System

Abstract

An object of a system according to an embodiment is to grasp the amount of CO2 emission of waste and to effectively reuse recyclable items.SOLUTION: A system includes a CO2 emission calculation unit, a display unit, and a cooperation unit. The CO2 emission amount calculation unit calculates the CO2 emission amount based on the type and the amount of the waste. The display unit displays the CO2 emission calculated by the CO2 emission calculation unit to a user. A cooperation part cooperates the recyclable item specified by the CO2 emission calculation part with a FURIMA application or the like.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to adequately grasp the CO2 emissions of discarded waste and effectively reuse recyclable items.

[0005] The system according to the embodiment aims to grasp the amount of CO2 emissions from waste and to effectively reuse recyclable items. [Means for solving the problem]

[0006] The system according to the embodiment includes a CO2 emission calculation unit, a display unit, and a linking unit. The CO2 emission calculation unit calculates the amount of CO2 emissions based on the type and amount of waste. The display unit displays the amount of CO2 emissions calculated by the CO2 emission calculation unit to the user. The linking unit links the recyclable items identified by the CO2 emission calculation unit to a flea market app or the like. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the amount of CO2 emissions from waste and effectively reuse recyclable items. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The recycling promotion system according to an embodiment of the present invention is a system that displays the amount of CO2 emissions from waste generated at home and links this information with flea market apps, etc. This allows the recycling promotion system to visualize the amount of CO2 emissions from waste and promote recycling.

[0029] A recycling promotion system according to an embodiment includes a CO2 emission calculation unit, a display unit, and a linking unit. The CO2 emission calculation unit calculates CO2 emissions based on the type and amount of waste. For example, it calculates the CO2 emissions for each type of waste, such as plastic waste, paper waste, and metal waste, and then calculates the total. The display unit displays the CO2 emissions calculated by the CO2 emission calculation unit to the user. For example, it displays the amount in graph or chart format on a device such as a smartphone or tablet. The linking unit links recyclable items identified by the CO2 emission calculation unit to a flea market app or the like. For example, it identifies furniture, home appliances, clothing, etc. that are still usable and generates information for listing them on the flea market app. This allows the recycling promotion system to calculate the CO2 emissions of waste, display it to the user, and promote recycling.

[0030] When the type and amount of waste is input, the generation AI uses image recognition technology to automatically identify the type of waste and calculate the amount of CO2 emissions. For example, when a user takes a photo of waste using a smartphone camera, the generation AI automatically identifies the type of waste using image recognition technology. For example, it classifies the waste into categories such as plastic, paper, and metal, and calculates the CO2 emissions for each. This makes it possible to automatically identify the type of waste and calculate the amount of CO2 emissions.

[0031] When calculating the CO2 emissions from discarded waste, the generation AI learns from past data and can predict fluctuations in emissions depending on the season or event. For example, the CO2 emissions calculation unit can predict seasonal fluctuations in emissions by having the generation AI learn from past discarded waste data. For example, the generation AI can reflect the tendency for CO2 emissions to increase in summer, as more beverage containers are discarded. This makes it possible to predict fluctuations in emissions depending on the season or event.

[0032] When calculating the CO2 emissions from waste, the generation AI takes into account the emission standards for each region and can display region-specific emissions. For example, the generation AI refers to emission standard data for each region and calculates the CO2 emissions from waste. For example, different emission standards are applied to urban and rural areas, and region-specific emissions are displayed. This makes it possible to display region-specific emissions.

[0033] When calculating the CO2 emissions from waste disposal, the generation AI works in conjunction with other energy consumption data within the home to display the overall environmental impact. For example, the generation AI collects electricity, gas, and water consumption data within the home and integrates it with the CO2 emissions from waste disposal to display the overall environmental impact. For example, the generation AI displays the overall CO2 emissions by month in a graph. This makes it possible to display the overall environmental impact.

[0034] When the generation AI identifies recyclable items, the collaboration unit can refer to the user's past purchase history and usage history and suggest a recycling priority order. For example, the generation AI analyzes the user's past purchase history to identify recyclable items. For example, the collaboration unit preferentially suggests items that have been purchased for a certain period of time as recycling candidates. This makes it possible to suggest a recycling priority order.

[0035] When the generative AI identifies recyclable items, the collaboration unit can evaluate the condition of the item and suggest the possibility of repair or reuse. For example, the collaboration unit allows the generative AI to evaluate the condition of an item and suggest the possibility of repair or reuse. For example, it can analyze the condition of furniture or home appliances and suggest repair methods if repair is possible. This allows the condition of an item to be evaluated and the possibility of repair or reuse to be suggested.

[0036] The collaboration unit can collaborate with other recycling platforms when the generation AI identifies recyclable items and propose the optimal recycling method. For example, the collaboration unit allows the generation AI to collaborate with other recycling platforms to identify recyclable items. For example, the collaboration unit refers to a database of local recycling centers and proposes the optimal recycling method. This allows collaboration with other recycling platforms to propose the optimal recycling method.

[0037] When the generation AI identifies a recyclable item, the linking unit can automatically take a photo of the item and generate image data to be listed on the flea market app. For example, the linking unit can cause the generation AI to automatically take a photo of the item and generate image data to be listed on the flea market app. For example, the linking unit can use a smartphone camera to take a photo of the entire item. This allows the image data to be automatically taken and listed on the flea market app.

[0038] When the generation AI visualizes CO2 emissions, the display unit can provide a customizable dashboard according to the user's lifestyle. For example, the generation AI displays individual CO2 emissions based on household energy consumption and types of waste disposal. This makes it possible to provide a customizable dashboard according to the user's lifestyle.

[0039] When the generation AI visualizes CO2 emissions, the display unit can provide feedback indicating areas for improvement by comparing with past data. For example, the generation AI analyzes past CO2 emissions data, compares it with current data, and provides feedback indicating areas for improvement. For example, the display unit compares it with past monthly data to show which areas need improvement. This makes it possible to provide feedback indicating areas for improvement by comparing with past data.

[0040] When the generation AI visualizes CO2 emissions, the display unit can display comparative data with other households or regions, thereby enhancing a sense of competition. For example, the display unit allows the generation AI to collect CO2 emissions data from other households or regions and display it in comparison with the user's data. For example, the display unit allows the user to check their own emissions in comparison with the average emissions in the same region. This allows the display of comparative data with other households or regions to enhance a sense of competition.

[0041] The collaboration unit allows the generation AI to provide personalized advice based on the user's past recycling records when raising awareness about the importance of recycling. For example, the collaboration unit allows the generation AI to analyze the user's past recycling records and provide personalized advice. For example, the collaboration unit suggests the next item to be recycled based on the type and amount of items recycled in the past. This makes it possible to provide personalized advice based on the user's past recycling records.

[0042] When the generation AI raises awareness of the importance of recycling, the collaboration unit can simulate the specific impact of recycling on the environment and present it to the user. For example, the collaboration unit allows the generation AI to simulate the specific impact of recycling on the environment and present it to the user. For example, it displays the amount of CO2 emissions and energy consumption reduced by recycling. In this way, the specific impact of recycling on the environment can be simulated and presented to the user.

[0043] The Collaboration Department can propose collaboration with other environmental protection activities when the Generating AI raises awareness of the importance of recycling. For example, when the Generating AI raises awareness of the importance of recycling, the Collaboration Department can propose collaboration with other environmental protection activities. For example, the Collaboration Department can propose an eco-event that combines recycling and tree-planting activities. This can propose collaboration with other environmental protection activities.

[0044] The collaboration unit can provide points and rewards that can be earned by users participating in recycling activities when the generation AI raises awareness of the importance of recycling. For example, the collaboration unit can provide points and rewards that can be earned by users participating in recycling activities when the generation AI raises awareness of the importance of recycling. For example, a system can be introduced that allows users to earn points every time they recycle. This makes it possible to provide points and rewards that can be earned by participating in recycling activities.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The recycling promotion system may further include a health impact assessment unit that collects the user's health data and displays the impact of recycling activities on health. For example, the system evaluates the amount of exercise and stress reduction effects of recycling activities and provides feedback to the user. This allows the user to see the positive impact that recycling activities have on health.

[0047] The recycling promotion system may further include a purchase promotion unit that analyzes the user's purchase history and provides incentives for purchasing recyclable products. For example, points may be awarded each time a recyclable product is purchased, and a special benefit may be offered when a certain number of points are accumulated. This increases the user's motivation to actively purchase recyclable products.

[0048] The recycling promotion system can also be equipped with a results display section that records the user's recycling activities and visualizes long-term results. For example, it can display monthly recycling amounts and CO2 reduction amounts in graphs, allowing users to see the results of their efforts. This can increase users' motivation to continue recycling activities.

[0049] The recycling promotion system can also have a community function to support users' recycling activities. For example, it can provide event information for participating in local recycling activities and a platform where users can interact with each other. This allows users to feel a social connection to their recycling activities.

[0050] The recycling promotion system can also have a reminder function to support users' recycling activities. For example, the system can periodically notify users of recycling dates and information on recyclable items so that users do not forget to recycle. This allows users to continue recycling activities.

[0051] The recycling promotion system can also provide educational content to support users' recycling activities. For example, it can provide videos and articles explaining the importance of recycling and specific methods, allowing users to learn about recycling. This will help users deepen their understanding of recycling activities.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The CO2 emission calculation unit calculates the CO2 emissions based on the type and amount of waste. For example, it calculates the CO2 emissions for each type of waste, such as plastic waste, paper waste, and metal waste, and then calculates the total. Step 2: The display unit displays the CO2 emissions calculated by the CO2 emission calculation unit to the user, for example, in the form of a graph or chart on a device such as a smartphone or tablet. Step 3: The linking unit links the recyclable items identified by the CO2 emission calculation unit to a flea market app, etc. For example, it identifies furniture, home appliances, clothing, etc. that are still usable and generates information for listing them on the flea market app.

[0054] (Example 2) The recycling promotion system according to an embodiment of the present invention is a system that displays the amount of CO2 emissions from waste generated at home and links this information with flea market apps, etc. This allows the recycling promotion system to visualize the amount of CO2 emissions from waste and promote recycling.

[0055] A recycling promotion system according to an embodiment includes a CO2 emission calculation unit, a display unit, and a linking unit. The CO2 emission calculation unit calculates CO2 emissions based on the type and amount of waste. For example, it calculates the CO2 emissions for each type of waste, such as plastic waste, paper waste, and metal waste, and then calculates the total. The display unit displays the CO2 emissions calculated by the CO2 emission calculation unit to the user. For example, it displays the amount in graph or chart format on a device such as a smartphone or tablet. The linking unit links recyclable items identified by the CO2 emission calculation unit to a flea market app or the like. For example, it identifies furniture, home appliances, clothing, etc. that are still usable and generates information for listing them on the flea market app. This allows the recycling promotion system to calculate the CO2 emissions of waste, display it to the user, and promote recycling.

[0056] When the type and amount of waste is input, the generation AI uses image recognition technology to automatically identify the type of waste and calculate the amount of CO2 emissions. For example, when a user takes a photo of waste using a smartphone camera, the generation AI automatically identifies the type of waste using image recognition technology. For example, it classifies the waste into categories such as plastic, paper, and metal, and calculates the CO2 emissions for each. This makes it possible to automatically identify the type of waste and calculate the amount of CO2 emissions.

[0057] When calculating the CO2 emissions from discarded waste, the generation AI learns from past data and can predict fluctuations in emissions depending on the season or event. For example, the CO2 emissions calculation unit can predict seasonal fluctuations in emissions by having the generation AI learn from past discarded waste data. For example, the generation AI can reflect the tendency for CO2 emissions to increase in summer, as more beverage containers are discarded. This makes it possible to predict fluctuations in emissions depending on the season or event.

[0058] When calculating the CO2 emissions from waste, the generation AI takes into account the emission standards for each region and can display region-specific emissions. For example, the generation AI refers to emission standard data for each region and calculates the CO2 emissions from waste. For example, different emission standards are applied to urban and rural areas, and region-specific emissions are displayed. This makes it possible to display region-specific emissions.

[0059] When calculating the CO2 emissions from waste disposal, the generation AI works in conjunction with other energy consumption data within the home to display the overall environmental impact. For example, the generation AI collects electricity, gas, and water consumption data within the home and integrates it with the CO2 emissions from waste disposal to display the overall environmental impact. For example, the generation AI displays the overall CO2 emissions by month in a graph. This makes it possible to display the overall environmental impact.

[0060] The CO2 emission calculation unit uses the emotion estimation function to analyze the emotions expressed when the user inputs the type and amount of garbage, and can provide feedback to elicit positive emotions. For example, when a user inputs the type and amount of garbage, the generation AI analyzes their facial expressions and voice to estimate their emotions. For example, if the input task is stressful, an encouraging message is displayed. This allows for feedback to be provided to elicit positive emotions.

[0061] When the generation AI identifies recyclable items, the collaboration unit can refer to the user's past purchase history and usage history and suggest a recycling priority order. For example, the generation AI analyzes the user's past purchase history to identify recyclable items. For example, the collaboration unit preferentially suggests items that have been purchased for a certain period of time as recycling candidates. This makes it possible to suggest a recycling priority order.

[0062] When the generative AI identifies recyclable items, the collaboration unit can evaluate the condition of the item and suggest the possibility of repair or reuse. For example, the collaboration unit allows the generative AI to evaluate the condition of an item and suggest the possibility of repair or reuse. For example, it can analyze the condition of furniture or home appliances and suggest repair methods if repair is possible. This allows the condition of an item to be evaluated and the possibility of repair or reuse to be suggested.

[0063] The linking unit uses the emotion estimation function to analyze the emotions of the user when selecting recyclable items and can make recycling suggestions to elicit positive emotions. For example, when a user selects a recyclable item, the linking unit uses the generation AI to analyze the emotions and make suggestions to elicit positive emotions. For example, it displays a message that emphasizes the benefits of recycling. This makes it possible to make recycling suggestions to elicit positive emotions.

[0064] The collaboration unit can collaborate with other recycling platforms when the generation AI identifies recyclable items and propose the optimal recycling method. For example, the collaboration unit allows the generation AI to collaborate with other recycling platforms to identify recyclable items. For example, the collaboration unit refers to a database of local recycling centers and proposes the optimal recycling method. This allows collaboration with other recycling platforms to propose the optimal recycling method.

[0065] When the generation AI identifies a recyclable item, the linking unit can automatically take a photo of the item and generate image data to be listed on the flea market app. For example, the linking unit can cause the generation AI to automatically take a photo of the item and generate image data to be listed on the flea market app. For example, the linking unit can use a smartphone camera to take a photo of the entire item. This allows the image data to be automatically taken and listed on the flea market app.

[0066] The collaboration unit uses the emotion estimation function to analyze the emotions of users when selecting recyclable items in real time, and can provide incentives to raise awareness of recycling. For example, when a user selects a recyclable item, the collaboration unit uses the generative AI to analyze the emotions in real time and provide incentives to elicit positive emotions. For example, a system can be introduced that allows users to earn points every time they recycle. This can provide incentives to raise awareness of recycling.

[0067] When the generation AI visualizes CO2 emissions, the display unit can provide a customizable dashboard according to the user's lifestyle. For example, the generation AI displays individual CO2 emissions based on household energy consumption and types of waste disposal. This makes it possible to provide a customizable dashboard according to the user's lifestyle.

[0068] When the generation AI visualizes CO2 emissions, the display unit can provide feedback indicating areas for improvement by comparing with past data. For example, the generation AI analyzes past CO2 emissions data, compares it with current data, and provides feedback indicating areas for improvement. For example, the display unit compares it with past monthly data to show which areas need improvement. This makes it possible to provide feedback indicating areas for improvement by comparing with past data.

[0069] The display unit uses the emotion estimation function to analyze the emotions felt when the user views a display of CO2 emissions, and can suggest a display format that elicits positive emotions. For example, when a user views a display of CO2 emissions, the generation AI analyzes the emotions and suggests a display format that elicits positive emotions. For example, a message that evokes a sense of accomplishment can be displayed. This makes it possible to suggest a display format that elicits positive emotions.

[0070] When the generation AI visualizes CO2 emissions, the display unit can display comparative data with other households or regions, thereby enhancing a sense of competition. For example, the display unit allows the generation AI to collect CO2 emissions data from other households or regions and display it in comparison with the user's data. For example, the display unit allows the user to check their own emissions in comparison with the average emissions in the same region. This allows the display of comparative data with other households or regions to enhance a sense of competition.

[0071] The display unit uses the emotion estimation function to analyze the emotions of the user when they view the display of CO2 emissions in real time and provide feedback according to their emotions. For example, when a user views the display of CO2 emissions, the generation AI analyzes their emotions in real time and provides feedback according to their emotions. For example, if they have negative emotions, an encouraging message is displayed. This makes it possible to provide feedback according to their emotions.

[0072] The collaboration unit allows the generation AI to provide personalized advice based on the user's past recycling records when raising awareness about the importance of recycling. For example, the collaboration unit allows the generation AI to analyze the user's past recycling records and provide personalized advice. For example, the collaboration unit suggests the next item to be recycled based on the type and amount of items recycled in the past. This makes it possible to provide personalized advice based on the user's past recycling records.

[0073] When the generation AI raises awareness of the importance of recycling, the collaboration unit can simulate the specific impact of recycling on the environment and present it to the user. For example, the collaboration unit allows the generation AI to simulate the specific impact of recycling on the environment and present it to the user. For example, it displays the amount of CO2 emissions and energy consumption reduced by recycling. In this way, the specific impact of recycling on the environment can be simulated and presented to the user.

[0074] The linking unit uses the emotion estimation function to analyze the emotions a user feels when requesting information about recycling, and can provide information that elicits positive emotions. For example, when a user requests information about recycling, the linking unit uses the generation AI to analyze the emotions and provide information that elicits positive emotions. For example, it displays a message that emphasizes the benefits of recycling. This makes it possible to provide information that elicits positive emotions.

[0075] The Collaboration Department can propose collaboration with other environmental protection activities when the Generating AI raises awareness of the importance of recycling. For example, when the Generating AI raises awareness of the importance of recycling, the Collaboration Department can propose collaboration with other environmental protection activities. For example, the Collaboration Department can propose an eco-event that combines recycling and tree-planting activities. This can propose collaboration with other environmental protection activities.

[0076] The collaboration unit can provide points and rewards that can be earned by users participating in recycling activities when the generation AI raises awareness of the importance of recycling. For example, the collaboration unit can provide points and rewards that can be earned by users participating in recycling activities when the generation AI raises awareness of the importance of recycling. For example, a system can be introduced that allows users to earn points every time they recycle. This makes it possible to provide points and rewards that can be earned by participating in recycling activities.

[0077] The linking unit uses the emotion estimation function to analyze the emotions of users when they request information about recycling in real time, and can carry out awareness-raising activities according to their emotions. For example, when a user requests information about recycling, the linking unit uses the generation AI to analyze the emotions in real time and carry out awareness-raising activities according to their emotions. For example, it displays a message to elicit positive emotions. This makes it possible to carry out awareness-raising activities according to their emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The recycling promotion system may further include a health impact assessment unit that collects the user's health data and displays the impact of recycling activities on health. For example, the system evaluates the amount of exercise and stress reduction effects of recycling activities and provides feedback to the user. This allows the user to see the positive impact that recycling activities have on health.

[0080] The recycling promotion system may further include a purchase promotion unit that analyzes the user's purchase history and provides incentives for purchasing recyclable products. For example, points may be awarded each time a recyclable product is purchased, and a special benefit may be offered when a certain number of points are accumulated. This increases the user's motivation to actively purchase recyclable products.

[0081] The recycling promotion system can also estimate the user's emotions and incorporate game elements to increase motivation for recycling activities. For example, a system can be provided where users can earn points each time they perform a recycling activity and compete with other users in a ranking format. This allows users to participate in recycling activities while having fun.

[0082] The recycling promotion system may further include an emotion feedback unit that estimates the user's emotions and provides positive feedback regarding the recycling activity. For example, when the user performs a recycling activity, the system analyzes the user's emotions and displays a message that enhances the user's sense of accomplishment and satisfaction. This allows the user to have positive emotions regarding the recycling activity.

[0083] The recycling promotion system can also estimate the user's emotions and provide support to reduce negative emotions toward recycling activities. For example, for users who find recycling to be a hassle, the system can suggest easy ways to recycle and convenient tools. This can reduce the negative emotions users have toward recycling activities.

[0084] The recycling promotion system can also estimate the user's emotions and provide a message expressing gratitude for the recycling activity. For example, when the user performs recycling activities, the system analyzes the user's emotions and displays a message of gratitude and data showing the results of the recycling. This allows the user to feel appreciated for their recycling activities.

[0085] The recycling promotion system can also be equipped with a results display section that records the user's recycling activities and visualizes long-term results. For example, it can display monthly recycling amounts and CO2 reduction amounts in graphs, allowing users to see the results of their efforts. This can increase users' motivation to continue recycling activities.

[0086] The recycling promotion system can also have a community function to support users' recycling activities. For example, it can provide event information for participating in local recycling activities and a platform where users can interact with each other. This allows users to feel a social connection to their recycling activities.

[0087] The recycling promotion system can also have a reminder function to support users' recycling activities. For example, the system can periodically notify users of recycling dates and information on recyclable items so that users do not forget to recycle. This allows users to continue recycling activities.

[0088] The recycling promotion system can also provide educational content to support users' recycling activities. For example, it can provide videos and articles explaining the importance of recycling and specific methods, allowing users to learn about recycling. This will help users deepen their understanding of recycling activities.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The CO2 emission calculation unit calculates the CO2 emissions based on the type and amount of waste. For example, it calculates the CO2 emissions for each type of waste, such as plastic waste, paper waste, and metal waste, and then calculates the total. Step 2: The display unit displays the CO2 emissions calculated by the CO2 emission calculation unit to the user, for example, in the form of a graph or chart on a device such as a smartphone or tablet. Step 3: The linking unit links the recyclable items identified by the CO2 emission calculation unit to a flea market app, etc. For example, it identifies furniture, home appliances, clothing, etc. that are still usable and generates information for listing them on the flea market app.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a CO2 emission calculation unit that calculates CO2 emissions based on the type and amount of waste; a display unit that displays the CO2 emission amount calculated by the CO2 emission calculation unit to a user; a linking unit that links the recyclable items identified by the CO2 emission calculation unit to a flea market app or the like. A system characterized by:

2. The CO2 emission calculation unit When the type and amount of waste is entered, the AI ​​uses image recognition technology to automatically identify the type of waste and calculate the amount of CO2 emissions.

2. The system of claim 1.

3. The CO2 emission calculation unit When calculating CO2 emissions from waste disposal, the AI ​​generator takes into account regional emission standards and displays region-specific emissions.

2. The system of claim 1.

4. The linking unit is When the generation AI identifies the recyclable items, it refers to the user's past purchase and usage history and suggests recycling priorities.

2. The system of claim 1.

5. The display unit When the generating AI visualizes the CO2 emissions, it provides a customizable dashboard according to the user's lifestyle.

2. The system of claim 1.

6. The CO2 emission calculation unit Analyzing the emotions felt by the user when inputting the type and amount of waste, and providing feedback to elicit positive emotions 2. The system of claim 1.

7. The linking unit is Analyzing the emotions of the user when selecting the recyclable items and providing recycling suggestions to elicit the positive emotions.

2. The system of claim 1.

8. The display unit Analyzing the emotions felt by the user when viewing the display of the CO2 emissions, and proposing a display format that will elicit positive emotions 2. The system of claim 1.

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