System

Abstract

An object of a system according to an embodiment is to confirm an environmental influence due to a purchase activity of a user and to promote product selection in consideration of sustainability.SOLUTION: A system includes an environmental influence confirmation part, a point use part, and a recommendation part. The environmental influence confirmation unit confirms the environmental influence due to the purchase activity of the user. The point using section uses the points obtained in the transaction to purchase the eco-friendly commodity. The recommendation unit recommends a product selection in consideration of sustainability.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 technologies do not adequately understand the environmental impact of users' purchasing activities and encourage them to make sustainable product choices, so there is room for improvement.

[0005] The system according to the embodiment aims to check the environmental impact of a user's purchasing activities and promote product selection with sustainability in mind. [Means for solving the problem]

[0006] The system according to the embodiment includes an environmental impact confirmation unit, a points utilization unit, and a recommendation unit. The environmental impact confirmation unit confirms the environmental impact of a user's purchasing activities. The points utilization unit purchases eco-friendly products using points earned in transactions. The recommendation unit recommends product selections that are conscious of sustainability. [Effects of the Invention]

[0007] The system according to the embodiment can check the environmental impact of a user's purchasing activities and promote product selection with sustainability in mind. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The eco-friendly purchasing system according to an embodiment of the present invention is a system that checks the environmental impact of a user's purchasing activities, allows the user to purchase eco-friendly products using points earned through transactions, and recommends sustainable product selections. As a result, the eco-friendly purchasing system can check the environmental impact of a user's purchasing activities, purchase eco-friendly products, and recommend sustainable product selections.

[0029] An eco-friendly purchasing system according to an embodiment includes an environmental impact confirmation unit, a points utilization unit, and a recommendation unit. The environmental impact confirmation unit confirms the environmental impact of a user's purchasing activities. For example, the generation AI collects data on products purchased by the user and analyzes the environmental impact of the purchasing activities. The generation AI analyzes carbon dioxide emissions and resource usage during the product manufacturing and transportation processes and visualizes the impact for the user. For example, the generation AI displays the environmental impact of the products purchased by the user using graphs and numbers to indicate the level of environmental impact. The points utilization unit purchases eco-friendly products using points earned through transactions. For example, users can use points to purchase eco-friendly products, such as products that use renewable energy or recycled materials. The recommendation unit recommends product selections that are conscious of sustainability. For example, the generation AI analyzes the user's purchasing history and preferences and suggests environmentally friendly products. The generation AI provides information such as "This product is manufactured using renewable energy" or "This product uses recycled materials" to help users make sustainable choices. As a result, the eco-friendly purchasing system according to the embodiment can confirm the environmental impact of the user's purchasing activities, purchase eco-friendly products, and recommend sustainable product selections.

[0030] The environmental impact confirmation unit can analyze the environmental impact based on the user's purchasing activities in real time and provide feedback immediately after the purchase. For example, immediately after a user purchases a product, the generation AI analyzes the carbon dioxide emissions from the manufacturing and transportation processes of the product in real time and provides immediate feedback to the user. For example, the environmental impact confirmation unit displays numerical values ​​and graphs of the environmental impact on the purchase completion screen. The environmental impact confirmation unit also analyzes the environmental impact in real time based on purchase history data and notifies the user of the results. For example, the generation AI uses the smartphone's notification function to send information about the environmental impact immediately after the purchase. The environmental impact confirmation unit also monitors the user's purchasing activities in real time, analyzes the environmental impact immediately after the purchase, and provides feedback. For example, a detailed environmental impact report is sent by email after the purchase is completed. This allows the user to check the environmental impact of the products they purchased in real time.

[0031] The environmental impact confirmation unit can integrate and analyze not only data from the manufacturer but also evaluation data from third-party organizations. For example, when evaluating the environmental impact of a purchased product, the environmental impact confirmation unit integrates and analyzes not only data provided by the manufacturer but also evaluation data from third-party organizations. For example, it uses data from environmental protection organizations and research institutes. In addition, the environmental impact confirmation unit uses a generation AI to integrate the manufacturer's data with evaluation data from third-party organizations and present the user with a comprehensive environmental impact. For example, it combines the manufacturer's carbon dioxide emissions data with resource usage data from third-party organizations. The environmental impact confirmation unit also builds a system that simultaneously analyzes the manufacturer's data and evaluation data from third-party organizations to evaluate the environmental impact of purchased products from multiple perspectives. For example, it integrates the manufacturer's energy consumption data with waste data from third-party organizations. This allows environmental impact to be analyzed based on multiple data.

[0032] The environmental impact confirmation unit can analyze the environmental impact of purchased products as their impact on the user's entire lifestyle and present the overall environmental load. For example, the environmental impact confirmation unit analyzes the environmental impact of purchased products as their impact on the user's entire lifestyle and presents the overall environmental load. For example, it displays the total carbon dioxide emissions based on annual purchasing activities. The environmental impact confirmation unit also uses the generation AI to analyze the user's purchasing history and evaluate the environmental impact on the entire lifestyle. For example, it calculates the environmental load for each category, such as groceries, clothing, and home appliances, and presents the overall impact. The environmental impact confirmation unit also analyzes the environmental impact of purchased products as their impact on the user's entire lifestyle and visualizes the long-term environmental load. For example, it displays a graph showing the trend in environmental load based on purchasing activities over the past few years. This allows the environmental impact on the user's entire lifestyle to be evaluated.

[0033] The environmental impact confirmation unit may add a function that allows the user to share the results of the environmental impact analysis with the user's friends and family, thereby promoting joint efforts to reduce the environmental impact. The environmental impact confirmation unit may add a function that allows the user to share the results of the environmental impact analysis with the user's friends and family, thereby promoting joint efforts to reduce the environmental impact. For example, the analysis results may be shared via social media or a messaging app. The environmental impact confirmation unit may also provide a platform for sharing the results of the environmental impact analyzed by the generation AI with the user's friends and family. For example, the purchasing activities of all family members may be integrated to display the overall environmental impact. The environmental impact confirmation unit may also add a function that allows the user to share the results of the environmental impact analysis with friends and family, thereby allowing the user to create an action plan to reduce the environmental impact together with friends and family. For example, the user may make a plan to jointly purchase eco-friendly products. This may promote joint efforts to reduce the environmental impact together with friends and family.

[0034] The point utilization unit can quantify the environmental contribution of eco-friendly products when points are used, and visualize that contribution for the user. For example, the point utilization unit builds a system that quantifies the environmental contribution of eco-friendly products when points are used, and visualizes that contribution for the user. For example, it displays an environmental contribution score on the purchase screen. The point utilization unit also uses a generation AI to analyze the environmental contribution of eco-friendly products, and presents the results to the user when points are used. For example, it displays the amount of carbon dioxide reduction or resource savings of eco-friendly products in numerical form. The point utilization unit also provides an interface for visualizing the environmental contribution of eco-friendly products when points are used. For example, it displays the environmental contribution of eco-friendly products in graphs or icons. This makes it easier for users to understand the environmental contribution of eco-friendly products.

[0035] The point usage unit analyzes the point usage history, evaluates the extent to which the user has purchased eco-friendly products, and can show the progress of eco-friendly activities. The point usage unit, for example, analyzes the point usage history and builds a system that evaluates the extent to which the user has purchased eco-friendly products. For example, it tallys up the number of eco-friendly product purchases and the amount of points used. The point usage unit also uses a generation AI to analyze the point usage history and generate a report showing the progress of the user's eco-friendly activities. For example, it displays the monthly eco-friendly product purchase status in a graph. The point usage unit also evaluates the progress of the user's eco-friendly activities based on the point usage history and provides an interface that visualizes the results. For example, it displays the degree of achievement of eco-friendly activities with badges or medals. This makes it possible to visualize the progress of the user's eco-friendly activities.

[0036] The point utilization unit expands the range of eco-friendly products that can be purchased using points to include eco-friendly products and services that are local specialties. For example, the point utilization unit builds a system that expands the range of eco-friendly products that can be purchased using points to include eco-friendly products and services that are local specialties. For example, local agricultural products and handicrafts are offered as eco-friendly products. The point utilization unit also uses a generation AI to analyze eco-friendly products and services that are local specialties and add them as items eligible for point utilization. For example, it makes it possible to purchase participation in local eco-tours and environmental conservation activities with points. The point utilization unit also expands the scope of point utilization to include eco-friendly products and services that are local specialties, allowing users to participate in local eco-activities. For example, local eco-events and workshops can be booked with points. This makes it possible to purchase eco-friendly products and services that are local specialties with points.

[0037] The point utilization unit can provide options when using points, not only for purchasing eco-friendly products but also for donating to environmental protection activities and participating in volunteer activities. For example, the point utilization unit builds a system that provides options when using points, not only for purchasing eco-friendly products but also for donating to environmental protection activities and participating in volunteer activities. For example, using points to donate to an environmental protection organization. Furthermore, the point utilization unit uses the generation AI to expand the options for using points, allowing users to participate in environmental protection activities. For example, using points to purchase tickets to participate in volunteer activities. Furthermore, the point utilization unit provides options when using points, not only for purchasing eco-friendly products but also for donating to environmental protection activities and participating in volunteer activities. For example, using points to participate in tree planting activities. This increases the options for users to participate in environmental protection activities.

[0038] The recommendation unit analyzes not only a user's purchase history, but also their social media posts and search history, enabling more accurate recommendations. For example, the generation AI in the recommendation unit analyzes not only a user's purchase history but also their social media posts and search history to provide more accurate recommendations. For example, it can suggest sustainable products based on articles the user shared on social media and keywords they searched for. The recommendation unit also analyzes a user's social media posts and search history, and the generation AI recommends sustainable products based on that data. For example, it can suggest eco-friendly products related to topics the user is interested in. The recommendation unit also recommends sustainability-conscious products by comprehensively analyzing a user's purchase history, social media posts, and search history. For example, it can suggest related eco-friendly products based on information on environmental protection that the user searched for. This enables more accurate recommendations based on a variety of user data.

[0039] The recommendation unit can provide detailed information about the sustainability of a product when making a recommendation. For example, the recommendation unit provides detailed information about the sustainability of a product when the generation AI makes a recommendation. For example, details about the manufacturing process and the materials used may be displayed, allowing the user to make a sustainable choice. The recommendation unit also builds a system that provides detailed information about the sustainability of recommended products. For example, the energy source used in the product manufacturing process and the percentage of recycled materials may be displayed. The recommendation unit also provides detailed information about the sustainability of a product when the generation AI makes a recommendation, allowing the user to make a selection based on that information. For example, the product's environmental impact and whether it has acquired an eco-label may be displayed. This allows the user to select a product based on detailed information about sustainability.

[0040] The recommendation unit may add a function that allows the user to share recommended products with the user's friends and family, thereby promoting collaborative efforts to select sustainable products. For example, the recommendation unit may add a function that allows the user to share recommended products with the user's friends and family, thereby promoting collaborative efforts to select sustainable products. For example, the recommendation unit may share recommendation results via social media or messaging apps. The recommendation unit may also provide a platform for sharing products recommended by the generative AI with the user's friends and family. For example, the recommendation unit may integrate the purchasing histories of all family members and evaluate their overall sustainability. The recommendation unit may also add a function to share recommended products, thereby allowing the user to create an action plan for collaboratively selecting sustainable products with friends and family. For example, the recommendation unit may make a plan to jointly purchase eco-friendly products. This may promote collaborative efforts to select sustainable products with friends and family.

[0041] The recommendation unit can suggest sustainable products that are appropriate for the user's region and season when making recommendations. For example, when the generation AI makes recommendations, the recommendation unit suggests sustainable products that are appropriate for the user's region and season. For example, it recommends local specialties or eco-friendly products that are suitable for the season. The recommendation unit also builds a system that recommends sustainable products that are appropriate for the user's region and season. For example, it suggests eco-friendly products that match the local climate or seasonal events. The recommendation unit also suggests sustainable products that are appropriate for the user's region and season when the generation AI makes recommendations, allowing the user to make a selection based on that information. For example, it provides a seasonal eco-friendly product catalog. This makes it possible to suggest sustainable products that are appropriate for the user's region and season.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The eco-purchasing system can also include a function that compares the environmental impact of a user's purchasing activities with that of other users. For example, a user can compare the environmental impact of the products they purchased with the average value of other users to see how environmentally conscious their purchasing activities are. The generation AI can also aggregate users' purchasing activities by region to visualize the environmental impact of the entire region. Furthermore, when a user purchases an eco-friendly product, the system can compare the product's environmental contribution with other eco-friendly products to help them make the most environmentally friendly choice. This allows users to see how environmentally conscious their purchasing activities are compared to other users and regions, and make more sustainable choices.

[0044] The environmental impact confirmation unit can also be equipped with a function to track the environmental impact of a user's purchasing activities over the long term and compare it with past data. For example, it can tally up the environmental impact of products purchased by the user over the past year by month and display the trend in a graph. The generation AI can also analyze the user's purchasing activities on a yearly basis to evaluate the long-term environmental impact. Furthermore, when a user purchases an eco-friendly product, it can compare the product's environmental contribution with past purchases to see how environmentally conscious their choice is. This allows users to see how environmentally conscious their purchasing activities are over the long term and receive help making sustainable choices.

[0045] The environmental impact confirmation unit can further share the environmental impact based on the user's purchasing activities with other users, promoting joint efforts to reduce environmental impact. For example, a user can share the environmental impact of purchased products with friends and family and make plans to jointly purchase eco-friendly products. The generation AI can also aggregate users' purchasing activities by group and visualize the environmental impact of the entire group. Furthermore, when a user purchases an eco-friendly product, the product's environmental contribution can be shared within the group to help them make the most environmentally friendly choice. This allows users to jointly work with friends and family to reduce environmental impact and promote sustainable choices.

[0046] The environmental impact confirmation unit can further compare the environmental impact of a user's purchasing activities with that of other users, incorporating a competitive element. For example, the environmental impact of a user's purchased products can be compared with that of other users, and a reward can be offered to the user who makes the most environmentally conscious purchases. The generation AI can also display users' purchasing activities in a ranking format and offer special benefits to users who rank highly. Furthermore, when a user purchases an eco-friendly product, the environmental contribution of that product can be compared with that of other users, helping the user make the most environmentally friendly choice. This allows users to compete with other users in an effort to reduce their environmental impact and promote sustainable choices.

[0047] The environmental impact confirmation unit can further share the environmental impact based on the user's purchasing activities with other users, promoting joint efforts to reduce environmental impact. For example, a user can share the environmental impact of purchased products with friends and family and make plans to jointly purchase eco-friendly products. The generation AI can also aggregate users' purchasing activities by group and visualize the environmental impact of the entire group. Furthermore, when a user purchases an eco-friendly product, the product's environmental contribution can be shared within the group to help them make the most environmentally friendly choice. This allows users to jointly work with friends and family to reduce environmental impact and promote sustainable choices.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The environmental impact confirmation unit confirms the environmental impact of the user's purchasing activities. For example, the generation AI collects data on products purchased by the user and analyzes the impact of those purchasing activities on the environment. The generation AI analyzes the carbon dioxide emissions and resource usage during the product manufacturing and transportation processes, and visualizes the impact for the user. For example, the generation AI displays the environmental impact of the products purchased by the user in graphs and figures, indicating the extent of the environmental impact. Step 2: The point usage unit purchases eco-friendly products using the points acquired through the transaction. For example, the user can use the points to purchase eco-friendly products such as products that use renewable energy or recycled materials. Step 3: The recommendation unit recommends sustainable product choices. For example, the generation AI analyzes the user's purchasing history and preferences to suggest environmentally friendly products. For example, the generation AI provides information such as "This product is manufactured using renewable energy" or "This product uses recycled materials" to help the user make sustainable choices.

[0050] (Example 2) The eco-friendly purchasing system according to an embodiment of the present invention is a system that checks the environmental impact of a user's purchasing activities, allows the user to purchase eco-friendly products using points earned through transactions, and recommends sustainable product selections. As a result, the eco-friendly purchasing system can check the environmental impact of a user's purchasing activities, purchase eco-friendly products, and recommend sustainable product selections.

[0051] An eco-friendly purchasing system according to an embodiment includes an environmental impact confirmation unit, a points utilization unit, and a recommendation unit. The environmental impact confirmation unit confirms the environmental impact of a user's purchasing activities. For example, the generation AI collects data on products purchased by the user and analyzes the environmental impact of the purchasing activities. The generation AI analyzes carbon dioxide emissions and resource usage during the product manufacturing and transportation processes and visualizes the impact for the user. For example, the generation AI displays the environmental impact of the products purchased by the user using graphs and numbers to indicate the level of environmental impact. The points utilization unit purchases eco-friendly products using points earned through transactions. For example, users can use points to purchase eco-friendly products, such as products that use renewable energy or recycled materials. The recommendation unit recommends product selections that are conscious of sustainability. For example, the generation AI analyzes the user's purchasing history and preferences and suggests environmentally friendly products. The generation AI provides information such as "This product is manufactured using renewable energy" or "This product uses recycled materials" to help users make sustainable choices. As a result, the eco-friendly purchasing system according to the embodiment can confirm the environmental impact of the user's purchasing activities, purchase eco-friendly products, and recommend sustainable product selections.

[0052] The environmental impact confirmation unit can analyze the environmental impact based on the user's purchasing activities in real time and provide feedback immediately after the purchase. For example, immediately after a user purchases a product, the generation AI analyzes the carbon dioxide emissions from the manufacturing and transportation processes of the product in real time and provides immediate feedback to the user. For example, the environmental impact confirmation unit displays numerical values ​​and graphs of the environmental impact on the purchase completion screen. The environmental impact confirmation unit also analyzes the environmental impact in real time based on purchase history data and notifies the user of the results. For example, the generation AI uses the smartphone's notification function to send information about the environmental impact immediately after the purchase. The environmental impact confirmation unit also monitors the user's purchasing activities in real time, analyzes the environmental impact immediately after the purchase, and provides feedback. For example, a detailed environmental impact report is sent by email after the purchase is completed. This allows the user to check the environmental impact of the products they purchased in real time.

[0053] The environmental impact confirmation unit can integrate and analyze not only data from the manufacturer but also evaluation data from third-party organizations. For example, when evaluating the environmental impact of a purchased product, the environmental impact confirmation unit integrates and analyzes not only data provided by the manufacturer but also evaluation data from third-party organizations. For example, it uses data from environmental protection organizations and research institutes. In addition, the environmental impact confirmation unit uses a generation AI to integrate the manufacturer's data with evaluation data from third-party organizations and present the user with a comprehensive environmental impact. For example, it combines the manufacturer's carbon dioxide emissions data with resource usage data from third-party organizations. The environmental impact confirmation unit also builds a system that simultaneously analyzes the manufacturer's data and evaluation data from third-party organizations to evaluate the environmental impact of purchased products from multiple perspectives. For example, it integrates the manufacturer's energy consumption data with waste data from third-party organizations. This allows environmental impact to be analyzed based on multiple data.

[0054] The environmental impact confirmation unit can use the emotion estimation function to analyze the user's emotions regarding environmental impacts and adjust the content of the feedback based on the emotions. The environmental impact confirmation unit, for example, uses the emotion estimation function to analyze the user's emotions regarding environmental impacts and adjust the content of the feedback based on the results. For example, if the user has positive emotions, the environmental impact confirmation unit suggests further eco-friendly activities. The environmental impact confirmation unit also analyzes the user's emotional response in real time and individually customizes the content of the feedback regarding environmental impacts. For example, if the user has negative emotions, the environmental impact confirmation unit suggests specific actions to reduce environmental load. The environmental impact confirmation unit also uses the emotion estimation function to analyze the user's emotions regarding environmental impacts and adjusts the tone and content of the feedback based on the emotions. For example, if the user is surprised, a detailed explanation is added. This makes it possible to provide feedback that corresponds to the user's emotions.

[0055] The environmental impact confirmation unit can analyze the environmental impact of purchased products as their impact on the user's entire lifestyle and present the overall environmental load. For example, the environmental impact confirmation unit analyzes the environmental impact of purchased products as their impact on the user's entire lifestyle and presents the overall environmental load. For example, it displays the total carbon dioxide emissions based on annual purchasing activities. The environmental impact confirmation unit also uses the generation AI to analyze the user's purchasing history and evaluate the environmental impact on the entire lifestyle. For example, it calculates the environmental load for each category, such as groceries, clothing, and home appliances, and presents the overall impact. The environmental impact confirmation unit also analyzes the environmental impact of purchased products as their impact on the user's entire lifestyle and visualizes the long-term environmental load. For example, it displays a graph showing the trend in environmental load based on purchasing activities over the past few years. This allows the environmental impact on the user's entire lifestyle to be evaluated.

[0056] The environmental impact confirmation unit may add a function that allows the user to share the results of the environmental impact analysis with the user's friends and family, thereby promoting joint efforts to reduce the environmental impact. The environmental impact confirmation unit may add a function that allows the user to share the results of the environmental impact analysis with the user's friends and family, thereby promoting joint efforts to reduce the environmental impact. For example, the analysis results may be shared via social media or a messaging app. The environmental impact confirmation unit may also provide a platform for sharing the results of the environmental impact analyzed by the generation AI with the user's friends and family. For example, the purchasing activities of all family members may be integrated to display the overall environmental impact. The environmental impact confirmation unit may also add a function that allows the user to share the results of the environmental impact analysis with friends and family, thereby allowing the user to create an action plan to reduce the environmental impact together with friends and family. For example, the user may make a plan to jointly purchase eco-friendly products. This may promote joint efforts to reduce the environmental impact together with friends and family.

[0057] The environmental impact confirmation unit can use the emotion estimation function to analyze the emotions of the user when receiving information about environmental impacts in real time and provide an interface for eliciting positive emotions. For example, the environmental impact confirmation unit can use the emotion estimation function to analyze the emotions of the user when receiving information about environmental impacts in real time and provide an interface for eliciting positive emotions. For example, the environmental impact confirmation unit can display positive messages and graphics. The environmental impact confirmation unit can also analyze the user's emotional reactions in real time and provide a customized interface for eliciting positive emotions when receiving information about environmental impacts. For example, the environmental impact confirmation unit can use designs and colors that match the user's preferences. The environmental impact confirmation unit can also use the emotion estimation function to analyze the emotions of the user when receiving information about environmental impacts and adjust the content and tone of the interface based on the results. For example, if the user is feeling negative emotions, the environmental impact confirmation unit can display an encouraging message. This allows the user to receive information about environmental impacts with positive emotions.

[0058] The point utilization unit can quantify the environmental contribution of eco-friendly products when points are used, and visualize that contribution for the user. For example, the point utilization unit builds a system that quantifies the environmental contribution of eco-friendly products when points are used, and visualizes that contribution for the user. For example, it displays an environmental contribution score on the purchase screen. The point utilization unit also uses a generation AI to analyze the environmental contribution of eco-friendly products, and presents the results to the user when points are used. For example, it displays the amount of carbon dioxide reduction or resource savings of eco-friendly products in numerical form. The point utilization unit also provides an interface for visualizing the environmental contribution of eco-friendly products when points are used. For example, it displays the environmental contribution of eco-friendly products in graphs or icons. This makes it easier for users to understand the environmental contribution of eco-friendly products.

[0059] The point usage unit analyzes the point usage history, evaluates the extent to which the user has purchased eco-friendly products, and can show the progress of eco-friendly activities. The point usage unit, for example, analyzes the point usage history and builds a system that evaluates the extent to which the user has purchased eco-friendly products. For example, it tallys up the number of eco-friendly product purchases and the amount of points used. The point usage unit also uses a generation AI to analyze the point usage history and generate a report showing the progress of the user's eco-friendly activities. For example, it displays the monthly eco-friendly product purchase status in a graph. The point usage unit also evaluates the progress of the user's eco-friendly activities based on the point usage history and provides an interface that visualizes the results. For example, it displays the degree of achievement of eco-friendly activities with badges or medals. This makes it possible to visualize the progress of the user's eco-friendly activities.

[0060] The point utilization unit can use the emotion estimation function to analyze the emotion of a user when purchasing an eco-friendly product and make a point utilization suggestion based on the emotion. The point utilization unit, for example, uses the emotion estimation function to analyze the emotion of a user when purchasing an eco-friendly product and builds a system that makes a point utilization suggestion based on the result. For example, a user who has positive emotions is encouraged to purchase an eco-friendly product. The point utilization unit also analyzes the user's emotional response in real time and makes a point utilization suggestion based on the emotion when purchasing an eco-friendly product. For example, if the user is feeling happy, it suggests the use of additional points. The point utilization unit also uses the emotion estimation function to analyze the emotion of a user when purchasing an eco-friendly product and makes a customized point utilization suggestion based on the emotion. For example, if the user is excited, it suggests a special eco-friendly product. This makes it possible to make a point utilization suggestion based on the user's emotion.

[0061] The point utilization unit expands the range of eco-friendly products that can be purchased using points to include eco-friendly products and services that are local specialties. For example, the point utilization unit builds a system that expands the range of eco-friendly products that can be purchased using points to include eco-friendly products and services that are local specialties. For example, local agricultural products and handicrafts are offered as eco-friendly products. The point utilization unit also uses a generation AI to analyze eco-friendly products and services that are local specialties and add them as items eligible for point utilization. For example, it makes it possible to purchase participation in local eco-tours and environmental conservation activities with points. The point utilization unit also expands the scope of point utilization to include eco-friendly products and services that are local specialties, allowing users to participate in local eco-activities. For example, local eco-events and workshops can be booked with points. This makes it possible to purchase eco-friendly products and services that are local specialties with points.

[0062] The point utilization unit can provide options when using points, not only for purchasing eco-friendly products but also for donating to environmental protection activities and participating in volunteer activities. For example, the point utilization unit builds a system that provides options when using points, not only for purchasing eco-friendly products but also for donating to environmental protection activities and participating in volunteer activities. For example, using points to donate to an environmental protection organization. Furthermore, the point utilization unit uses the generation AI to expand the options for using points, allowing users to participate in environmental protection activities. For example, using points to purchase tickets to participate in volunteer activities. Furthermore, the point utilization unit provides options when using points, not only for purchasing eco-friendly products but also for donating to environmental protection activities and participating in volunteer activities. For example, using points to participate in tree planting activities. This increases the options for users to participate in environmental protection activities.

[0063] The point use unit can use the emotion estimation function to analyze the emotion of the user when using points in real time and provide an interface that elicits positive emotions. The point use unit, for example, uses the emotion estimation function to analyze the emotion of the user when using points in real time and provide an interface that elicits positive emotions. For example, it displays positive messages and graphics when the user uses points. The point use unit also analyzes the user's emotional response in real time and provides a customized interface that elicits positive emotions when using points. For example, it uses designs and colors that match the user's preferences. The point use unit also uses the emotion estimation function to analyze the emotion of the user when using points and adjusts the content and tone of the interface based on the results. For example, if the user is feeling negative emotions, it displays an encouraging message. This allows the user to use points with positive emotions.

[0064] The recommendation unit analyzes not only a user's purchase history, but also their social media posts and search history, enabling more accurate recommendations. For example, the generation AI in the recommendation unit analyzes not only a user's purchase history but also their social media posts and search history to provide more accurate recommendations. For example, it can suggest sustainable products based on articles the user shared on social media and keywords they searched for. The recommendation unit also analyzes a user's social media posts and search history, and the generation AI recommends sustainable products based on that data. For example, it can suggest eco-friendly products related to topics the user is interested in. The recommendation unit also recommends sustainability-conscious products by comprehensively analyzing a user's purchase history, social media posts, and search history. For example, it can suggest related eco-friendly products based on information on environmental protection that the user searched for. This enables more accurate recommendations based on a variety of user data.

[0065] The recommendation unit can provide detailed information about the sustainability of a product when making a recommendation. For example, the recommendation unit provides detailed information about the sustainability of a product when the generation AI makes a recommendation. For example, details about the manufacturing process and the materials used may be displayed, allowing the user to make a sustainable choice. The recommendation unit also builds a system that provides detailed information about the sustainability of recommended products. For example, the energy source used in the product manufacturing process and the percentage of recycled materials may be displayed. The recommendation unit also provides detailed information about the sustainability of a product when the generation AI makes a recommendation, allowing the user to make a selection based on that information. For example, the product's environmental impact and whether it has acquired an eco-label may be displayed. This allows the user to select a product based on detailed information about sustainability.

[0066] The recommendation unit can use the emotion estimation function to analyze how the user feels about the recommended product and adjust the content of the recommendation based on that emotion. For example, the recommendation unit can use the emotion estimation function to analyze how the user feels about the recommended product and adjust the content of the recommendation based on the results. For example, the recommendation unit can prioritize recommending products for which the user feels positive emotions. The recommendation unit can also analyze the user's emotional response in real time and customize the content of the recommendation individually. For example, for a user who feels negative emotions, the recommendation unit can suggest another sustainable product. The recommendation unit can also use the emotion estimation function to analyze how the user feels about the recommended product and adjust the tone and content of the recommendation based on that emotion. For example, if the user feels surprised, a detailed explanation can be added. This makes it possible to make recommendations according to the user's emotions.

[0067] The recommendation unit may add a function that allows the user to share recommended products with the user's friends and family, thereby promoting collaborative efforts to select sustainable products. For example, the recommendation unit may add a function that allows the user to share recommended products with the user's friends and family, thereby promoting collaborative efforts to select sustainable products. For example, the recommendation unit may share recommendation results via social media or messaging apps. The recommendation unit may also provide a platform for sharing products recommended by the generative AI with the user's friends and family. For example, the recommendation unit may integrate the purchasing histories of all family members and evaluate their overall sustainability. The recommendation unit may also add a function to share recommended products, thereby allowing the user to create an action plan for collaboratively selecting sustainable products with friends and family. For example, the recommendation unit may make a plan to jointly purchase eco-friendly products. This may promote collaborative efforts to select sustainable products with friends and family.

[0068] The recommendation unit can suggest sustainable products that are appropriate for the user's region and season when making recommendations. For example, when the generation AI makes recommendations, the recommendation unit suggests sustainable products that are appropriate for the user's region and season. For example, it recommends local specialties or eco-friendly products that are suitable for the season. The recommendation unit also builds a system that recommends sustainable products that are appropriate for the user's region and season. For example, it suggests eco-friendly products that match the local climate or seasonal events. The recommendation unit also suggests sustainable products that are appropriate for the user's region and season when the generation AI makes recommendations, allowing the user to make a selection based on that information. For example, it provides a seasonal eco-friendly product catalog. This makes it possible to suggest sustainable products that are appropriate for the user's region and season.

[0069] The recommendation unit can use the emotion estimation function to analyze the user's emotions toward the recommended product in real time and provide an interface for eliciting positive emotions. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotions toward the recommended product in real time and provide an interface for eliciting positive emotions. For example, it displays positive messages and graphics. The recommendation unit also analyzes the user's emotional response in real time and provides a customized interface for eliciting emotions toward the recommended product. For example, it uses designs and colors that match the user's preferences. The recommendation unit also uses the emotion estimation function to analyze the user's emotions toward the recommended product and adjusts the content and tone of the interface based on the results. For example, if the user is feeling negative emotions, it displays an encouraging message. This allows the user to receive the recommended product with positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The eco-purchasing system can also include a function that compares the environmental impact of a user's purchasing activities with that of other users. For example, a user can compare the environmental impact of the products they purchased with the average value of other users to see how environmentally conscious their purchasing activities are. The generation AI can also aggregate users' purchasing activities by region to visualize the environmental impact of the entire region. Furthermore, when a user purchases an eco-friendly product, the system can compare the product's environmental contribution with other eco-friendly products to help them make the most environmentally friendly choice. This allows users to see how environmentally conscious their purchasing activities are compared to other users and regions, and make more sustainable choices.

[0072] The environmental impact confirmation unit can also be equipped with a function to track the environmental impact of a user's purchasing activities over the long term and compare it with past data. For example, it can tally up the environmental impact of products purchased by the user over the past year by month and display the trend in a graph. The generation AI can also analyze the user's purchasing activities on a yearly basis to evaluate the long-term environmental impact. Furthermore, when a user purchases an eco-friendly product, it can compare the product's environmental contribution with past purchases to see how environmentally conscious their choice is. This allows users to see how environmentally conscious their purchasing activities are over the long term and receive help making sustainable choices.

[0073] The environmental impact confirmation unit can further share the environmental impact based on the user's purchasing activities with other users, promoting joint efforts to reduce environmental impact. For example, a user can share the environmental impact of purchased products with friends and family and make plans to jointly purchase eco-friendly products. The generation AI can also aggregate users' purchasing activities by group and visualize the environmental impact of the entire group. Furthermore, when a user purchases an eco-friendly product, the product's environmental contribution can be shared within the group to help them make the most environmentally friendly choice. This allows users to jointly work with friends and family to reduce environmental impact and promote sustainable choices.

[0074] The environmental impact confirmation unit can further compare the environmental impact of a user's purchasing activities with that of other users, incorporating a competitive element. For example, the environmental impact of a user's purchased products can be compared with that of other users, and a reward can be offered to the user who makes the most environmentally conscious purchases. The generation AI can also display users' purchasing activities in a ranking format and offer special benefits to users who rank highly. Furthermore, when a user purchases an eco-friendly product, the environmental contribution of that product can be compared with that of other users, helping the user make the most environmentally friendly choice. This allows users to compete with other users in an effort to reduce their environmental impact and promote sustainable choices.

[0075] The environmental impact confirmation unit can further share the environmental impact based on the user's purchasing activities with other users, promoting joint efforts to reduce environmental impact. For example, a user can share the environmental impact of purchased products with friends and family and make plans to jointly purchase eco-friendly products. The generation AI can also aggregate users' purchasing activities by group and visualize the environmental impact of the entire group. Furthermore, when a user purchases an eco-friendly product, the product's environmental contribution can be shared within the group to help them make the most environmentally friendly choice. This allows users to jointly work with friends and family to reduce environmental impact and promote sustainable choices.

[0076] The environmental impact confirmation unit uses the emotion estimation function to analyze how the user feels about environmental impacts and can adjust the content of the feedback based on that emotion. For example, if the user feels positive, the unit suggests further eco-friendly activities. The environmental impact confirmation unit also analyzes the user's emotional response in real time and individually customizes the content of the feedback about environmental impacts. For example, if a user feels negative, the unit suggests specific actions to reduce environmental load. The environmental impact confirmation unit also uses the emotion estimation function to analyze how the user feels about environmental impacts and adjusts the tone and content of the feedback based on that emotion. For example, if the user feels surprised, the unit adds a detailed explanation. This makes it possible to provide feedback that matches the user's emotions.

[0077] The environmental impact confirmation unit can use the emotion estimation function to analyze the user's emotions in real time when receiving information about environmental impacts, and provide an interface that elicits positive emotions. For example, it can display positive messages and graphics. The environmental impact confirmation unit can also analyze the user's emotional reactions in real time and provide a customized interface that elicits positive emotions when receiving information about environmental impacts. For example, it can use designs and colors that match the user's preferences. The environmental impact confirmation unit can also use the emotion estimation function to analyze the user's emotions when receiving information about environmental impacts, and adjust the content and tone of the interface based on the results. For example, if the user is feeling negative emotions, it can display an encouraging message. This allows the user to receive information about environmental impacts with positive emotions.

[0078] The point utilization unit can use the emotion estimation function to analyze the emotion of the user when purchasing an eco-friendly product and make point utilization suggestions based on that emotion. For example, it encourages users who are feeling positive emotions to purchase eco-friendly products. The point utilization unit also analyzes the user's emotional response in real time and makes point utilization suggestions based on their emotion when purchasing an eco-friendly product. For example, if the user is feeling happy, it suggests the use of additional points. The point utilization unit also uses the emotion estimation function to analyze the emotion of the user when purchasing an eco-friendly product and makes customized point utilization suggestions based on that emotion. For example, if the user is excited, it suggests special eco-friendly products. This makes it possible to make point utilization suggestions based on the user's emotion.

[0079] The recommendation unit uses the emotion estimation function to analyze how the user feels about the recommended product and can adjust the content of the recommendation based on that emotion. For example, it may preferentially recommend products for which the user feels positive emotions. The recommendation unit also analyzes the user's emotional response in real time and customizes the content of the recommendation individually. For example, it may suggest a different sustainable product to a user who feels negative emotions. The recommendation unit also uses the emotion estimation function to analyze how the user feels about the recommended product and adjusts the tone and content of the recommendation based on that emotion. For example, if the user feels surprised, it may add a detailed explanation. This makes it possible to make recommendations that correspond to the user's emotions.

[0080] The recommendation unit can use the emotion estimation function to analyze the user's emotions toward the recommended product in real time and provide an interface that elicits positive emotions. For example, it can display positive messages and graphics. The recommendation unit can also analyze the user's emotional response in real time and provide a customized interface that elicits emotions toward the recommended product. For example, it can use designs and colors that match the user's preferences. The recommendation unit can also use the emotion estimation function to analyze the user's emotions toward the recommended product and adjust the content and tone of the interface based on the results. For example, if the user is feeling negative emotions, it can display an encouraging message. This allows the user to receive the recommended product with positive emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The environmental impact confirmation unit confirms the environmental impact of the user's purchasing activities. For example, the generation AI collects data on products purchased by the user and analyzes the impact of those purchasing activities on the environment. The generation AI analyzes the carbon dioxide emissions and resource usage during the product manufacturing and transportation processes, and visualizes the impact for the user. For example, the generation AI displays the environmental impact of the products purchased by the user in graphs and figures, indicating the extent of the environmental impact. Step 2: The point usage unit purchases eco-friendly products using the points acquired through the transaction. For example, the user can use the points to purchase eco-friendly products such as products that use renewable energy or recycled materials. Step 3: The recommendation unit recommends sustainable product choices. For example, the generation AI analyzes the user's purchasing history and preferences to suggest environmentally friendly products. For example, the generation AI provides information such as "This product is manufactured using renewable energy" or "This product uses recycled materials" to help the user make sustainable choices.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

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

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. an environmental impact confirmation unit that confirms the environmental impact of users' purchasing activities; A point-use department where points earned through transactions are used to purchase eco-friendly products; A recommendation unit that recommends sustainability-conscious product selection. A system characterized by:

2. The environmental impact confirmation unit Analyze the environmental impact of the user's purchasing activities in real time and provide feedback immediately after purchase 2. The system of claim 1.

3. The environmental impact confirmation unit In addition to data from manufacturers, we also integrate and analyze evaluation data from third-party organizations.

2. The system of claim 1.

4. The environmental impact confirmation unit Analyzing how the user feels about the environmental impact and adjusting the feedback content based on the emotion 2. The system of claim 1.

5. The environmental impact confirmation unit Analyze the environmental impact of purchased products as a function of the user's overall lifestyle, and present the overall environmental impact.

2. The system of claim 1.

6. The environmental impact confirmation unit Add a feature that allows users to share the results of their environmental impact analysis with their friends and family, encouraging joint efforts to reduce environmental impact.

2. The system of claim 1.

7. The environmental impact confirmation unit Analyzing the emotions of the user when receiving information about environmental impacts in real time and providing an interface for eliciting positive emotions 2. The system of claim 1.

8. The point utilization unit When points are used, the environmental contribution of the eco-product is quantified and the contribution is visualized for the user.

2. The system of claim 1.

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