System

Abstract

An object of a system according to an embodiment is to predict an upcoming fashion trend and promote reuse of clothing.SOLUTION: A system includes a trend prediction unit, a reuse promotion unit, and a recycle proposal unit. A trend prediction part learns and predicts the next fashion trend by using the generated AI. The reuse promotion unit promotes reuse of the clothing to the consumer based on the trend information predicted by the trend prediction unit. The recycling proposal unit analyzes the information on the consumer's clothing promoted by the reuse promotion unit, and proposes a method of reuse or recycling.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of generating a large amount of clothing waste and not recycling or reusing it sufficiently.

[0005] The system according to the embodiment aims to predict upcoming fashion trends and promote the reuse of clothing. [Means for solving the problem]

[0006] The system according to the embodiment comprises a trend prediction unit, a reuse promotion unit, and a recycling suggestion unit. The trend prediction unit uses generative AI to learn and predict upcoming fashion trends. The reuse promotion unit encourages consumers to reuse clothing based on trend information predicted by the trend prediction unit. The recycling suggestion unit analyzes information about the consumer's clothing encouraged by the reuse promotion unit and suggests ways to reuse and recycle. [Effects of the Invention]

[0007] The system according to the embodiment can predict upcoming fashion trends and promote the reuse of clothing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The clothing waste reduction system according to an embodiment of the present invention is a system that uses generative AI to learn and predict upcoming fashion trends and reduce clothing waste. As a result, the clothing waste reduction system can reduce clothing waste and promote reuse and recycling.

[0029] A clothing waste reduction system according to an embodiment includes a trend prediction unit, a reuse promotion unit, and a recycling suggestion unit. The trend prediction unit uses a generation AI to learn and predict upcoming fashion trends. For example, the generation AI learns past fashion trend data and predicts upcoming trends. The generation AI can also analyze fashion magazines and social media posts from the past 10 years to predict which styles and items are likely to become popular again. The generation AI can also predict trends based on fashion trend data and prompts for analyzing that data. The reuse promotion unit encourages consumers to reuse clothing based on the trend information predicted by the trend prediction unit. For example, if the generation AI predicts that "1990s fashion will become popular again next year," it advises consumers to keep their 1990s items rather than throw them away. The reuse promotion unit can also analyze information about the clothing owned by consumers and suggest ways to reuse and recycle them. For example, the generation AI can suggest, "This jacket can be remade into a new design," thereby encouraging consumers to reuse clothing rather than throwing it away. The recycling suggestion unit analyzes the information about the consumer's clothing prompted by the reuse promotion unit and suggests ways to reuse or recycle. For example, the generation AI analyzes the condition of the consumer's clothing and suggests optimal ways to remake or recycle it. The generation AI can also learn successful cases of reuse and recycling and provide specific remake methods to consumers. In this way, the clothing waste reduction system according to the embodiment can reduce clothing waste and promote reuse and recycling. For example, by allowing consumers to predict upcoming trends and store their clothing, wasteful waste can be prevented. Furthermore, by having the generation AI suggest ways to remake or recycle, consumers are more motivated to reuse clothing. This is expected to reduce clothing waste and contribute to the realization of a sustainable society.

[0030] The trend prediction unit can learn weather data and economic indicators in addition to past fashion trend data to improve the accuracy of trend predictions. The generation AI learns weather data and economic indicators in addition to past fashion trend data to improve the accuracy of trend predictions. For example, the generation AI learns weather data in addition to past fashion trend data to predict seasonal trends. For example, it combines past weather data and fashion data to predict items that will be popular during cold seasons. The generation AI also learns economic indicators to predict trends based on economic conditions. For example, it predicts that luxury brand items are likely to be popular during economically prosperous periods. The generation AI also learns weather data and economic indicators simultaneously to make trend predictions that take multiple factors into account. For example, it predicts demand for specific items based on temperature fluctuations and consumer purchasing power. This improves the accuracy of trend predictions.

[0031] The trend prediction unit can learn the purchasing history and preferences of individual consumers and provide personalized trend predictions. The generation AI learns the purchasing history and preferences of individual consumers and provides personalized trend predictions. For example, the generation AI learns the consumer's purchasing history and makes trend predictions tailored to individual preferences. For example, it predicts the next item that is likely to be purchased based on data on items purchased in the past. The generation AI also learns the consumer's preferences and provides personalized trend predictions. For example, it suggests the next popular item based on the consumer's favorite colors and styles. The generation AI also learns the consumer's purchasing history and preferences simultaneously to make more accurate personalized trend predictions. For example, it combines past purchasing data with preference trends to predict the next popular item. This makes it possible to make trend predictions tailored to each individual consumer.

[0032] The reuse promotion department can analyze the contents of a consumer's closet, identify items that are likely to become popular again, and encourage them to keep them. The generative AI analyzes the contents of a consumer's closet, identifies items that are likely to become popular again, and encourages them to keep them. For example, the generative AI analyzes the contents of a consumer's closet and identifies items that are likely to become popular again. For example, it lists items that are likely to become popular again based on past trend data. The generative AI also learns the consumer's closet data, identifies items that are likely to become popular again, and encourages them to keep them. For example, it predicts that items of a specific brand or design are likely to become popular again. The generative AI also analyzes the contents of a consumer's closet in real time, identifies items that are likely to become popular again, and encourages them to keep them. For example, it notifies consumers of the possibility of a resurgence of an item before they throw it away. This encourages them to keep items that are likely to become popular again, preventing clothing waste.

[0033] The reuse promotion department can suggest items that will become popular again at specific times based on the consumer's lifestyle and event schedule. The generation AI considers the consumer's lifestyle and event schedule to suggest items that will become popular again at specific times. For example, the generation AI learns the consumer's lifestyle data and suggests items that will become popular again at specific times. For example, it suggests items that match the consumer's event schedule. The generation AI can also analyze the consumer's event schedule to suggest items that will become popular again. For example, it can predict items that will become popular again in line with specific seasons or events. The generation AI can also consider the consumer's lifestyle and event schedule simultaneously to suggest items that will become popular again at specific times. For example, it can suggest items based on the consumer's annual schedule. This makes it possible to suggest popular items that match the consumer's lifestyle.

[0034] The recycling suggestion unit can analyze the condition of the consumer's clothing and suggest the optimal method for remaking or recycling. The generative AI analyzes the condition of the consumer's clothing and suggests the optimal method for remaking or recycling. For example, the generative AI analyzes the condition of the consumer's clothing and suggests the optimal method for remaking. For example, it provides remake ideas based on the material and design of the clothing. The generative AI also analyzes the condition of the consumer's clothing and suggests the optimal method for recycling. For example, it suggests recyclable methods based on the material of the clothing. The generative AI also analyzes the condition of the consumer's clothing in real time and suggests the optimal method for remaking or recycling. For example, it suggests the optimal remake method based on the state of deterioration of the clothing. This makes it possible to suggest the optimal remake or recycling method according to the condition of the clothing.

[0035] The recycling suggestion unit can learn from successful cases of reuse and recycling and provide specific remake methods to consumers. The generative AI learns from successful cases of reuse and recycling and provides specific remake methods to consumers. For example, the generative AI learns from successful cases of reuse and recycling and provides specific remake methods to consumers. For example, it can suggest remake procedures based on past successful cases. The generative AI can also learn from successful recycling cases and provide specific recycling methods to consumers. For example, it can suggest recyclable materials and methods. The generative AI can also learn from successful cases of reuse and recycling in real time and provide specific remake methods to consumers. For example, it can suggest remake ideas based on the latest successful cases. This makes it possible to provide specific remake methods based on successful cases.

[0036] The recycling suggestion unit can be applied not only to the reuse and recycling of clothing, but also to the recycling of plastic and metal products. Generative AI can be applied not only to the reuse and recycling of clothing, but also to the recycling of plastic and metal products. For example, generative AI can be applied not only to the reuse and recycling of clothing, but also to the recycling of plastic products. For example, it can suggest recycling methods for plastic products. Generative AI can also be applied to the recycling of metal products. For example, it can suggest recyclable methods for metal products. Generative AI can also build a system that comprehensively recycles clothing, plastic, and metal products. For example, it can analyze items in each category and suggest recycling methods. This allows it to be applied to recycling of things other than clothing.

[0037] The recycling suggestion unit can form a community for reuse and recycling, and provide a platform for consumers to share information with each other. The generative AI forms a community for reuse and recycling, and provides a platform for consumers to share information with each other. For example, the generative AI forms a community for reuse and recycling, and provides a platform for consumers to share information with each other. For example, they can share remake ideas and recycling methods. The generative AI also provides a platform for consumers to share successful reuse and recycling cases. For example, they can share remake procedures based on past success cases. The generative AI also forms a community for reuse and recycling in real time, and provides a platform for consumers to share information with each other. For example, they can share the latest remake ideas and recycling methods. This allows consumers to share information with each other and increase their motivation to reuse and recycle.

[0038] The generative AI can analyze the effect of reducing environmental impact through reduced clothing waste and show it in concrete numerical values. The generative AI can analyze the effect of reducing environmental impact through reduced clothing waste and show it in concrete numerical values. For example, the generative AI can analyze the effect of reducing environmental impact through reduced clothing waste and show it in concrete numerical values. For example, it can quantify the amount of waste reduction and the amount of CO2 emissions reduction. The generative AI can also analyze the effect of reducing environmental impact through reduced clothing waste and show it to consumers in concrete numerical values. For example, it can display the amount of waste reduction in graphs and charts. The generative AI can also analyze the effect of reducing environmental impact through reduced clothing waste in real time and show it in concrete numerical values. For example, every time a consumer reuses clothing, it can display the effect of reducing environmental impact in numerical values. This makes it possible to show the effect of reducing environmental impact in concrete numerical values.

[0039] Generative AI can track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society. Generative AI can track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society. For example, generative AI can track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society. For example, it can track the amount of clothing recycled and the number of items reused. Generative AI can also track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society for consumers. For example, it can display the amount of clothing recycled in graphs and charts. Generative AI can also track the effects of reuse and recycling in real time and evaluate their contribution to a sustainable society. For example, each time a consumer reuses clothing, it can display the contribution numerically. This makes it possible to evaluate the effects of reuse and recycling over the long term.

[0040] Generative AI can be applied to fields other than clothing as well, aiming to contribute comprehensively to the SDGs. Generative AI can be applied to fields other than clothing (for example, reducing food waste) as well, aiming to contribute comprehensively to the SDGs. For example, generative AI can be applied to fields other than clothing as well, aiming to contribute comprehensively to the SDGs. For example, it could make suggestions for reducing food waste. Generative AI could also learn data from fields other than clothing as well, aiming to contribute comprehensively to the SDGs. For example, it could make suggestions for reducing plastic waste. Generative AI could also build a system that comprehensively reduces waste from clothing, food, plastic, etc. For example, it could suggest ways to reduce waste in each category. This would allow it to be applied to fields other than clothing as well, making it possible to contribute comprehensively to the SDGs.

[0041] Generative AI can work with companies and local governments to propose specific measures to contribute to the SDGs. Generative AI can work with companies and local governments to propose specific measures to contribute to the SDGs. For example, generative AI can work with companies to propose specific measures to contribute to the SDGs. For example, it can support a company's waste reduction plan. Generative AI can also work with local governments to propose specific measures to contribute to the SDGs. For example, it can support a local recycling program. Generative AI can also work with companies and local governments to propose comprehensive measures to contribute to the SDGs. For example, it can propose a waste reduction project in cooperation between companies and local governments. This allows it to work with companies and local governments to propose specific SDG measures.

[0042] Generative AI can educate consumers about the cycle of fashion trends and the importance of reuse and recycling using concrete examples. Generative AI educates consumers about the cycle of fashion trends and the importance of reuse and recycling using concrete examples. For example, generative AI can educate consumers about the cycle of fashion trends using concrete examples. For example, it can introduce examples of past trends becoming popular again. Generative AI can also educate consumers about the importance of reuse and recycling using concrete examples. For example, it can show the effect of recycling on reducing environmental impact. Generative AI can also educate consumers about the cycle of fashion trends and the importance of reuse and recycling using concrete examples. For example, it can introduce examples of successful remakes. This makes it possible to educate consumers using concrete examples.

[0043] Generative AI can provide personalized educational content according to the consumer's age and interests. Generative AI provides personalized educational content according to the consumer's age and interests. For example, generative AI provides personalized educational content according to the consumer's age. For example, it might suggest trend information for young people or recycling methods for the elderly. Generative AI also provides personalized educational content according to the consumer's interests. For example, it might provide information on specific fashion styles or how to remake clothes. Generative AI also provides personalized educational content that takes the consumer's age and interests into consideration. For example, it might suggest the most suitable educational content based on the consumer's profile data. This makes it possible to provide personalized educational content to consumers.

[0044] Generative AI can provide educational programs for schools and companies, and conduct awareness-raising activities for a wide range of people. Generative AI can provide educational programs for schools and companies, and conduct awareness-raising activities for a wide range of people. For example, generative AI can provide educational programs for schools and companies, educating students about fashion trends and the importance of recycling. For example, it can provide teaching materials that can be used in class. Generative AI can also provide educational programs for companies, and educate employees about the importance of recycling. For example, it can be incorporated into corporate training programs. Generative AI can also simultaneously provide educational programs for schools and companies, and conduct awareness-raising activities for a wide range of people. For example, it can provide educational content that can be used online. This allows awareness-raising activities to be conducted for a wide range of people.

[0045] Generative AI can develop interactive applications for consumer education, allowing learning to take place in a game-like manner. Generative AI can develop interactive applications for consumer education, allowing learning to take place in a game-like manner. For example, generative AI can develop interactive applications for consumer education, allowing learning to take place in a game-like manner. For example, generative AI can provide a quiz game that teaches the importance of recycling. Generative AI can also develop interactive applications for consumer education, providing a simulation game that teaches the cycle of fashion trends. Generative AI can also develop interactive applications for consumer education, providing a practical game that teaches how to remake things. For example, a game can be provided in which virtual clothing can be remade. This makes it possible to provide interactive education that allows consumers to learn in a game-like manner.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The clothing waste reduction system can also analyze consumers' purchasing history and suggest ways to reuse items they have previously purchased. For example, it can suggest ways to remake or recycle items based on data on items a consumer has previously purchased. Also, if a consumer has a preference for a particular brand or design, it can suggest ways to reuse items based on that brand or design. It can also analyze the condition of items a consumer has previously purchased and suggest the optimal way to reuse them. This makes it possible to make personalized reuse suggestions based on a consumer's purchasing history.

[0048] The clothing waste reduction system can also analyze consumers' lifestyle data and suggest reuse methods tailored to specific life events. For example, when a consumer is attending an event such as a wedding or party, it can suggest ways to remake previously purchased items into a new design. It can also suggest ways to reuse existing items to create a new style when a consumer goes on a trip. Furthermore, by suggesting reuse methods tailored to the consumer's lifestyle, it can increase consumers' motivation to reuse clothing rather than throw it away.

[0049] The clothing waste reduction system can also analyze the contents of a consumer's closet, identify items that are likely to become popular again, and encourage them to keep them. For example, the system can analyze the contents of a consumer's closet and create a list of items that are likely to become popular again. If a consumer has a preference for a particular brand or design, it can also identify items that are likely to become popular again based on that brand or design. It can also analyze the contents of a consumer's closet in real time, identify items that are likely to become popular again, and encourage them to keep them. This can prevent clothing waste by encouraging the storage of items that are likely to become popular again.

[0050] The clothing waste reduction system can also analyze a consumer's purchasing history and suggest recycling methods for items purchased in the past. For example, it can suggest recycling methods based on data on items a consumer has purchased in the past. Also, if a consumer has a preference for a particular brand or design, it can suggest recycling methods based on that brand or design. It can also analyze the condition of items a consumer has purchased in the past and suggest the optimal recycling method. This makes it possible to make personalized recycling suggestions based on a consumer's purchasing history.

[0051] The clothing waste reduction system can also analyze consumer lifestyle data and suggest recycling methods tailored to specific life events. For example, when a consumer is attending an event such as a wedding or party, it can suggest ways to recycle previously purchased items into a new design. It can also suggest ways to recycle items that consumers already own to create a new style when they go on a trip. Furthermore, by suggesting recycling methods tailored to the consumer's lifestyle, it can increase the consumer's motivation to recycle clothing rather than throw it away.

[0052] The clothing waste reduction system can also analyze the contents of a consumer's closet, identify items that are likely to become popular again, and encourage them to keep them. For example, the system can analyze the contents of a consumer's closet and create a list of items that are likely to become popular again. If a consumer has a preference for a particular brand or design, it can also identify items that are likely to become popular again based on that brand or design. It can also analyze the contents of a consumer's closet in real time, identify items that are likely to become popular again, and encourage them to keep them. This can prevent clothing waste by encouraging the storage of items that are likely to become popular again.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The trend prediction unit uses generative AI to learn and predict upcoming fashion trends. For example, generative AI can learn from past fashion trend data and predict upcoming trends. Generative AI can also analyze fashion magazines and social media posts from the past 10 years to predict which styles and items are likely to become popular again. Generative AI can also make trend predictions based on fashion trend data and prompts that instruct it to analyze that data. Step 2: The reuse promotion unit encourages consumers to reuse clothing based on the trend information predicted by the trend prediction unit. For example, if the generative AI predicts that "90s fashion will be popular again next year," it will advise consumers to store their 90s items rather than throw them away. The reuse promotion unit can also analyze information about the clothing that consumers own and suggest ways to reuse or recycle them. For example, the generative AI could suggest that "this jacket can be remade into a new design," thereby increasing consumers' motivation to reuse clothing rather than throw it away. Step 3: The recycling suggestion unit analyzes the information about the consumer's clothing provided by the reuse promotion unit and suggests ways to reuse or recycle. For example, the generative AI can analyze the condition of the consumer's clothing and suggest optimal ways to remake or recycle it. The generative AI can also learn from successful reuse and recycling cases and provide specific remaking methods to consumers.

[0055] (Example 2) The clothing waste reduction system according to an embodiment of the present invention is a system that uses generative AI to learn and predict upcoming fashion trends and reduce clothing waste. As a result, the clothing waste reduction system can reduce clothing waste and promote reuse and recycling.

[0056] A clothing waste reduction system according to an embodiment includes a trend prediction unit, a reuse promotion unit, and a recycling suggestion unit. The trend prediction unit uses a generation AI to learn and predict upcoming fashion trends. For example, the generation AI learns past fashion trend data and predicts upcoming trends. The generation AI can also analyze fashion magazines and social media posts from the past 10 years to predict which styles and items are likely to become popular again. The generation AI can also predict trends based on fashion trend data and prompts for analyzing that data. The reuse promotion unit encourages consumers to reuse clothing based on the trend information predicted by the trend prediction unit. For example, if the generation AI predicts that "1990s fashion will become popular again next year," it advises consumers to keep their 1990s items rather than throw them away. The reuse promotion unit can also analyze information about the clothing owned by consumers and suggest ways to reuse and recycle them. For example, the generation AI can suggest, "This jacket can be remade into a new design," thereby encouraging consumers to reuse clothing rather than throwing it away. The recycling suggestion unit analyzes the information about the consumer's clothing prompted by the reuse promotion unit and suggests ways to reuse or recycle. For example, the generation AI analyzes the condition of the consumer's clothing and suggests optimal ways to remake or recycle it. The generation AI can also learn successful cases of reuse and recycling and provide specific remake methods to consumers. In this way, the clothing waste reduction system according to the embodiment can reduce clothing waste and promote reuse and recycling. For example, by allowing consumers to predict upcoming trends and store their clothing, wasteful waste can be prevented. Furthermore, by having the generation AI suggest ways to remake or recycle, consumers are more motivated to reuse clothing. This is expected to reduce clothing waste and contribute to the realization of a sustainable society.

[0057] The trend prediction unit can learn weather data and economic indicators in addition to past fashion trend data to improve the accuracy of trend predictions. The generation AI learns weather data and economic indicators in addition to past fashion trend data to improve the accuracy of trend predictions. For example, the generation AI learns weather data in addition to past fashion trend data to predict seasonal trends. For example, it combines past weather data and fashion data to predict items that will be popular during cold seasons. The generation AI also learns economic indicators to predict trends based on economic conditions. For example, it predicts that luxury brand items are likely to be popular during economically prosperous periods. The generation AI also learns weather data and economic indicators simultaneously to make trend predictions that take multiple factors into account. For example, it predicts demand for specific items based on temperature fluctuations and consumer purchasing power. This improves the accuracy of trend predictions.

[0058] The trend prediction unit can learn the purchasing history and preferences of individual consumers and provide personalized trend predictions. The generation AI learns the purchasing history and preferences of individual consumers and provides personalized trend predictions. For example, the generation AI learns the consumer's purchasing history and makes trend predictions tailored to individual preferences. For example, it predicts the next item that is likely to be purchased based on data on items purchased in the past. The generation AI also learns the consumer's preferences and provides personalized trend predictions. For example, it suggests the next popular item based on the consumer's favorite colors and styles. The generation AI also learns the consumer's purchasing history and preferences simultaneously to make more accurate personalized trend predictions. For example, it combines past purchasing data with preference trends to predict the next popular item. This makes it possible to make trend predictions tailored to each individual consumer.

[0059] The trend prediction unit uses the emotion estimation function to analyze consumer emotions and make trend predictions that elicit a positive emotional response. The generation AI uses the emotion estimation function to analyze consumer emotions and make trend predictions that elicit a positive emotional response. For example, the generation AI analyzes consumer emotions and makes trend predictions that elicit positive emotions. For example, it predicts items that will make consumers feel joy or excitement. The emotion estimation function is also used to analyze in real time how consumers feel about trend predictions and adjust the prediction results. For example, it modifies predictions to avoid negative emotions. The generation AI also learns consumer emotional data and makes trend predictions that elicit a positive emotional response. For example, it makes predictions based on items that consumers have previously expressed positive emotions about. This makes it possible to make trend predictions that take consumer emotions into consideration.

[0060] The reuse promotion department can analyze the contents of a consumer's closet, identify items that are likely to become popular again, and encourage them to keep them. The generative AI analyzes the contents of a consumer's closet, identifies items that are likely to become popular again, and encourages them to keep them. For example, the generative AI analyzes the contents of a consumer's closet and identifies items that are likely to become popular again. For example, it lists items that are likely to become popular again based on past trend data. The generative AI also learns the consumer's closet data, identifies items that are likely to become popular again, and encourages them to keep them. For example, it predicts that items of a specific brand or design are likely to become popular again. The generative AI also analyzes the contents of a consumer's closet in real time, identifies items that are likely to become popular again, and encourages them to keep them. For example, it notifies consumers of the possibility of a resurgence of an item before they throw it away. This encourages them to keep items that are likely to become popular again, preventing clothing waste.

[0061] The reuse promotion department can suggest items that will become popular again at specific times based on the consumer's lifestyle and event schedule. The generation AI considers the consumer's lifestyle and event schedule to suggest items that will become popular again at specific times. For example, the generation AI learns the consumer's lifestyle data and suggests items that will become popular again at specific times. For example, it suggests items that match the consumer's event schedule. The generation AI can also analyze the consumer's event schedule to suggest items that will become popular again. For example, it can predict items that will become popular again in line with specific seasons or events. The generation AI can also consider the consumer's lifestyle and event schedule simultaneously to suggest items that will become popular again at specific times. For example, it can suggest items based on the consumer's annual schedule. This makes it possible to suggest popular items that match the consumer's lifestyle.

[0062] The reuse promotion department can use the emotion estimation function to analyze the emotions consumers have when throwing away clothing and suggest reuse methods that elicit positive emotions. The generation AI uses the emotion estimation function to analyze the emotions consumers have when throwing away clothing and suggest reuse methods that elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions consumers have when throwing away clothing. For example, emotions are estimated by analyzing the consumer's facial expressions and voice. The generation AI also suggests reuse methods that elicit positive emotions based on the consumer's emotional data. For example, it suggests reuse methods that make the consumer feel happy and satisfied. The emotion estimation function is also used to monitor the consumer's emotional reactions in real time and suggest reuse methods that elicit positive emotions. For example, it suggests remake methods that will emotionally satisfy the consumer. This makes it possible to suggest reuse methods that take the consumer's emotions into consideration.

[0063] The recycling suggestion unit can analyze the condition of the consumer's clothing and suggest the optimal method for remaking or recycling. The generative AI analyzes the condition of the consumer's clothing and suggests the optimal method for remaking or recycling. For example, the generative AI analyzes the condition of the consumer's clothing and suggests the optimal method for remaking. For example, it provides remake ideas based on the material and design of the clothing. The generative AI also analyzes the condition of the consumer's clothing and suggests the optimal method for recycling. For example, it suggests recyclable methods based on the material of the clothing. The generative AI also analyzes the condition of the consumer's clothing in real time and suggests the optimal method for remaking or recycling. For example, it suggests the optimal remake method based on the state of deterioration of the clothing. This makes it possible to suggest the optimal remake or recycling method according to the condition of the clothing.

[0064] The recycling suggestion unit can learn from successful cases of reuse and recycling and provide specific remake methods to consumers. The generative AI learns from successful cases of reuse and recycling and provides specific remake methods to consumers. For example, the generative AI learns from successful cases of reuse and recycling and provides specific remake methods to consumers. For example, it can suggest remake procedures based on past successful cases. The generative AI can also learn from successful recycling cases and provide specific recycling methods to consumers. For example, it can suggest recyclable materials and methods. The generative AI can also learn from successful cases of reuse and recycling in real time and provide specific remake methods to consumers. For example, it can suggest remake ideas based on the latest successful cases. This makes it possible to provide specific remake methods based on successful cases.

[0065] The recycling suggestion unit uses the emotion estimation function to analyze the emotions consumers have toward reuse and recycling, and can suggest methods to elicit positive emotions. The generation AI uses the emotion estimation function to analyze the emotions consumers have toward reuse and recycling, and makes suggestions that elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions consumers have toward reuse and recycling. For example, emotions are estimated by analyzing the consumer's facial expressions and voice. The generation AI also makes reuse and recycling suggestions that elicit positive emotions based on the consumer's emotional data. For example, it suggests remake methods that will make the consumer feel happy and satisfied. The emotion estimation function is also used to monitor the consumer's emotional responses in real time, and makes reuse and recycling suggestions that elicit positive emotions. For example, it suggests recycling methods that will emotionally satisfy the consumer. This makes it possible to make reuse and recycling suggestions that take into consideration the consumer's emotions.

[0066] The recycling suggestion unit can be applied not only to the reuse and recycling of clothing, but also to the recycling of plastic and metal products. Generative AI can be applied not only to the reuse and recycling of clothing, but also to the recycling of plastic and metal products. For example, generative AI can be applied not only to the reuse and recycling of clothing, but also to the recycling of plastic products. For example, it can suggest recycling methods for plastic products. Generative AI can also be applied to the recycling of metal products. For example, it can suggest recyclable methods for metal products. Generative AI can also build a system that comprehensively recycles clothing, plastic, and metal products. For example, it can analyze items in each category and suggest recycling methods. This allows it to be applied to recycling of things other than clothing.

[0067] The recycling suggestion unit can form a community for reuse and recycling, and provide a platform for consumers to share information with each other. The generative AI forms a community for reuse and recycling, and provides a platform for consumers to share information with each other. For example, the generative AI forms a community for reuse and recycling, and provides a platform for consumers to share information with each other. For example, they can share remake ideas and recycling methods. The generative AI also provides a platform for consumers to share successful reuse and recycling cases. For example, they can share remake procedures based on past success cases. The generative AI also forms a community for reuse and recycling in real time, and provides a platform for consumers to share information with each other. For example, they can share the latest remake ideas and recycling methods. This allows consumers to share information with each other and increase their motivation to reuse and recycle.

[0068] The recycling suggestion unit uses the emotion estimation function to analyze how consumers feel about reuse and recycling and can suggest ways to increase their motivation. The generation AI uses the emotion estimation function to analyze how consumers feel about reuse and recycling and make suggestions to increase their motivation. For example, the emotion estimation function can be used to analyze how consumers feel about reuse and recycling. For example, emotions can be estimated by analyzing consumers' facial expressions and voices. The generation AI can also make suggestions to increase their motivation to reuse and recycle based on consumer emotional data. For example, it can suggest remaking methods that will make consumers feel happy and satisfied. The emotion estimation function can also be used to monitor consumers' emotional responses in real time and make suggestions to increase their motivation to reuse and recycle. For example, it can suggest recycling methods that will emotionally satisfy consumers. This makes it possible to make reuse and recycling suggestions that take consumers' emotions into consideration.

[0069] The generative AI can analyze the effect of reducing environmental impact through reduced clothing waste and show it in concrete numerical values. The generative AI can analyze the effect of reducing environmental impact through reduced clothing waste and show it in concrete numerical values. For example, the generative AI can analyze the effect of reducing environmental impact through reduced clothing waste and show it in concrete numerical values. For example, it can quantify the amount of waste reduction and the amount of CO2 emissions reduction. The generative AI can also analyze the effect of reducing environmental impact through reduced clothing waste and show it to consumers in concrete numerical values. For example, it can display the amount of waste reduction in graphs and charts. The generative AI can also analyze the effect of reducing environmental impact through reduced clothing waste in real time and show it in concrete numerical values. For example, every time a consumer reuses clothing, it can display the effect of reducing environmental impact in numerical values. This makes it possible to show the effect of reducing environmental impact in concrete numerical values.

[0070] Generative AI can track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society. Generative AI can track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society. For example, generative AI can track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society. For example, it can track the amount of clothing recycled and the number of items reused. Generative AI can also track the effects of reuse and recycling over the long term and evaluate their contribution to a sustainable society for consumers. For example, it can display the amount of clothing recycled in graphs and charts. Generative AI can also track the effects of reuse and recycling in real time and evaluate their contribution to a sustainable society. For example, each time a consumer reuses clothing, it can display the contribution numerically. This makes it possible to evaluate the effects of reuse and recycling over the long term.

[0071] The generative AI can use its emotion estimation function to analyze the emotions consumers have toward contributing to the SDGs and make suggestions that elicit positive emotions. The generative AI can use its emotion estimation function to analyze the emotions consumers have toward contributing to the SDGs and make suggestions that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions consumers have toward contributing to the SDGs. For example, emotions can be estimated by analyzing consumers' facial expressions and voice. The generative AI can also make suggestions that elicit positive emotions toward contributing to the SDGs based on consumer emotion data. For example, it can suggest recycling methods that will make consumers feel happy and satisfied. The emotion estimation function can also be used to monitor consumers' emotional responses in real time and make suggestions that elicit positive emotions toward contributing to the SDGs. For example, it can suggest recycling methods that will emotionally satisfy consumers. This makes it possible to suggest SDG contributions that take consumers' emotions into consideration.

[0072] Generative AI can be applied to fields other than clothing as well, aiming to contribute comprehensively to the SDGs. Generative AI can be applied to fields other than clothing (for example, reducing food waste) as well, aiming to contribute comprehensively to the SDGs. For example, generative AI can be applied to fields other than clothing as well, aiming to contribute comprehensively to the SDGs. For example, it could make suggestions for reducing food waste. Generative AI could also learn data from fields other than clothing as well, aiming to contribute comprehensively to the SDGs. For example, it could make suggestions for reducing plastic waste. Generative AI could also build a system that comprehensively reduces waste from clothing, food, plastic, etc. For example, it could suggest ways to reduce waste in each category. This would allow it to be applied to fields other than clothing as well, making it possible to contribute comprehensively to the SDGs.

[0073] Generative AI can work with companies and local governments to propose specific measures to contribute to the SDGs. Generative AI can work with companies and local governments to propose specific measures to contribute to the SDGs. For example, generative AI can work with companies to propose specific measures to contribute to the SDGs. For example, it can support a company's waste reduction plan. Generative AI can also work with local governments to propose specific measures to contribute to the SDGs. For example, it can support a local recycling program. Generative AI can also work with companies and local governments to propose comprehensive measures to contribute to the SDGs. For example, it can propose a waste reduction project in cooperation between companies and local governments. This allows it to work with companies and local governments to propose specific SDG measures.

[0074] The generative AI can use its emotion estimation function to analyze how consumers feel about contributing to the SDGs and suggest ways to increase their motivation. The generative AI can use its emotion estimation function to analyze how consumers feel about contributing to the SDGs and make suggestions to increase their motivation. For example, the emotion estimation function can be used to analyze how consumers feel about contributing to the SDGs. For example, emotions can be estimated by analyzing the consumer's facial expressions and voice. The generative AI can also make suggestions to increase their motivation to contribute to the SDGs based on the consumer's emotion data. For example, it can suggest recycling methods that will bring joy and satisfaction to the consumer. The emotion estimation function can also be used to monitor consumers' emotional responses in real time and make suggestions to increase their motivation to contribute to the SDGs. For example, it can suggest recycling methods that will emotionally satisfy the consumer. This makes it possible to suggest SDG contributions that take into account the consumer's emotions.

[0075] Generative AI can educate consumers about the cycle of fashion trends and the importance of reuse and recycling using concrete examples. Generative AI educates consumers about the cycle of fashion trends and the importance of reuse and recycling using concrete examples. For example, generative AI can educate consumers about the cycle of fashion trends using concrete examples. For example, it can introduce examples of past trends becoming popular again. Generative AI can also educate consumers about the importance of reuse and recycling using concrete examples. For example, it can show the effect of recycling on reducing environmental impact. Generative AI can also educate consumers about the cycle of fashion trends and the importance of reuse and recycling using concrete examples. For example, it can introduce examples of successful remakes. This makes it possible to educate consumers using concrete examples.

[0076] Generative AI can provide personalized educational content according to the consumer's age and interests. Generative AI provides personalized educational content according to the consumer's age and interests. For example, generative AI provides personalized educational content according to the consumer's age. For example, it might suggest trend information for young people or recycling methods for the elderly. Generative AI also provides personalized educational content according to the consumer's interests. For example, it might provide information on specific fashion styles or how to remake clothes. Generative AI also provides personalized educational content that takes the consumer's age and interests into consideration. For example, it might suggest the most suitable educational content based on the consumer's profile data. This makes it possible to provide personalized educational content to consumers.

[0077] The generative AI can use its emotion estimation function to analyze the emotions consumers have toward educational content and propose educational methods that elicit positive emotions. The generative AI can use its emotion estimation function to analyze the emotions consumers have toward educational content and propose educational methods that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions consumers have toward educational content. For example, emotions can be estimated by analyzing the consumer's facial expressions and voice. The generative AI can also propose educational methods that elicit positive emotions based on the consumer's emotional data. For example, educational content that makes the consumer feel joyful and satisfied can be provided. The emotion estimation function can also be used to monitor the consumer's emotional reactions in real time and propose educational methods that elicit positive emotions. For example, educational content that satisfies the consumer emotionally can be provided. This makes it possible to propose educational methods that take into consideration the consumer's emotions.

[0078] Generative AI can provide educational programs for schools and companies, and conduct awareness-raising activities for a wide range of people. Generative AI can provide educational programs for schools and companies, and conduct awareness-raising activities for a wide range of people. For example, generative AI can provide educational programs for schools and companies, educating students about fashion trends and the importance of recycling. For example, it can provide teaching materials that can be used in class. Generative AI can also provide educational programs for companies, and educate employees about the importance of recycling. For example, it can be incorporated into corporate training programs. Generative AI can also simultaneously provide educational programs for schools and companies, and conduct awareness-raising activities for a wide range of people. For example, it can provide educational content that can be used online. This allows awareness-raising activities to be conducted for a wide range of people.

[0079] Generative AI can develop interactive applications for consumer education, allowing learning to take place in a game-like manner. Generative AI can develop interactive applications for consumer education, allowing learning to take place in a game-like manner. For example, generative AI can develop interactive applications for consumer education, allowing learning to take place in a game-like manner. For example, generative AI can provide a quiz game that teaches the importance of recycling. Generative AI can also develop interactive applications for consumer education, providing a simulation game that teaches the cycle of fashion trends. Generative AI can also develop interactive applications for consumer education, providing a practical game that teaches how to remake things. For example, a game can be provided in which virtual clothing can be remade. This makes it possible to provide interactive education that allows consumers to learn in a game-like manner.

[0080] The generative AI can use its emotion estimation function to analyze how consumers feel about educational content and suggest educational methods that will increase their motivation. The generative AI can use its emotion estimation function to analyze how consumers feel about educational content and make suggestions to increase their motivation. For example, the generative AI can use its emotion estimation function to analyze how consumers feel about educational content. For example, it can analyze the consumer's facial expressions and voice to estimate their emotions. The generative AI can also make suggestions to increase their motivation for educational content based on the consumer's emotion data. For example, it can provide educational methods that make consumers feel happy and satisfied. The generative AI can also use its emotion estimation function to monitor the consumer's emotional reactions in real time and make suggestions to increase their motivation for educational content. For example, it can provide educational methods that will emotionally satisfy the consumer. This makes it possible to suggest educational methods that take into consideration the consumer's emotions.

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

[0082] The clothing waste reduction system can also analyze consumers' purchasing history and suggest ways to reuse items they have previously purchased. For example, it can suggest ways to remake or recycle items based on data on items a consumer has previously purchased. Also, if a consumer has a preference for a particular brand or design, it can suggest ways to reuse items based on that brand or design. It can also analyze the condition of items a consumer has previously purchased and suggest the optimal way to reuse them. This makes it possible to make personalized reuse suggestions based on a consumer's purchasing history.

[0083] The clothing waste reduction system can also analyze consumers' lifestyle data and suggest reuse methods tailored to specific life events. For example, when a consumer is attending an event such as a wedding or party, it can suggest ways to remake previously purchased items into a new design. It can also suggest ways to reuse existing items to create a new style when a consumer goes on a trip. Furthermore, by suggesting reuse methods tailored to the consumer's lifestyle, it can increase consumers' motivation to reuse clothing rather than throw it away.

[0084] The clothing waste reduction system can also analyze consumer emotions and suggest reuse methods that elicit a positive emotional response. For example, it can suggest remake or recycling methods based on items that consumers have previously expressed positive emotions about. Also, if a consumer has an emotionally positive response to a particular item, it can suggest ways to reuse that item. Furthermore, it can analyze consumer emotional data in real time and suggest reuse methods that elicit a positive emotional response. This makes it possible to suggest reuse methods that take into account consumer emotions.

[0085] The clothing waste reduction system can also analyze the contents of a consumer's closet, identify items that are likely to become popular again, and encourage them to keep them. For example, the system can analyze the contents of a consumer's closet and create a list of items that are likely to become popular again. If a consumer has a preference for a particular brand or design, it can also identify items that are likely to become popular again based on that brand or design. It can also analyze the contents of a consumer's closet in real time, identify items that are likely to become popular again, and encourage them to keep them. This can prevent clothing waste by encouraging the storage of items that are likely to become popular again.

[0086] The clothing waste reduction system can also analyze consumer emotions and suggest recycling methods that elicit a positive emotional response. For example, it can suggest new recycling methods based on recycling methods that consumers have previously shown positive emotions about. Also, if a consumer has an emotionally positive response to a particular recycling method, it can suggest a new recycling method based on that method. Furthermore, it can analyze consumer emotional data in real time and suggest recycling methods that elicit a positive emotional response. This makes it possible to suggest recycling methods that take consumer emotions into consideration.

[0087] The clothing waste reduction system can also analyze a consumer's purchasing history and suggest recycling methods for items purchased in the past. For example, it can suggest recycling methods based on data on items a consumer has purchased in the past. Also, if a consumer has a preference for a particular brand or design, it can suggest recycling methods based on that brand or design. It can also analyze the condition of items a consumer has purchased in the past and suggest the optimal recycling method. This makes it possible to make personalized recycling suggestions based on a consumer's purchasing history.

[0088] The clothing waste reduction system can also analyze consumer emotions and suggest remake methods that elicit a positive emotional response. For example, it can suggest new remake methods based on remake methods that consumers have previously shown positive emotions about. Also, if a consumer has an emotionally positive response to a particular remake method, it can suggest a new remake method based on that method. Furthermore, it can analyze consumer emotional data in real time and suggest remake methods that elicit a positive emotional response. This makes it possible to suggest remake methods that take into account the consumer's emotions.

[0089] The clothing waste reduction system can also analyze consumer lifestyle data and suggest recycling methods tailored to specific life events. For example, when a consumer is attending an event such as a wedding or party, it can suggest ways to recycle previously purchased items into a new design. It can also suggest ways to recycle items that consumers already own to create a new style when they go on a trip. Furthermore, by suggesting recycling methods tailored to the consumer's lifestyle, it can increase the consumer's motivation to recycle clothing rather than throw it away.

[0090] The clothing waste reduction system can also analyze consumer emotions and suggest reuse methods that elicit a positive emotional response. For example, it can suggest new reuse methods based on reuse methods that consumers have previously shown positive emotions about. Also, if a consumer has an emotionally positive response to a particular reuse method, it can suggest a new reuse method based on that method. Furthermore, it can analyze consumer emotional data in real time and suggest reuse methods that elicit a positive emotional response. This makes it possible to suggest reuse methods that take into account consumer emotions.

[0091] The clothing waste reduction system can also analyze the contents of a consumer's closet, identify items that are likely to become popular again, and encourage them to keep them. For example, the system can analyze the contents of a consumer's closet and create a list of items that are likely to become popular again. If a consumer has a preference for a particular brand or design, it can also identify items that are likely to become popular again based on that brand or design. It can also analyze the contents of a consumer's closet in real time, identify items that are likely to become popular again, and encourage them to keep them. This can prevent clothing waste by encouraging the storage of items that are likely to become popular again.

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

[0093] Step 1: The trend prediction unit uses generative AI to learn and predict upcoming fashion trends. For example, generative AI can learn from past fashion trend data and predict upcoming trends. Generative AI can also analyze fashion magazines and social media posts from the past 10 years to predict which styles and items are likely to become popular again. Generative AI can also make trend predictions based on fashion trend data and prompts that instruct it to analyze that data. Step 2: The reuse promotion unit encourages consumers to reuse clothing based on the trend information predicted by the trend prediction unit. For example, if the generative AI predicts that "90s fashion will be popular again next year," it will advise consumers to store their 90s items rather than throw them away. The reuse promotion unit can also analyze information about the clothing that consumers own and suggest ways to reuse or recycle them. For example, the generative AI could suggest that "this jacket can be remade into a new design," thereby increasing consumers' motivation to reuse clothing rather than throw it away. Step 3: The recycling suggestion unit analyzes the information about the consumer's clothing provided by the reuse promotion unit and suggests ways to reuse or recycle. For example, the generative AI can analyze the condition of the consumer's clothing and suggest optimal ways to remake or recycle it. The generative AI can also learn from successful reuse and recycling cases and provide specific remaking methods to consumers.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following 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.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A trend forecasting department that uses generative AI to learn and predict upcoming fashion trends; a reuse promotion unit that encourages consumers to reuse clothing based on the trend information predicted by the trend prediction unit; and a recycling suggestion unit that analyzes information about the consumer's clothing encouraged by the reuse promotion unit and suggests methods of reuse and recycling. A system characterized by:

2. The trend prediction unit In addition to past fashion trend data, weather data and economic indicators are learned to improve the accuracy of trend forecasts.

2. The system of claim 1.

3. The reuse promotion unit Analyze the contents of consumers' closets, identify items that are coming back into fashion, and encourage them to keep them.

2. The system of claim 1.

4. The recycling proposal unit Analyzing the condition of consumers' clothing and suggesting optimal ways to remake or recycle them 2. The system of claim 1.

5. The generated AI is Analyze the effect of reducing environmental impact by reducing clothing waste and show it in concrete figures 2. The system of claim 1.

6. The trend prediction unit Analyze consumer sentiment and predict trends that elicit positive emotional responses 2. The system of claim 1.

7. The reuse promotion unit Analyzing consumers' emotions when discarding clothing and proposing recycling methods that elicit positive emotions 2. The system of claim 1.

8. The recycling proposal unit Analyzing consumers' feelings toward reuse and recycling and proposing ways to elicit positive emotions 2. The system of claim 1.

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