gamify eco-friendly activities to increase individual motivation. For example, it can award points or badges based on the level of eco-friendly activity achieved. It can also display rankings for eco-friendly activities, encouraging competition among users. Furthermore, it can offer rewards and incentives for achieving eco-friendly activities, increasing individual motivation to participate. This makes eco-friendly activities more enjoyable and sustainable.

[0105] The aggregation unit can further enhance the transparency and reliability of credit creation by utilizing blockchain technology. The aggregation unit records the results of eco-activities on the blockchain and manages them in a tamper-proof manner. For example, the aggregation unit records eco-activity data on the blockchain, enabling verification by third parties. Furthermore, the aggregation unit can ensure transparency by recording the credit issuance history on the blockchain. In addition, the aggregation unit can improve reliability by recording the credit transaction history on the blockchain. This makes the credit creation process transparent and reliable, gaining the trust of companies and individuals.

[0106] The sales department can further utilize its "emotion estimation function" to implement sales strategies that enhance a company's purchasing intent. The sales department estimates the emotions of the company's representatives and sells credit at the optimal time. For example, if the representative is relaxed, the sales department will offer a detailed explanation. If the representative is stressed, the sales department can offer a concise and to-the-point proposal. Furthermore, if the representative is in a hurry, the sales department can offer a quick purchase option. This enables the implementation of sales strategies tailored to the emotions of the company's representatives, resulting in effective credit sales.

[0107] The data collection unit can further adjust its data collection method based on the user's emotions using an "emotion estimation function." The unit estimates the user's emotions and selects the optimal data collection method. For example, if the user is relaxed, the unit collects detailed data. If the user is stressed, it can collect only basic data. Furthermore, if the user is busy, it can collect only essential data. This enables data collection tailored to the user's emotions, reducing the user's burden.

[0108] The suggestion function can further customize suggestions based on the user's emotions using an "emotion estimation function." The suggestion function estimates the user's emotions and selects the most appropriate suggestion. For example, if the user is relaxed, the suggestion function will suggest a detailed eco-activity plan. If the user is stressed, the suggestion function can also suggest a simple and easy-to-follow eco-activity plan. Furthermore, if the user is in a hurry, the suggestion function can suggest an eco-activity plan that can be implemented quickly. This enables suggestions tailored to the user's emotions, promoting effective eco-friendly activities.

[0109] The aggregation unit can further adjust the crediting process based on the user's emotions using an "emotion estimation function." The aggregation unit estimates the user's emotions and selects the optimal crediting method. For example, if the user is relaxed, the aggregation unit aggregates detailed data and performs highly accurate crediting. If the user is stressed, the aggregation unit can aggregate only basic data and perform crediting quickly. Furthermore, if the user is in a hurry, the aggregation unit can prioritize the aggregation of only important data and perform crediting quickly. This enables crediting that is tailored to the user's emotions, resulting in efficient crediting.

[0110] The sales department can further use an "emotion estimation function" to adjust sales methods based on the user's emotions. The sales department estimates the user's emotions and selects the optimal sales method. For example, if the user is relaxed, the sales department will provide a sales method that includes detailed explanations. If the user is stressed, the sales department can provide a concise and to-the-point sales method. Furthermore, if the user is in a hurry, the sales department can provide a sales method that allows for quick purchase. This enables sales methods that respond to the user's emotions, resulting in effective credit sales.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects data on individuals' eco-friendly activities. For example, data is collected through methods such as taking photos with a camera in the home, transmitting electricity bill data, and real-time monitoring of energy consumption using sensors in the home. Step 2: The proposal department uses a generation AI to propose SDGs activity plans based on the data collected by the collection department. For example, it proposes SDGs activity plans such as updating home appliances, introducing LED lighting, introducing solar power generation, and providing guidance on energy-saving techniques. Step 3: The aggregation department aggregates the results of the SDGs activities proposed by the proposal department and converts them into credits. For example, the results of individual eco-activities are aggregated and converted into J-Credits. Step 4: The sales department sells the credits, which have been converted into credits by the aggregation department, to companies. For example, they sell the converted J-credits to companies to offset their greenhouse gas emissions.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, proposal unit, aggregation unit, and sales unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on eco-activities using the camera 42 and sensors of the smart device 14 and analyzes the data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an SDGs activity plan using AI generated by the specific processing unit 290 of the data processing unit 12. The aggregation unit aggregates the results of eco-activities and converts them into credits using the specific processing unit 290 of the data processing unit 12. The sales unit sells the credits to companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, proposal unit, aggregation unit, and sales unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on eco-activities using the camera 42 and sensors of the smart glasses 214 and analyzes the data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an SDGs activity plan using AI generated by the specific processing unit 290 of the data processing unit 12. The aggregation unit aggregates the results of eco-activities and converts them into credits using the specific processing unit 290 of the data processing unit 12. The sales unit sells the credits to companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, proposal unit, aggregation unit, and sales unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on eco-activities using the camera 42 and sensors of the headset terminal 314 and analyzes the data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an SDGs activity plan using AI generated by the specific processing unit 290 of the data processing unit 12. The aggregation unit aggregates the results of eco-activities and converts them into credits using the specific processing unit 290 of the data processing unit 12. The sales unit sells the credits to companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, proposal unit, aggregation unit, and sales unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data on eco-activities using the camera 42 and sensors of the robot 414 and analyzes the data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an SDGs activity plan using AI generated by the specific processing unit 290 of the data processing unit 12. The aggregation unit aggregates the results of eco-activities and converts them into credits using the specific processing unit 290 of the data processing unit 12. The sales unit sells the credits to companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A data collection department that collects data on individuals' eco-activities, Based on the data collected by the aforementioned collection unit, a proposal unit proposes an SDGs activity plan. The aggregation unit will collect and credit the results of the SDGs activities proposed by the aforementioned proposal unit, The system includes a sales department that sells the credits, which have been converted into credits by the aggregation department, to companies. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is collected through methods such as taking photos with cameras inside the home and transmitting electricity bill data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose SDGs activity plans such as updating home appliances, installing LED lighting, installing solar power generation, and providing guidance on energy-saving techniques. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose methods to reduce greenhouse gas emissions from travel, such as walking, cycling, and using public transportation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned aggregation unit is The results of individual eco-activities are compiled and converted into J-Credits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sales department, We sell J-Credits, which have been converted into credits, to companies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sales department, The profits earned will be returned to individual participants in the form of points. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze household energy consumption patterns and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system monitors the usage of each device in the home in real time and collects data related to eco-friendly activities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We monitor household water usage and waste generation, and collect data related to eco-friendly activities. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze users' social media activity and collect data related to eco-friendly activities. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We propose an optimal SDGs activity plan based on your household energy consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, We provide customized eco-friendly activity plans tailored to the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We propose the optimal eco-friendly activity plan, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We provide the optimal plan by referring to the user's past eco-activity history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned aggregation unit is We estimate the user's emotions and adjust the aggregation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned aggregation unit is During the aggregation process, the results of each household's eco-friendly activities will be analyzed in detail, and the optimal method for converting them into credits will be selected. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned aggregation unit is During aggregation, different crediting algorithms are applied for each type of eco-activity. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned aggregation unit is The system estimates user sentiment and determines aggregation priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned aggregation unit is During aggregation, credits will be created taking into account the results of eco-activities in each region. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned aggregation unit is During aggregation, we refer to relevant literature on eco-activities to improve the accuracy of crediting. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned sales department, We estimate user sentiment and adjust sales methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned sales department, At the time of sale, the optimal credit sales strategy is selected based on the company's needs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned sales department, At the time of sale, we analyze credit market trends to determine the optimal timing for sale. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned sales department, It estimates user sentiment and determines sales priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned sales department, We propose the optimal credit sales strategy at the time of sale, taking into account the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned sales department, We provide the optimal sales strategy by referring to a company's past credit purchase history at the time of sale. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection department that collects data on individuals' eco-activities, Based on the data collected by the aforementioned collection unit, a proposal unit proposes an SDGs activity plan. The aggregation unit will collect and credit the results of the SDGs activities proposed by the aforementioned proposal unit, The system includes a sales department that sells the credits, which have been converted into credits by the aggregation department, to companies. A system characterized by the following features.

2. The aforementioned collection unit is Data is collected through methods such as taking photos with cameras inside the home and transmitting electricity bill data. The system according to feature 1.

3. The aforementioned proposal section is, We propose SDGs activity plans such as updating home appliances, installing LED lighting, installing solar power generation, and providing guidance on energy-saving techniques. The system according to feature 1.

4. The aforementioned proposal section is, We propose methods to reduce greenhouse gas emissions from travel, such as walking, cycling, and using public transportation. The system according to feature 1.

5. The aforementioned aggregation unit is The results of individual eco-activities are compiled and converted into J-Credits. The system according to feature 1.

6. The aforementioned sales department, We sell J-Credits, which have been converted into credits, to companies. The system according to feature 1.

7. The aforementioned sales department, The profits earned will be returned to individual participants in the form of points. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze household energy consumption patterns and select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is The system monitors the usage of each device in the home in real time and collects data related to eco-friendly activities. The system according to feature 1.

Images