system

Abstract

The system according to this embodiment aims to streamline the user's closet management and to suggest new purchases, disposal, and recycling. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a waste proposal unit. The collection unit collects data input by the user. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes new purchase proposals based on the analysis results obtained by the analysis unit. The waste proposal unit makes waste or recycling proposals based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is troublesome to manage a user's closet, and no efficient proposal for new purchases or disposal / recycling has been made.

[0005] The system according to the embodiment aims to improve the efficiency of managing a user's closet and make proposals for new purchases or disposal / recycling.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a waste proposal unit. The data collection unit collects data entered by the user. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes new purchase proposals based on the analysis results obtained by the analysis unit. The waste proposal unit makes waste disposal or recycling proposals based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the user's closet management and provide suggestions for new purchases, disposal, and recycling. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The closet management system according to an embodiment of the present invention is a system that manages everything from "buying" to "disposing" of clothes in one place. The closet management system allows users to input photos of the clothes they wear each day, photos of their closet, and purchase data into an app. Next, AI analyzes this data to analyze the user's preferences, the amount of space occupied in the closet, the amount of clothing duplication, and purchase frequency. Based on the analysis results, the AI ​​makes suggestions for new purchases and suggestions for disposal or recycling. This mechanism provides users with the best possible purchase suggestions and has the effect of activating the eco-cycle of clothing resources through recycling. For example, users take photos of the clothes they wear each day and input them into the app. For example, they take a photo of their morning outfit and upload it to the app. They also take a rough photo of the contents of their closet and input it into the app. Furthermore, purchase history from e-commerce sites and online shopping sites is linked to the app. This allows the user's purchase data to be imported into the app. Next, AI analyzes this data. The AI ​​analyzes the user's coordination trends and preferences, frequency of appearance, closet usage rate, and the amount of clothing duplication. For example, the system can identify clothes that users frequently wear and duplicate items in their closets. It also measures closet usage to understand how much space is being occupied. Based on the analysis results, the AI ​​makes new purchase suggestions. For instance, it analyzes the user's preferences, brands, seasonal timing, and trends to recommend items from e-commerce and online shopping sites at the optimal time. Furthermore, it provides coupons and sale information to suggest the most advantageous time to purchase. The AI ​​also makes suggestions for disposal and recycling. For example, it analyzes the closet's fullness and suggests "getting rid of" clothes that haven't been worn in a while or are duplicates. It also creates draft listings for online auctions and flea markets to support users in easily listing items. It also suggests eco-friendly disposal methods for items that don't sell well, activating an eco-cycle of clothing resources through recycling. This system allows users to manage everything from purchasing to disposing of clothes in one place, enabling efficient closet management.Furthermore, AI-powered analysis and suggestions provide users with optimal purchase and recycling recommendations, revitalizing the eco-cycle of clothing resources and creating an environmentally friendly system. This allows the closet management system to centrally manage everything from the purchase to the disposal of a user's clothes, enabling efficient closet management.

[0029] The closet management system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a disposal suggestion unit. The collection unit collects data entered by the user. For example, the collection unit collects photos of the clothes the user wears every day, photos of the closet, and purchase data. For example, the collection unit can take a photo of the user's morning outfit and upload it to the app. The collection unit can also take a rough photo of the contents of the closet and input it into the app. Furthermore, the collection unit can link the purchase history from e-commerce sites and online shopping sites with the app. This allows the collection unit to import the user's purchase data into the app. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the user's outfit trends and preferences, frequency of appearance, closet usage rate, and the degree of clothing duplication. For example, the analysis unit can identify clothes that the user wears frequently and clothes that are duplicated in the closet. The analysis unit can also measure the closet usage rate and understand how much space is occupied. The suggestion unit makes new purchase suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit analyzes the user's preferences, brands, seasonal timing, and trends, and makes recommendations from e-commerce sites and online shopping sites at the optimal time. The suggestion unit can also provide coupons and sale information, suggesting the most advantageous purchase timing for the user. The disposal suggestion unit makes disposal and recycling suggestions based on the analysis results obtained by the analysis unit. For example, the disposal suggestion unit analyzes the fullness of the closet and suggests "getting rid of" clothes that haven't been worn for a while or duplicate clothes. The disposal suggestion unit can also create draft listings for online auctions and flea markets, supporting users in easily listing items. The disposal suggestion unit can also suggest eco-friendly disposal methods for items that are difficult to sell. In this way, the closet management system according to the embodiment can collect and analyze user data and make optimal purchase suggestions as well as suggestions for disposal and recycling.

[0030] The data collection unit collects data entered by the user. For example, the unit collects photos of the clothes the user wears each day, photos of their closet, and purchase data. Specifically, the unit collects data on the day's outfit when the user takes a photo of their morning outfit and uploads it to the app. The unit can also take a rough photo of the contents of the closet and input it into the app. This allows the user to get an overall picture of the items in their closet. Furthermore, the unit can link purchase history from e-commerce sites and online shopping sites with the app. This allows the unit to automatically import detailed information about items the user has purchased. This information may include purchase date and time, brand, price, size, and color. The unit centrally manages this data and can understand the state of the user's closet in real time. In addition to data manually entered by the user, the unit can also automatically collect data using smart devices and IoT technology. For example, it can use smart mirrors or smart hangers to automatically detect items the user takes out of the closet and send that information to the app. This reduces the user's effort and enables more accurate data collection.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the user's coordination trends and preferences, frequency of appearance, closet usage rate, and the degree of clothing duplication. Specifically, it can use AI to perform image analysis to identify clothes that the user frequently wears and clothes that are duplicated in the closet. For example, if a user tends to prefer certain colors or styles of clothing, the analysis unit can analyze this tendency to understand the user's preferences. It can also measure closet usage rate to understand how much space is occupied. This makes it clear which items the user uses frequently and which items are rarely used. Furthermore, based on past data and trend information, the analysis unit can also provide suggestions for future coordination and advice for optimizing the closet. For example, it can suggest appropriate item replacements or the addition of new items to match the change of seasons. The analysis unit provides personalized analysis results tailored to the user's lifestyle and preferences, supporting the user's closet management.

[0032] The Proposal Department makes new purchase suggestions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department analyzes user preferences, brands, seasonal timing, and trends, and makes recommendations from e-commerce sites and online shopping sites at the optimal time. Specifically, it suggests the latest trendy items and items that are in season based on the brands and styles that the user prefers. The Proposal Department can also provide coupons and sale information to suggest the most advantageous time to purchase for the user. For example, by providing sale information for specific brands or discount coupons based on purchase history, users can enjoy shopping more affordably. Furthermore, the Proposal Department can also consider the state of the user's wardrobe and suggest new items that are easy to combine with existing items. This allows users to avoid unnecessary purchases and efficiently enrich their wardrobe. The Proposal Department makes personalized suggestions that match the user's preferences and lifestyle, improving the user's shopping experience.

[0033] The waste disposal suggestion department makes suggestions for disposal and recycling based on the analysis results obtained by the analysis department. For example, the waste disposal suggestion department analyzes the fullness of the closet and suggests "getting rid of" clothes that haven't been worn for a while or duplicate clothes. Specifically, if a user has items that haven't been used for a certain period of time, or if there are multiple items of the same design or color, it will suggest getting rid of them. The waste disposal suggestion department can also create draft listings for online auctions and flea markets, supporting users in easily listing items. For example, it can automatically generate photos and descriptions of items, allowing users to list items without any effort. Furthermore, the waste disposal suggestion department can also suggest eco-friendly disposal methods for items that are difficult to sell. For example, it can provide information on recycling shops and donation destinations, allowing users to dispose of items in an environmentally conscious way. This allows users to efficiently manage their closet space and dispose of unwanted items appropriately. The waste disposal suggestion department makes suggestions that take into account the user's lifestyle and consideration for the environment, supporting closet management.

[0034] The data collection unit can collect photos of the clothes the user wears daily, photos of their closet, and purchase data. For example, the data collection unit can take photos of the clothes the user wears daily and input them into the app. For example, the data collection unit can take photos of the user's morning outfit and upload them to the app. The data collection unit can also take rough photos of the contents of the closet and input them into the app. Furthermore, the data collection unit can link purchase history from e-commerce sites and online shopping sites with the app. This allows the data collection unit to import the user's purchase data into the app. By collecting photos of the user's clothes, photos of their closet, and purchase data, detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input photos of the clothes the user wears daily into a generating AI and have the generating AI perform photo analysis.

[0035] The analysis unit can analyze the user's coordination trends and preferences, frequency of appearance, closet usage rate, and clothing duplication. For example, the analysis unit can analyze the user's coordination trends and preferences. For example, the analysis unit can identify clothes that the user frequently wears and clothes that are duplicated in the closet. The analysis unit can also measure the closet usage rate and understand how much space is occupied. By analyzing the user's coordination trends and preferences, frequency of appearance, closet usage rate, and clothing duplication, detailed analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's coordination trends and preferences into a generating AI and have the generating AI perform the analysis.

[0036] The recommendation unit can analyze user preferences, brands, seasonal timing, and trends, and make recommendations from e-commerce sites and online shopping sites at the optimal time. For example, the recommendation unit can analyze user preferences and brands. For example, the recommendation unit can analyze seasonal timing and trends, and make recommendations at the optimal time. By analyzing user preferences, brands, seasonal timing, and trends and making recommendations at the optimal time, the recommendation unit can provide users with optimal purchase suggestions. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit can input user preferences and brands into a generative AI and have the generative AI perform the recommendations.

[0037] The suggestion unit can provide coupons and sales information and suggest the most advantageous time for the user to make a purchase. For example, the suggestion unit can provide coupons and sales information. The suggestion unit can suggest the most advantageous time for the user to make a purchase. This improves the user's purchasing experience by providing coupons and sales information and suggesting the most advantageous time for the user to make a purchase. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input coupons and sales information into a generating AI and have the generating AI generate a suggestion for the optimal time to make a purchase.

[0038] The waste suggestion unit can analyze the fullness of the closet and suggest "getting rid of" clothes that haven't been worn for a while or are duplicates. For example, the waste suggestion unit can analyze the fullness of the closet. For example, the waste suggestion unit can suggest "getting rid of" clothes that haven't been worn for a while or are duplicates. This allows for efficient use of the closet by analyzing the fullness of the closet and suggesting "getting rid of" clothes that haven't been worn for a while or are duplicates. Some or all of the above processing in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input the fullness of the closet into a generating AI and have the generating AI execute suggestions for clothes that haven't been worn for a while or are duplicates.

[0039] The waste suggestion unit can create draft listings for online auctions and flea markets, and support users in easily listing items. For example, the waste suggestion unit creates draft listings for online auctions and flea markets. The waste suggestion unit can support users in easily listing items. This promotes the recycling of unwanted clothing by creating draft listings for online auctions and flea markets and supporting users in easily listing items. Some or all of the above processes in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input draft listings for online auctions and flea markets into a generation AI and have the generation AI perform listing support.

[0040] The waste proposal department can propose eco-friendly disposal methods for products that are difficult to sell. For example, the waste proposal department can propose eco-friendly disposal methods for products that are difficult to sell. For example, the waste proposal department can propose recycling methods and methods to reduce environmental impact. By proposing eco-friendly disposal methods for products that are difficult to sell, environmentally friendly disposal becomes possible. Some or all of the above processes in the waste proposal department may be performed using AI, for example, or without AI. For example, the waste proposal department can input eco-friendly disposal methods into a generation AI and have the generation AI execute the proposal of disposal methods.

[0041] The data collection unit can analyze the user's past collected data and select the optimal data collection method. For example, the data collection unit can prioritize suggesting data collection methods (photos, text, etc.) that the user has frequently used in the past. For example, the data collection unit can predict and suggest the most efficient data collection timing based on the user's past collected data. For example, the data collection unit can analyze the user's past collected data and select the optimal data collection method for a specific time period. In this way, the optimal data collection method can be selected by analyzing the user's past collected data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collected data into a generating AI and have the generating AI select the optimal data collection method.

[0042] The data collection unit can filter the collection of clothing photos and purchase data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of clothing from specific brands or styles based on the user's current lifestyle. For example, the data collection unit can filter and collect relevant purchase data based on the user's areas of interest. For example, the data collection unit can adjust the scope of data to be collected based on the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information, when collecting photos of clothing and purchase data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can determine the optimal collection timing based on the user's geographical location information. For example, the data collection unit can filter and collect highly relevant data, taking into account the user's geographical location information. This allows for the acquisition of more relevant data by collecting data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform data filtering.

[0044] The data collection unit can analyze the user's social media activity and collect relevant data when collecting photos of clothing and purchase data. For example, the data collection unit can collect data on brands and styles of interest from the user's social media activity. For example, the data collection unit can analyze the user's social media activity and collect relevant purchase data. For example, the data collection unit can adjust the scope of data to be collected based on the user's social media activity. This allows relevant data to be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the data collection.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for a more detailed analysis of more important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a fashion analysis algorithm to coordination data. For example, the analysis unit can apply a purchasing behavior analysis algorithm to purchasing data. For example, the analysis unit can apply a spatial analysis algorithm to closet usage rate data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can adjust the analysis schedule based on the data submission date. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0049] The proposal unit can adjust the level of detail of its proposals based on the importance of the products. For example, the proposal unit can provide detailed proposals for important products. For example, it can provide concise proposals for less important products. The proposal unit can also determine the priority of proposals based on the importance of the products. This allows for more detailed proposals to be provided for more important products by adjusting the level of detail of the proposals based on the importance of the products. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the products into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0050] The suggestion unit can apply different suggestion algorithms depending on the product category. For example, the suggestion unit can apply a fashion suggestion algorithm to fashion items. For example, the suggestion unit can apply a home appliance suggestion algorithm to home appliances. For example, the suggestion unit can apply a food suggestion algorithm to food products. By applying different suggestion algorithms depending on the product category, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0051] The proposal department can determine the priority of proposals based on the product submission date. For example, the proposal department can prioritize proposals for the newest products. For example, the proposal department can postpone proposals for older products. The proposal department can adjust the proposal schedule based on the product submission date. This allows for prioritizing proposals based on the product submission date, thereby ensuring that the newest products are proposed first. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the product submission dates into a generating AI and have the generating AI determine the proposal priority.

[0052] The suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit can prioritize suggesting highly relevant products. For example, the suggestion unit can postpone suggesting less relevant products. The suggestion unit can adjust the order of suggestions based on the relevance of the products. By adjusting the order of suggestions based on the relevance of the products, highly relevant products can be suggested preferentially. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0053] The waste suggestion unit can analyze the user's past consumption behavior and select the optimal suggestion method. For example, the waste suggestion unit can suggest the optimal disposal method based on items the user has frequently discarded in the past. For example, the waste suggestion unit can analyze the user's past recycling behavior and suggest the most effective recycling method. For example, the waste suggestion unit can analyze the user's consumption behavior patterns and suggest the optimal timing for disposal or recycling. In this way, by analyzing the user's past consumption behavior, the optimal disposal or recycling suggestion method can be selected. Some or all of the above processes in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal suggestion method.

[0054] The waste disposal suggestion unit can customize its suggestions based on the user's current living situation. For example, if the user is planning to move, the waste disposal suggestion unit can suggest disposal or recycling options before the move. For example, if the user has started a new job, the waste disposal suggestion unit can suggest disposal or recycling options that suit their new lifestyle. The waste disposal suggestion unit can customize the optimal disposal or recycling options based on the user's living situation. This allows the unit to provide more appropriate disposal and recycling suggestions by customizing the suggestions based on the user's current living situation. Some or all of the above-described processes in the waste disposal suggestion unit may be performed using AI, for example, or without AI. For example, the waste disposal suggestion unit can input user living situation data into a generating AI and have the generating AI perform the customization of the suggestion options.

[0055] The waste proposal unit can select the optimal proposal method considering the user's geographical location information. For example, if the user is in a specific region, the waste proposal unit will propose waste or recycling methods relevant to that region. For example, the waste proposal unit can determine the optimal timing for waste disposal or recycling based on the user's geographical location information. For example, the waste proposal unit can filter and propose highly relevant waste or recycling methods considering the user's geographical location information. This allows for the provision of more relevant proposals by selecting the optimal proposal method considering the user's geographical location information. Some or all of the above processing in the waste proposal unit may be performed using AI, for example, or without AI. For example, the waste proposal unit can input the user's geographical location information into a generating AI and have the generating AI select the proposal method.

[0056] The waste suggestion unit can analyze a user's social media activity and propose methods for disposal. For example, the waste suggestion unit can suggest recycling methods that the user is interested in based on their social media activity. For example, the waste suggestion unit can analyze a user's social media activity and propose relevant disposal methods. For example, the waste suggestion unit can customize the proposed disposal and recycling methods based on the user's social media activity. This allows for the provision of more appropriate disposal and recycling suggestions by analyzing the user's social media activity. Some or all of the above processes in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input user social media activity data into a generating AI and have the generating AI select the proposed methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The closet management system can also collect and analyze user health data. For example, it can collect data such as the user's steps, heart rate, and sleep patterns, and use this data to understand the user's activity level and health condition. The analysis unit can then use this health data to suggest clothing that matches the user's activity level. For instance, it can suggest comfortable clothing for active days and comfortable clothing for relaxed days. It can also suggest appropriate clothing materials and designs based on the user's health condition. This makes it possible to suggest the optimal clothing for each user based on their health status.

[0059] The closet management system can further analyze users' social media activity and suggest relevant clothing items. For example, it can analyze the styles of fashion influencers users follow on social media and suggest similar styles. It can also suggest related clothing items based on the designs and brands of clothing that users have liked or commented on on social media. Furthermore, it can suggest clothing suitable for events and parties that users are planning to attend. This enables the system to provide optimal clothing suggestions based on the user's social media activity.

[0060] The closet management system can also suggest clothing based on the user's geographical location. For example, it can suggest clothing suitable for the climate and culture of the user's travel destination. If the user is in a specific region, it can suggest clothing appropriate for the weather and temperature of that region. It can also suggest clothing suitable for a specific event or festival the user is attending. This enables the system to suggest the most suitable clothing based on the user's geographical location.

[0061] The closet management system can further analyze users' past purchase data and predict future purchasing trends. For example, it can analyze the brands, designs, and colors of clothes the user has purchased in the past to predict future purchasing trends. Based on this data, the analysis unit can suggest new clothes that the user is likely to like. It can also consider the frequency of use and satisfaction level of clothes the user has purchased in the past to suggest clothes that will be more satisfying. This makes it possible to suggest optimal clothes based on the user's past purchase data.

[0062] The closet management system can further suggest clothing based on the user's lifestyle. For example, if the user has an active lifestyle, it can suggest sportswear or casual clothing. If the user is a business person, it can suggest formal wear or business casual attire. Furthermore, if the user spends a lot of time at home, it can suggest relaxing clothes or loungewear. This allows for optimal clothing suggestions tailored to the user's lifestyle.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects data entered by the user. For example, it collects photos of the clothes the user wears each day, photos of their closet, and purchase data. Users can take photos of their morning outfits and upload them to the app, or take photos of the contents of their closet and input them into the app. It is also possible to link purchase history from e-commerce sites and online shopping sites with the app. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the user's styling trends and preferences, frequency of appearance, closet usage rate, and the degree of clothing duplication. It identifies the clothes that the user frequently wears and the clothes that are duplicated in the closet, and measures the closet usage rate. Step 3: The Proposal Department makes new purchase suggestions based on the analysis results obtained by the Analysis Department. For example, it analyzes user preferences, brands, seasonal timing, and trends, and makes recommendations from e-commerce sites and online shopping sites at the optimal time. Furthermore, it provides coupons and sale information to suggest the most advantageous purchase timing for the user. Step 4: The waste proposal department makes suggestions for disposal and recycling based on the analysis results obtained by the analysis department. For example, it analyzes the fullness of the closet and suggests "getting rid of" clothes that haven't been worn for a while or duplicate clothes. Furthermore, it creates draft listings for online auctions and flea markets to support users in easily listing items. It also suggests eco-friendly disposal methods for items that are difficult to sell.

[0065] (Example of form 2) The closet management system according to an embodiment of the present invention is a system that manages everything from "buying" to "disposing" of clothes in one place. The closet management system allows users to input photos of the clothes they wear each day, photos of their closet, and purchase data into an app. Next, AI analyzes this data to analyze the user's preferences, the amount of space occupied in the closet, the amount of clothing duplication, and purchase frequency. Based on the analysis results, the AI ​​makes suggestions for new purchases and suggestions for disposal or recycling. This mechanism provides users with the best possible purchase suggestions and has the effect of activating the eco-cycle of clothing resources through recycling. For example, users take photos of the clothes they wear each day and input them into the app. For example, they take a photo of their morning outfit and upload it to the app. They also take a rough photo of the contents of their closet and input it into the app. Furthermore, purchase history from e-commerce sites and online shopping sites is linked to the app. This allows the user's purchase data to be imported into the app. Next, AI analyzes this data. The AI ​​analyzes the user's coordination trends and preferences, frequency of appearance, closet usage rate, and the amount of clothing duplication. For example, the system can identify clothes that users frequently wear and duplicate items in their closets. It also measures closet usage to understand how much space is being occupied. Based on the analysis results, the AI ​​makes new purchase suggestions. For instance, it analyzes the user's preferences, brands, seasonal timing, and trends to recommend items from e-commerce and online shopping sites at the optimal time. Furthermore, it provides coupons and sale information to suggest the most advantageous time to purchase. The AI ​​also makes suggestions for disposal and recycling. For example, it analyzes the closet's fullness and suggests "getting rid of" clothes that haven't been worn in a while or are duplicates. It also creates draft listings for online auctions and flea markets to support users in easily listing items. It also suggests eco-friendly disposal methods for items that don't sell well, activating an eco-cycle of clothing resources through recycling. This system allows users to manage everything from purchasing to disposing of clothes in one place, enabling efficient closet management.Furthermore, AI-powered analysis and suggestions provide users with optimal purchase and recycling recommendations, revitalizing the eco-cycle of clothing resources and creating an environmentally friendly system. This allows the closet management system to centrally manage everything from the purchase to the disposal of a user's clothes, enabling efficient closet management.

[0066] The closet management system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a disposal suggestion unit. The collection unit collects data entered by the user. For example, the collection unit collects photos of the clothes the user wears every day, photos of the closet, and purchase data. For example, the collection unit can take a photo of the user's morning outfit and upload it to the app. The collection unit can also take a rough photo of the contents of the closet and input it into the app. Furthermore, the collection unit can link the purchase history from e-commerce sites and online shopping sites with the app. This allows the collection unit to import the user's purchase data into the app. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the user's outfit trends and preferences, frequency of appearance, closet usage rate, and the degree of clothing duplication. For example, the analysis unit can identify clothes that the user wears frequently and clothes that are duplicated in the closet. The analysis unit can also measure the closet usage rate and understand how much space is occupied. The suggestion unit makes new purchase suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit analyzes the user's preferences, brands, seasonal timing, and trends, and makes recommendations from e-commerce sites and online shopping sites at the optimal time. The suggestion unit can also provide coupons and sale information, suggesting the most advantageous purchase timing for the user. The disposal suggestion unit makes disposal and recycling suggestions based on the analysis results obtained by the analysis unit. For example, the disposal suggestion unit analyzes the fullness of the closet and suggests "getting rid of" clothes that haven't been worn for a while or duplicate clothes. The disposal suggestion unit can also create draft listings for online auctions and flea markets, supporting users in easily listing items. The disposal suggestion unit can also suggest eco-friendly disposal methods for items that are difficult to sell. In this way, the closet management system according to the embodiment can collect and analyze user data and make optimal purchase suggestions as well as suggestions for disposal and recycling.

[0067] The data collection unit collects data entered by the user. For example, the unit collects photos of the clothes the user wears each day, photos of their closet, and purchase data. Specifically, the unit collects data on the day's outfit when the user takes a photo of their morning outfit and uploads it to the app. The unit can also take a rough photo of the contents of the closet and input it into the app. This allows the user to get an overall picture of the items in their closet. Furthermore, the unit can link purchase history from e-commerce sites and online shopping sites with the app. This allows the unit to automatically import detailed information about items the user has purchased. This information may include purchase date and time, brand, price, size, and color. The unit centrally manages this data and can understand the state of the user's closet in real time. In addition to data manually entered by the user, the unit can also automatically collect data using smart devices and IoT technology. For example, it can use smart mirrors or smart hangers to automatically detect items the user takes out of the closet and send that information to the app. This reduces the user's effort and enables more accurate data collection.

[0068] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the user's coordination trends and preferences, frequency of appearance, closet usage rate, and the degree of clothing duplication. Specifically, it can use AI to perform image analysis to identify clothes that the user frequently wears and clothes that are duplicated in the closet. For example, if a user tends to prefer certain colors or styles of clothing, the analysis unit can analyze this tendency to understand the user's preferences. It can also measure closet usage rate to understand how much space is occupied. This makes it clear which items the user uses frequently and which items are rarely used. Furthermore, based on past data and trend information, the analysis unit can also provide suggestions for future coordination and advice for optimizing the closet. For example, it can suggest appropriate item replacements or the addition of new items to match the change of seasons. The analysis unit provides personalized analysis results tailored to the user's lifestyle and preferences, supporting the user's closet management.

[0069] The Proposal Department makes new purchase suggestions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department analyzes user preferences, brands, seasonal timing, and trends, and makes recommendations from e-commerce sites and online shopping sites at the optimal time. Specifically, it suggests the latest trendy items and items that are in season based on the brands and styles that the user prefers. The Proposal Department can also provide coupons and sale information to suggest the most advantageous time to purchase for the user. For example, by providing sale information for specific brands or discount coupons based on purchase history, users can enjoy shopping more affordably. Furthermore, the Proposal Department can also consider the state of the user's wardrobe and suggest new items that are easy to combine with existing items. This allows users to avoid unnecessary purchases and efficiently enrich their wardrobe. The Proposal Department makes personalized suggestions that match the user's preferences and lifestyle, improving the user's shopping experience.

[0070] The waste disposal suggestion department makes suggestions for disposal and recycling based on the analysis results obtained by the analysis department. For example, the waste disposal suggestion department analyzes the fullness of the closet and suggests "getting rid of" clothes that haven't been worn for a while or duplicate clothes. Specifically, if a user has items that haven't been used for a certain period of time, or if there are multiple items of the same design or color, it will suggest getting rid of them. The waste disposal suggestion department can also create draft listings for online auctions and flea markets, supporting users in easily listing items. For example, it can automatically generate photos and descriptions of items, allowing users to list items without any effort. Furthermore, the waste disposal suggestion department can also suggest eco-friendly disposal methods for items that are difficult to sell. For example, it can provide information on recycling shops and donation destinations, allowing users to dispose of items in an environmentally conscious way. This allows users to efficiently manage their closet space and dispose of unwanted items appropriately. The waste disposal suggestion department makes suggestions that take into account the user's lifestyle and consideration for the environment, supporting closet management.

[0071] The data collection unit can collect photos of the clothes the user wears daily, photos of their closet, and purchase data. For example, the data collection unit can take photos of the clothes the user wears daily and input them into the app. For example, the data collection unit can take photos of the user's morning outfit and upload them to the app. The data collection unit can also take rough photos of the contents of the closet and input them into the app. Furthermore, the data collection unit can link purchase history from e-commerce sites and online shopping sites with the app. This allows the data collection unit to import the user's purchase data into the app. By collecting photos of the user's clothes, photos of their closet, and purchase data, detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input photos of the clothes the user wears daily into a generating AI and have the generating AI perform photo analysis.

[0072] The analysis unit can analyze the user's coordination trends and preferences, frequency of appearance, closet usage rate, and clothing duplication. For example, the analysis unit can analyze the user's coordination trends and preferences. For example, the analysis unit can identify clothes that the user frequently wears and clothes that are duplicated in the closet. The analysis unit can also measure the closet usage rate and understand how much space is occupied. By analyzing the user's coordination trends and preferences, frequency of appearance, closet usage rate, and clothing duplication, detailed analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's coordination trends and preferences into a generating AI and have the generating AI perform the analysis.

[0073] The recommendation unit can analyze user preferences, brands, seasonal timing, and trends, and make recommendations from e-commerce sites and online shopping sites at the optimal time. For example, the recommendation unit can analyze user preferences and brands. For example, the recommendation unit can analyze seasonal timing and trends, and make recommendations at the optimal time. By analyzing user preferences, brands, seasonal timing, and trends and making recommendations at the optimal time, the recommendation unit can provide users with optimal purchase suggestions. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit can input user preferences and brands into a generative AI and have the generative AI perform the recommendations.

[0074] The suggestion unit can provide coupons and sales information and suggest the most advantageous time for the user to make a purchase. For example, the suggestion unit can provide coupons and sales information. The suggestion unit can suggest the most advantageous time for the user to make a purchase. This improves the user's purchasing experience by providing coupons and sales information and suggesting the most advantageous time for the user to make a purchase. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input coupons and sales information into a generating AI and have the generating AI generate a suggestion for the optimal time to make a purchase.

[0075] The waste suggestion unit can analyze the fullness of the closet and suggest "getting rid of" clothes that haven't been worn for a while or are duplicates. For example, the waste suggestion unit can analyze the fullness of the closet. For example, the waste suggestion unit can suggest "getting rid of" clothes that haven't been worn for a while or are duplicates. This allows for efficient use of the closet by analyzing the fullness of the closet and suggesting "getting rid of" clothes that haven't been worn for a while or are duplicates. Some or all of the above processing in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input the fullness of the closet into a generating AI and have the generating AI execute suggestions for clothes that haven't been worn for a while or are duplicates.

[0076] The waste suggestion unit can create draft listings for online auctions and flea markets, and support users in easily listing items. For example, the waste suggestion unit creates draft listings for online auctions and flea markets. The waste suggestion unit can support users in easily listing items. This promotes the recycling of unwanted clothing by creating draft listings for online auctions and flea markets and supporting users in easily listing items. Some or all of the above processes in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input draft listings for online auctions and flea markets into a generation AI and have the generation AI perform listing support.

[0077] The waste proposal department can propose eco-friendly disposal methods for products that are difficult to sell. For example, the waste proposal department can propose eco-friendly disposal methods for products that are difficult to sell. For example, the waste proposal department can propose recycling methods and methods to reduce environmental impact. By proposing eco-friendly disposal methods for products that are difficult to sell, environmentally friendly disposal becomes possible. Some or all of the above processes in the waste proposal department may be performed using AI, for example, or without AI. For example, the waste proposal department can input eco-friendly disposal methods into a generation AI and have the generation AI execute the proposal of disposal methods.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection for clothing photos and purchase data based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. For example, if the user is excited, the data collection unit can collect data immediately to obtain purchase data from when emotions are heightened. For example, if the user is tired, the data collection unit can adjust the collection timing to collect data after the user has rested. This allows for more appropriate data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0079] The data collection unit can analyze the user's past collected data and select the optimal data collection method. For example, the data collection unit can prioritize suggesting data collection methods (photos, text, etc.) that the user has frequently used in the past. For example, the data collection unit can predict and suggest the most efficient data collection timing based on the user's past collected data. For example, the data collection unit can analyze the user's past collected data and select the optimal data collection method for a specific time period. In this way, the optimal data collection method can be selected by analyzing the user's past collected data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collected data into a generating AI and have the generating AI select the optimal data collection method.

[0080] The data collection unit can filter the collection of clothing photos and purchase data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of clothing from specific brands or styles based on the user's current lifestyle. For example, the data collection unit can filter and collect relevant purchase data based on the user's areas of interest. For example, the data collection unit can adjust the scope of data to be collected based on the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit may prioritize collecting important data. For example, if the user is excited, the data collection unit may prioritize collecting emotion-related data. This allows for the priority collection of more important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0082] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information, when collecting photos of clothing and purchase data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can determine the optimal collection timing based on the user's geographical location information. For example, the data collection unit can filter and collect highly relevant data, taking into account the user's geographical location information. This allows for the acquisition of more relevant data by collecting data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can analyze the user's social media activity and collect relevant data when collecting photos of clothing and purchase data. For example, the data collection unit can collect data on brands and styles of interest from the user's social media activity. For example, the data collection unit can analyze the user's social media activity and collect relevant purchase data. For example, the data collection unit can adjust the scope of data to be collected based on the user's social media activity. This allows relevant data to be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the data collection.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for a more detailed analysis of more important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a fashion analysis algorithm to coordination data. For example, the analysis unit can apply a purchasing behavior analysis algorithm to purchasing data. For example, the analysis unit can apply a spatial analysis algorithm to closet usage rate data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can adjust the analysis schedule based on the data submission date. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0091] The proposal unit can adjust the level of detail of its proposals based on the importance of the products. For example, the proposal unit can provide detailed proposals for important products. For example, it can provide concise proposals for less important products. The proposal unit can also determine the priority of proposals based on the importance of the products. This allows for more detailed proposals to be provided for more important products by adjusting the level of detail of the proposals based on the importance of the products. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the products into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0092] The suggestion unit can apply different suggestion algorithms depending on the product category. For example, the suggestion unit can apply a fashion suggestion algorithm to fashion items. For example, the suggestion unit can apply a home appliance suggestion algorithm to home appliances. For example, the suggestion unit can apply a food suggestion algorithm to food products. By applying different suggestion algorithms depending on the product category, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0094] The proposal department can determine the priority of proposals based on the product submission date. For example, the proposal department can prioritize proposals for the newest products. For example, the proposal department can postpone proposals for older products. The proposal department can adjust the proposal schedule based on the product submission date. This allows for prioritizing proposals based on the product submission date, thereby ensuring that the newest products are proposed first. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the product submission dates into a generating AI and have the generating AI determine the proposal priority.

[0095] The suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit can prioritize suggesting highly relevant products. For example, the suggestion unit can postpone suggesting less relevant products. The suggestion unit can adjust the order of suggestions based on the relevance of the products. By adjusting the order of suggestions based on the relevance of the products, highly relevant products can be suggested preferentially. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0096] The waste suggestion unit can estimate the user's emotions and adjust the waste or recycling suggestion method based on the estimated emotions. For example, if the user is relaxed, the waste suggestion unit can provide detailed waste or recycling suggestions. If the user is in a hurry, for example, the waste suggestion unit can provide concise waste or recycling suggestions that get straight to the point. If the user is excited, for example, the waste suggestion unit can provide visually stimulating waste or recycling suggestions. This allows for more appropriate suggestions to be provided by adjusting the waste or recycling suggestion method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the waste suggestion unit may be performed using AI or not. For example, the waste suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the waste or recycling suggestion method.

[0097] The waste suggestion unit can analyze the user's past consumption behavior and select the optimal suggestion method. For example, the waste suggestion unit can suggest the optimal disposal method based on items the user has frequently discarded in the past. For example, the waste suggestion unit can analyze the user's past recycling behavior and suggest the most effective recycling method. For example, the waste suggestion unit can analyze the user's consumption behavior patterns and suggest the optimal timing for disposal or recycling. In this way, by analyzing the user's past consumption behavior, the optimal disposal or recycling suggestion method can be selected. Some or all of the above processes in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal suggestion method.

[0098] The waste disposal suggestion unit can customize its suggestions based on the user's current living situation. For example, if the user is planning to move, the waste disposal suggestion unit can suggest disposal or recycling options before the move. For example, if the user has started a new job, the waste disposal suggestion unit can suggest disposal or recycling options that suit their new lifestyle. The waste disposal suggestion unit can customize the optimal disposal or recycling options based on the user's living situation. This allows the unit to provide more appropriate disposal and recycling suggestions by customizing the suggestions based on the user's current living situation. Some or all of the above-described processes in the waste disposal suggestion unit may be performed using AI, for example, or without AI. For example, the waste disposal suggestion unit can input user living situation data into a generating AI and have the generating AI perform the customization of the suggestion options.

[0099] The waste suggestion unit can estimate the user's emotions and determine the priority of waste disposal and recycling based on the estimated emotions. For example, if the user is relaxed, the waste suggestion unit can provide detailed waste disposal and recycling priorities. For example, if the user is in a hurry, the waste suggestion unit can provide concise waste disposal and recycling priorities that get straight to the point. For example, if the user is excited, the waste suggestion unit can provide visually stimulating waste disposal and recycling priorities. This allows for more appropriate priorities to be provided by determining waste disposal and recycling priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the waste suggestion unit may be performed using AI or not using AI. For example, the waste suggestion unit can input user emotion data into a generative AI and have the generative AI perform the determination of waste disposal and recycling priorities.

[0100] The waste proposal unit can select the optimal proposal method considering the user's geographical location information. For example, if the user is in a specific region, the waste proposal unit will propose waste or recycling methods relevant to that region. For example, the waste proposal unit can determine the optimal timing for waste disposal or recycling based on the user's geographical location information. For example, the waste proposal unit can filter and propose highly relevant waste or recycling methods considering the user's geographical location information. This allows for the provision of more relevant proposals by selecting the optimal proposal method considering the user's geographical location information. Some or all of the above processing in the waste proposal unit may be performed using AI, for example, or without AI. For example, the waste proposal unit can input the user's geographical location information into a generating AI and have the generating AI select the proposal method.

[0101] The waste suggestion unit can analyze a user's social media activity and propose methods for disposal. For example, the waste suggestion unit can suggest recycling methods that the user is interested in based on their social media activity. For example, the waste suggestion unit can analyze a user's social media activity and propose relevant disposal methods. For example, the waste suggestion unit can customize the proposed disposal and recycling methods based on the user's social media activity. This allows for the provision of more appropriate disposal and recycling suggestions by analyzing the user's social media activity. Some or all of the above processes in the waste suggestion unit may be performed using AI, for example, or without AI. For example, the waste suggestion unit can input user social media activity data into a generating AI and have the generating AI select the proposed methods.

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

[0103] The closet management system can also collect and analyze user health data. For example, it can collect data such as the user's steps, heart rate, and sleep patterns, and use this data to understand the user's activity level and health condition. The analysis unit can then use this health data to suggest clothing that matches the user's activity level. For instance, it can suggest comfortable clothing for active days and comfortable clothing for relaxed days. It can also suggest appropriate clothing materials and designs based on the user's health condition. This makes it possible to suggest the optimal clothing for each user based on their health status.

[0104] The closet management system can further estimate the user's emotions and suggest clothing based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing clothes. If the user is excited, it can suggest clothes with energetic designs. And if the user is sad, it can suggest bright-colored clothes to lift their mood. This makes it possible to suggest the most suitable clothing according to the user's emotions.

[0105] The closet management system can further analyze users' social media activity and suggest relevant clothing items. For example, it can analyze the styles of fashion influencers users follow on social media and suggest similar styles. It can also suggest related clothing items based on the designs and brands of clothing that users have liked or commented on on social media. Furthermore, it can suggest clothing suitable for events and parties that users are planning to attend. This enables the system to provide optimal clothing suggestions based on the user's social media activity.

[0106] The closet management system can also suggest clothing based on the user's geographical location. For example, it can suggest clothing suitable for the climate and culture of the user's travel destination. If the user is in a specific region, it can suggest clothing appropriate for the weather and temperature of that region. It can also suggest clothing suitable for a specific event or festival the user is attending. This enables the system to suggest the most suitable clothing based on the user's geographical location.

[0107] The closet management system can further analyze users' past purchase data and predict future purchasing trends. For example, it can analyze the brands, designs, and colors of clothes the user has purchased in the past to predict future purchasing trends. Based on this data, the analysis unit can suggest new clothes that the user is likely to like. It can also consider the frequency of use and satisfaction level of clothes the user has purchased in the past to suggest clothes that will be more satisfying. This makes it possible to suggest optimal clothes based on the user's past purchase data.

[0108] The closet management system can further estimate the user's emotions and make discard or recycling suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest discarding unnecessary clothes to create a more relaxing environment. If the user is excited, it can suggest recycling unnecessary clothes to maintain an energetic mood. If the user is sad, it can suggest decluttering unnecessary clothes to lift their spirits. This allows for optimal discard and recycling suggestions tailored to the user's emotions.

[0109] The closet management system can further suggest clothing based on the user's lifestyle. For example, if the user has an active lifestyle, it can suggest sportswear or casual clothing. If the user is a business person, it can suggest formal wear or business casual attire. Furthermore, if the user spends a lot of time at home, it can suggest relaxing clothes or loungewear. This allows for optimal clothing suggestions tailored to the user's lifestyle.

[0110] The closet management system can further estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. If the user is excited, it can provide visually stimulating suggestions. In this way, by adjusting the way suggestions are presented based on the user's emotions, it can provide more appropriate suggestions.

[0111] The closet management system can further estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. If the user is excited, it can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it can provide more appropriate analysis results.

[0112] The closet management system can further estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is relaxed, detailed data can be prioritized. If the user is in a hurry, important data can be prioritized. Also, if the user is excited, emotion-related data can be prioritized. This allows for the collection of more important data by prioritizing data based on the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects data entered by the user. For example, it collects photos of the clothes the user wears each day, photos of their closet, and purchase data. Users can take photos of their morning outfits and upload them to the app, or take photos of the contents of their closet and input them into the app. It is also possible to link purchase history from e-commerce sites and online shopping sites with the app. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the user's styling trends and preferences, frequency of appearance, closet usage rate, and the degree of clothing duplication. It identifies the clothes that the user frequently wears and the clothes that are duplicated in the closet, and measures the closet usage rate. Step 3: The Proposal Department makes new purchase suggestions based on the analysis results obtained by the Analysis Department. For example, it analyzes user preferences, brands, seasonal timing, and trends, and makes recommendations from e-commerce sites and online shopping sites at the optimal time. Furthermore, it provides coupons and sale information to suggest the most advantageous purchase timing for the user. Step 4: The waste proposal department makes suggestions for disposal and recycling based on the analysis results obtained by the analysis department. For example, it analyzes the fullness of the closet and suggests "getting rid of" clothes that haven't been worn for a while or duplicate clothes. Furthermore, it creates draft listings for online auctions and flea markets to support users in easily listing items. It also suggests eco-friendly disposal methods for items that are difficult to sell.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and disposal proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the smart device 14 to collect photos of the clothes the user wears daily, photos of the closet, and purchase data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes new purchase suggestions based on the analysis results. The disposal proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for disposal or recycling. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and disposal proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the smart glasses 214 to collect photos of the clothes the user wears daily, photos of the closet, and purchase data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes new purchase suggestions based on the analysis results. The disposal proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for disposal or recycling. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and disposal proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the headset terminal 314 to collect photos of the clothes the user wears daily, photos of the closet, and purchase data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes new purchase suggestions based on the analysis results. The disposal proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for disposal or recycling. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and disposal proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the robot 414 to collect photos of the clothes the user wears daily, photos of the closet, and purchase data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes new purchase suggestions based on the analysis results. The disposal proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for disposal or recycling. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A data collection unit that collects data entered by the user, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a new purchase proposal based on the analysis results obtained by the aforementioned analysis unit, The system includes a waste proposal unit that makes suggestions for disposal or recycling based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects photos of the clothes users wear daily, photos of their closets, and purchase data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes user styling trends and preferences, frequency of appearance, closet usage, and clothing duplication. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, By analyzing user preferences, brands, seasonal timing, and trends, we provide recommendations from e-commerce sites and online shopping sites at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We offer coupons and sale information, suggesting the best time for users to make a purchase. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned waste proposal unit, We analyze the fullness of your closet and suggest getting rid of clothes you haven't worn in a while or that are duplicates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned waste proposal unit, We help users easily create draft listings for online auctions and flea markets. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned waste proposal unit, We propose eco-friendly disposal methods for products that are not selling well. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting clothing photos and purchase data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting photos of clothing and purchase data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting photos of clothing and purchase data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting photos of clothing and purchase data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Prioritize analysis based on data submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Apply different suggestion algorithms depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We will prioritize proposals based on the timing of product submissions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, Adjust the order of suggestions based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned waste proposal unit, It estimates the user's emotions and adjusts the methods of suggesting disposal and recycling based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned waste proposal unit, We analyze users' past purchasing behavior to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned waste proposal unit, Customize the suggestion method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned waste proposal unit, It estimates user emotions and determines the priority of disposal and recycling based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned waste proposal unit, The optimal suggestion method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned waste proposal unit, Analyze users' social media activity and propose solutions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data entered by the user, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a new purchase proposal based on the analysis results obtained by the aforementioned analysis unit, The system includes a waste proposal unit that makes suggestions for disposal or recycling based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects photos of the clothes users wear daily, photos of their closets, and purchase data. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes user styling trends and preferences, frequency of appearance, closet usage, and clothing duplication. The system according to feature 1.

4. The aforementioned proposal section is, By analyzing user preferences, brands, seasonal timing, and trends, we provide recommendations from e-commerce sites and online shopping sites at the optimal time. The system according to feature 1.

5. The aforementioned proposal section is, We offer coupons and sale information, suggesting the best time for users to make a purchase. The system according to feature 1.

6. The aforementioned waste proposal unit, We analyze the fullness of your closet and suggest getting rid of clothes you haven't worn in a while or that you own duplicates of. The system according to feature 1.

7. The aforementioned waste proposal unit, We help users easily create draft listings for online auctions and flea markets. The system according to feature 1.

8. The aforementioned waste proposal unit, We propose eco-friendly disposal methods for products that are not selling well. The system according to feature 1.

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