system

Abstract

We provide the system. [Solution] A means of receiving clothing information entered by the user, A means of using an artificial intelligence model to analyze the aforementioned information and evaluate the frequency and necessity of clothing use, A means for proposing a method of disposing of or storing clothing based on the evaluation results, A means for providing price information of similar products in the market in accordance with the above proposal, A means of linking the aforementioned clothing to other platforms corresponding to the selected disposal method, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Many consumers are unable to properly manage the clothes they purchase, and the clothes may be accumulated uselessly in the closet. Such a situation causes consumers to purchase more clothes than necessary, which also has an adverse impact on the environment. In addition, it takes time to dispose of unnecessary clothes, and since the criteria for determining which clothes should be discarded are not clear, many consumers have difficulty in decluttering. The purpose of this invention is to solve this problem by providing a system that allows consumers to easily analyze the frequency of use and necessity of clothes, and proposing an appropriate timing and method for disposal. 【Means for Solving the Problems】 【0005】 This invention relates to a system that uses a terminal for consumers to input information about the clothing they own, a server to analyze that information, and an artificial intelligence model to provide the analysis results. When a user inputs information such as the purchase date, price, number of times worn, and weather information for their clothing, the server uses the artificial intelligence model to analyze the data and evaluate the frequency of use and necessity of the clothing. Based on the evaluation results, it generates suggestions for disposal or storage of the clothing and presents disposal methods such as donating to a recycling box or selling through a flea market application. Furthermore, it provides price information for similar products in the market and enables integration with other platforms corresponding to the selected disposal method. As a result, consumers can declutter more wisely and save money for purchasing new clothing. 【0006】 "User" refers to a consumer who uses this system to manage their clothing. 【0007】 "Clothing information" refers to data necessary for evaluating clothing, such as purchase date, price, number of times worn, and weather information. 【0008】 A "terminal" refers to an electronic device used by the user to input information about clothing and communicate with a server. 【0009】 A "server" refers to a computer system that receives clothing information transmitted from a terminal, analyzes it, and provides the results. 【0010】 "Analysis" refers to the process of evaluating the frequency and necessity of clothing use based on the data that has been submitted. 【0011】 An "artificial intelligence model" refers to machine learning algorithms and statistical methods used to analyze data. 【0012】 "Evaluation results" refer to information about the frequency and necessity of clothing use, obtained after analysis by an artificial intelligence model. 【0013】 "Suggestions" refer to recommendations regarding the disposal or storage of clothing provided to the user based on the evaluation results. 【0014】 A "recycling box" refers to a collection box used to gather unwanted clothing for donation or reuse. 【0015】 A "flea market application" refers to an online platform for individuals to buy and sell items such as clothing. 【0016】 "Market price information" refers to the estimated selling price of similar clothing, calculated based on past transaction data and current market conditions. 【0017】 "Disposal methods" refer to means of recycling, donating, or selling clothing. [Brief explanation of the drawing] 【0018】 [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]Shows an emotion map where multiple emotions are mapped. [Figure 10] Shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Modes for Carrying Out the Invention】 【0019】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0020】 First, the language used in the following description will be explained. 【0021】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0022】 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. 【0023】 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. 【0024】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0025】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0026】 [First Embodiment] 【0027】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0028】 As shown in Figure 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. 【0029】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0030】 The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0031】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0032】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0033】 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. 【0034】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0035】 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. 【0036】 The 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. 【0037】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0038】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0039】 This invention is a system designed for efficient clothing management. The system primarily aims to accurately track information about the user's clothing, analyze its frequency of use and necessity, and then propose appropriate disposal methods. Users input information about their clothing using a terminal. Specifically, by registering information such as purchase date, price, number of times worn, and weather conditions, the system gains a detailed understanding of the clothing's usage. 【0040】 The terminal sends information entered by the user to the server. After receiving this information, the server analyzes the data using an internal artificial intelligence model. This model utilizes machine learning algorithms to evaluate the frequency of use and importance of each registered garment. The evaluation results are calculated as a score based on the usage patterns and future usage predictions for each garment. 【0041】 After the analysis is complete, the server generates suggestions for each garment. These suggestions include whether the garment needs to be disposed of and recommendations for storage. The suggestions also include price information for similar garments based on market transaction data, helping users set appropriate prices when selling on a flea market application. 【0042】 As a concrete example, consider a case where a user registers a suit that was purchased more than two years ago and worn five times or less. Analysis reveals that this suit has been used very little and is likely to be used infrequently in the future. The server encourages donation to a recycling bin and suggests a fair price based on market research for selling it on a flea market application. In this way, users can choose the best way to dispose of their unwanted clothing based on the server's suggestions. 【0043】 The system promotes environmental considerations through clothing management and supports user decision-making, thereby helping consumers practice sustainable living. Through this process, this invention provides a practical means for consumers to manage their clothing efficiently and effectively. 【0044】 The following describes the processing flow. 【0045】 Step 1: 【0046】 Users input clothing information using their devices. Specifically, users enter data such as the purchase date, price, number of times worn, and weather conditions into a dedicated input form. This information is registered for each garment, and users can confirm and submit it as instructed. 【0047】 Step 2: 【0048】 The terminal temporarily stores the clothing information entered by the user and then transmits it to the server via the internet. Security is considered during this process, and encrypted communication protocols such as HTTPS are used. 【0049】 Step 3: 【0050】 The server receives data sent from the terminal and records it in a database. It then prepares the recorded data for use as input for an artificial intelligence model. 【0051】 Step 4: 【0052】 The server analyzes the received data using an artificial intelligence model. Based on the registered information, the model evaluates the frequency of clothing use and calculates a score to determine its necessity. This score is then used in subsequent suggestion generation. 【0053】 Step 5: 【0054】 The server generates suggestions for the user based on the analysis results. Specifically, these suggestions include the need to declutter clothing, appropriate disposal methods, the possibility of donating to a recycling bin, and recommended selling prices on flea market applications. 【0055】 Step 6: 【0056】 The server sends the generated proposal to the terminal. The terminal receives it and displays the proposal to the user in an easy-to-understand format. The user then decides what action to take based on the information provided. 【0057】 Step 7: 【0058】 If the user chooses to take action according to the suggestion, the device will initiate the next step, depending on the choice, such as listing an item on a flea market application or donating it to a recycling box. The server updates the status and records the results to confirm that these actions have been completed. 【0059】 (Example 1) 【0060】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0061】 Currently, many consumers lack efficient ways to manage their clothing, resulting in an increase in unused garments, wasted expenses, and environmental burdens. Furthermore, a lack of information on appropriate disposal methods often leads consumers to make unhelpful decisions. Against this backdrop, there is a need for efficient and effective methods to propose clothing management and disposal strategies. 【0062】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0063】 In this invention, the server includes means for acquiring attribute information about clothing entered by the user, means for transferring the attribute information to an information processing device via a network, and means for evaluating the frequency of use and importance of clothing based on the attribute information using an internal generating AI model. This makes it possible to propose appropriate management and efficient disposal methods for clothing. 【0064】 A "user" is an individual or organization that inputs clothing attribute information into the system. 【0065】 "Clothing attribute information" refers to data such as purchase date, price, number of times worn, and weather conditions, and is necessary information for evaluating the usage status of clothing. 【0066】 "Means of transferring information to an information processing device via a network" refers to a communication method for sending data from a terminal to a server and analyzing the information. 【0067】 A "database" is an information system used to organize and securely store received attribute information. 【0068】 A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and evaluate the frequency and importance of clothing use. 【0069】 "Disposal methods" refer to ways of dealing with clothing, such as recycling, reuse, or selling it on the market. 【0070】 "Market value of similar items" refers to market price information for other products with similar attributes to the clothing item in question, and is used as a reference when users sell or trade clothing. 【0071】 "Means for exchanging data with data systems" refers to communication means for exchanging information with other platforms and applications in order to implement the proposed disposal method. 【0072】 This invention provides a system for efficiently managing a user's clothing. This system primarily consists of a terminal, a server, and a generative AI model. Specific embodiments are described below. 【0073】 First, users use their devices to input detailed attribute information about their clothing. Specifically, they can register information such as the purchase date, price, number of times worn, and weather conditions. This allows users to accurately understand how their clothing is being used. 【0074】 Next, the terminal structures the input information as digital data and sends it to the server via the network. Universal data formats such as JSON are often used in this process. Furthermore, compliance with security protocols ensures data integrity and protection. 【0075】 After receiving the data, the server stores it in a database for subsequent analysis. The server's internal generative AI model is used for data analysis. This model employs machine learning algorithms to evaluate the frequency of use and importance of each garment. For example, a coat that has been worn infrequently over the years is presumed to have decreased value. 【0076】 Based on the analysis results, the server suggests the best way for the user to store or dispose of their clothing. For example, for suits worn infrequently, it can encourage donation to a recycling bin and suggest a selling price at a flea market based on market value. 【0077】 An example of a prompt message is: "This jacket was purchased in March 2021 and has been worn 10 times. Please suggest how to dispose of this garment." This allows the user to make a data-driven, rational decision. 【0078】 This system allows users to efficiently manage their clothing and helps them choose appropriate disposal methods. This promotes sustainable consumption behavior and reduces environmental impact. 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The user uses the terminal to enter detailed attribute information about the clothing. Specifically, they enter data such as the purchase date, price, number of times worn, and relevant weather conditions through the application screen, and then press the submit button. Once this input is complete, the terminal prepares the data for the next step. 【0082】 Step 2: 【0083】 The terminal structures the attribute information received from the user as digital data. This data is typically in JSON format, a format that facilitates subsequent parsing. The terminal sends the structured data to the server via the internet. During this process, the data is appropriately encoded and security protocols are applied to ensure the integrity of the information. 【0084】 Step 3: 【0085】 The server receives digital data transmitted from the terminal and stores it in the database. Here, the server validates the input data to check for inconsistencies and errors. It then saves the information to the appropriate table in the database, preparing it for analysis. Once the received data is correctly stored, the server proceeds with the analysis process. 【0086】 Step 4: 【0087】 The server uses a generative AI model to perform data analysis. Based on the received clothing attribute information, it uses a machine learning algorithm to evaluate the frequency of use and importance of each garment. This analysis process also references past usage data and market data to predict future usage. The output of the analysis is generated as an evaluation score for each garment. 【0088】 Step 5: 【0089】 Based on the analysis results, the server generates suggestions for how to store or dispose of clothing for the user. Using the evaluation score obtained from the generated AI model as a reference, it presents options such as recycling, reuse, or selling. The suggestions also include the prices of similar items based on market data, which are presented as reference information when selling. 【0090】 Step 6: 【0091】 The server sends the generated suggestions back to the terminal and notifies the user. The terminal receives the data from the server and displays the suggestions in the user interface. Based on this information, the user can make specific decisions regarding the management and disposal of clothing. 【0092】 (Application Example 1) 【0093】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0094】 In modern times, it has become common for individuals to own a large amount of clothing, which has made clothing management more complex. Many clothes remain unused in closets, without proper use or disposal. This situation can clutter living spaces and potentially encourage overconsumption, thus highlighting the need for efficient clothing management methods. 【0095】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0096】 In this invention, the server includes means for receiving information about clothing entered by the user, means for using an artificial intelligence model to analyze the information and evaluate the frequency of use and necessity of the clothing, and means for proposing a method of disposal or storage of the clothing based on the evaluation results. This enables efficient registration and management of clothing using new image recognition technology, and allows for suggestions based on the frequency of use and necessity of individual clothing items from among a large number of items. 【0097】 "User" refers to an individual or user who utilizes the clothing management system. 【0098】 "Information" refers to detailed data related to clothing, including purchase date, price, number of times worn, and other relevant information. 【0099】 An "artificial intelligence model" is a system designed using machine learning algorithms to analyze the frequency and necessity of clothing use. 【0100】 "Suggestions" refer to specific advice provided to users based on the analysis results regarding how to dispose of and store clothing. 【0101】 "Market" refers to an economic space or flea market where similar goods are traded. 【0102】 "Image recognition technology" is a technology used to extract specific information from image data, and is used for registering and classifying clothing. 【0103】 "Application software" refers to computer programs that run systems and provide user interfaces. 【0104】 A "platform" is a foundational system or application that provides specific functions or services. 【0105】 This invention is a system for managing clothing and suggesting optimal disposal or storage methods. The system consists of user terminals, servers, and a cloud architecture. 【0106】 The user's device is a hardware device such as a smartphone, tablet, or home robot. This device uses image recognition technology to scan the user's clothing and provides a means to input relevant information such as the purchase date and number of times worn. For example, TENSORFLOW® is used to classify images of clothing and register the information in a database. 【0107】 The server is located in the cloud and processes information using cloud computing services such as AWS® Lambda. It receives clothing data sent by users and performs data analysis using an internal artificial intelligence model. This model is built using machine learning frameworks such as PyTorch and evaluates the frequency and necessity of clothing use. Based on the analysis results, it suggests specific disposal and storage methods to the user. 【0108】 Based on the suggestions provided, users can decide whether to donate or sell unwanted clothing. The suggestions include pricing information for similar items based on market research, allowing users to set appropriate prices. For example, if a user hasn't worn a jacket in their closet for more than three months, they will be presented with the option to donate or sell it, along with its market value. 【0109】 As a concrete example, when a user scans their summer shirt and registers it in the system, the AI ​​model can analyze the usage frequency data and recommend donations. Furthermore, it can confirm the user's intentions using prompts such as, "Have you worn this shirt recently? Do you plan to wear it again?" 【0110】 In this way, the system can efficiently manage clothing and support sustainable consumption. 【0111】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0112】 Step 1: 【0113】 The user's device captures images of clothing via its camera. The user enters detailed information such as the purchase date and price. The entered information is organized as image data and text data and sent to a database. 【0114】 Step 2: 【0115】 The server analyzes image and text data received from the database. Image recognition technology is used to identify clothing items, and machine learning algorithms evaluate factors such as frequency of wear and type. The resulting data is output as a score reflecting the user's clothing usage. 【0116】 Step 3: 【0117】 The server generates management suggestions for each garment based on the evaluation results. Using market price information and usage frequency data, specific action plans are created that suggest disposal and storage methods. These suggestions are stored in a database and notified to the user's terminal. 【0118】 Step 4: 【0119】 The user's device displays suggestions sent from the server and provides an interface for selecting specific actions. Users can choose to donate or sell items at a flea market, and their selections are returned to the server. 【0120】 Step 5: 【0121】 The server notifies the corresponding platform of the necessary data in order to perform actions based on the user's selection. For example, a process is initiated to contact a recycling company according to the selected donation method. 【0122】 Through the above processing steps, users can efficiently manage their clothing, enabling them to either continue using it or dispose of it properly. 【0123】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0124】 This invention is a system that personalizes suggestions to users by combining emotion recognition technology with suggestions for clothing management and disposal methods. The system receives clothing information from the user and, based on the analysis results, suggests disposal methods. Furthermore, by using an emotion engine that recognizes the user's emotional state, the system can flexibly adapt the suggestions to the user's emotions. 【0125】 First, the user uses a device to enter information related to their clothing. This information includes basic data such as the purchase date, price, number of times worn, and weather conditions. The device then sends this information to the server. 【0126】 The server receives the transmitted information and uses an artificial intelligence model to analyze the frequency and necessity of clothing use. The results of the analysis are quantified, and a score is generated to determine how often the clothing is actually used and how necessary it is. 【0127】 Furthermore, the server uses an emotion engine to analyze the user's emotions based on their voice tone and facial expressions. The analysis results are used to determine the user's emotional state at that moment (e.g., happiness, sadness, stress, etc.). This allows the server to adjust its suggestions according to the user's emotions. 【0128】 As a concrete example, consider a scenario where a user is using a device equipped with a camera and microphone for emotion recognition, and unconsciously reveals their emotions after entering clothing information. In this case, if the emotion engine detects that the user is experiencing stress, the system will provide messages to alleviate the emotions and suggestions to promote relaxation, in addition to the default suggestions. For example, "Considering your current stress level, we recommend that you first enjoy your favorite clothing." 【0129】 This invention enables flexible suggestions that take into account the user's emotional state, streamlining clothing management and improving the user experience. This system provides concrete means to support sustainable clothing management while paying greater attention to the user's emotions. 【0130】 The following describes the processing flow. 【0131】 Step 1: 【0132】 The user uses a terminal to enter information about their clothing. They enter data such as the purchase date, price, number of times worn, and weather conditions, and then press the submit button to prepare the data for sending to the server. 【0133】 Step 2: 【0134】 The terminal saves the entered information to a database and sends it to the server using a secure protocol. User input data is transferred to the server in the format necessary for analysis. 【0135】 Step 3: 【0136】 The server receives data sent from the terminal and prepares to input the data into the artificial intelligence model. Preprocessing is performed to analyze the registered clothing information. 【0137】 Step 4: 【0138】 The server uses an artificial intelligence model to analyze the received data and generate a score that evaluates the frequency of use and necessity of clothing. This score is an important indicator that influences suggestions for disposing of or storing clothing. 【0139】 Step 5: 【0140】 On the other hand, the device collects the user's voice tone and facial expression data. The device uses its camera and microphone to send this data to the emotion engine. 【0141】 Step 6: 【0142】 The server uses an emotion engine to analyze the user's emotional state. It recognizes the user's emotional state (happiness, sadness, stress, etc.) and incorporates the analysis results into the disposal suggestions. 【0143】 Step 7: 【0144】 The server integrates the output of the artificial intelligence model with the analysis results of the emotion engine to generate suggestions for the optimal disposal method and pricing for the user. These suggestions are tailored to take the user's emotional state into consideration. 【0145】 Step 8: 【0146】 The generated suggestions are sent to the device and presented to the user visually or audibly. Based on the presented information, the user can decide on their actions and proceed to the next step. 【0147】 (Example 2) 【0148】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0149】 In recent years, as the amount of personal possessions has increased, there has been a growing need to properly manage them and choose appropriate disposal methods as needed. However, there is no system in place that takes into account not only the value and usage of the items but also the owner's emotional state when making suggestions, making it difficult to provide flexible proposals that meet individual needs. 【0150】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0151】 In this invention, the server includes means for receiving information about items entered by the user, means for using a machine learning model to analyze the information and evaluate the frequency and necessity of using the items, and means for recognizing the user's emotional state from voice and image data and adjusting the suggestions based on that emotional state. This enables flexible and appropriate suggestions that take into account the usage status of the items and the owner's emotions. 【0152】 A "user" is an individual or legal entity that uses this system to input information about goods and receives management and suggestions. 【0153】 "Items" refer to specific objects that users input into the system and which are the subject of management and suggestions. 【0154】 "Information" includes detailed data about an item, such as the purchase date, price, number of uses, and weather information. 【0155】 A "machine learning model" is a collection of artificially constructed algorithms used to analyze information about an item and evaluate its frequency of use and necessity. 【0156】 "Emotional state" refers to the psychological and emotional state identified based on data such as the user's voice tone and facial expressions. 【0157】 "Audio and image data" refers to audio and visual data collected to identify the user's emotional state. 【0158】 "Suggestions" refer to information and recommended actions that the system provides to the user regarding how to dispose of or store items. 【0159】 A "digital platform" refers to a collection of online resources and services used for the disposal and coordination of goods. 【0160】 To implement this invention, the following processes are performed via the user, terminal, and server. First, the user uses the terminal to input information about the items they own. This information includes the purchase date, price, number of uses, and related weather information. This information is transmitted from the terminal to the server. 【0161】 The server analyzes the received information using a machine learning model. This analysis utilizes programming languages ​​such as Python and R, as well as data analysis software libraries such as NumPy and Pandas. The machine learning model evaluates the frequency of use and necessity of items, and scores the results. The resulting score is an important indicator of how often or how necessary an item is. 【0162】 In addition, the server uses emotion recognition technology to analyze the user's emotional state from their voice tone and facial expressions. This process utilizes machine learning frameworks such as TensorFlow and PyTorch to process audio and image data. For example, if the user's voice is unstable or their facial expression shows signs of fatigue, the system will determine that the user is experiencing stress. 【0163】 Based on the analyzed data, the server generates suggestions regarding the disposal and storage of items, taking into account the user's emotional state. A generative AI model is used to generate these suggestions, presenting the user with personalized messages. An example of a prompt might be, "Consider the user's emotional state and generate suggestions to alleviate stress." 【0164】 For example, if a user inputs information about clothing and the device shows a significant stress level, the server will generate a suggestion such as, "Considering your current stress level, we recommend that you first enjoy your favorite clothing," and provide feedback to the user through the device. 【0165】 In this way, the present invention aims to provide flexible solutions that meet user needs, make the management of goods more efficient, and improve the owner's experience. 【0166】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0167】 Step 1: 【0168】 The user uses a terminal to input information about items. Specific input data includes purchase date, price, usage count, and weather information. On the input screen, the user enters the required information for each item and, after confirmation, presses the submit button. This action causes the terminal to compile the entered information into a data packet and send it to the server. 【0169】 Step 2: 【0170】 The server receives data packets sent from the terminal. After verifying the accuracy of the received data, the server stores information about each item in its database. This data is then ready for subsequent analysis. After being stored in the database, a confirmation response is sent to the terminal indicating that the data is ready for analysis. 【0171】 Step 3: 【0172】 The server uses machine learning models to analyze data. Using the item information received as input data, it first formats and cleans the data using libraries such as NumPy and Pandas. Then, the formatted data is input into a generating AI model to generate scores based on the frequency of use and necessity of each item. These scores are then saved again in the database. 【0173】 Step 4: 【0174】 The server begins processing to recognize the user's emotional state from audio and image data. The device sends the collected audio tone and facial image data to the server for emotion recognition. The server analyzes this data using TensorFlow or PyTorch to determine the user's emotional state. The analysis results are stored as a numerical representation of the user's emotions. 【0175】 Step 5: 【0176】 The server combines the item's usage frequency score and the user's emotional state score to generate a suggestion for disposal or storage. A generative AI model forms appropriate suggestions in natural language based on the input prompt. For example, a suggestion might be presented as, "Considering your stress level, we recommend enjoying your favorite items first." 【0177】 Step 6: 【0178】 The server sends the generated suggestion text to the terminal. The terminal displays the received suggestion to the user and provides voice guidance as needed. The user reviews the suggestion on the screen and uses it as a reference when choosing their next action. This feedback also contributes to improvements for the next data analysis. 【0179】 (Application Example 2) 【0180】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0181】 In modern society, the amount of clothing owned has increased, making its management more complex. This makes it difficult for users to decide which clothes to keep and which to discard. Furthermore, the lack of flexible suggestions that take into account the user's emotions and mental state makes clothing management even more inconvenient and stressful. To address this challenge, a system is needed that streamlines clothing management and provides personalized suggestions. 【0182】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0183】 In this invention, the server includes means for receiving clothing data entered by the user, means for using a machine learning model to analyze the data and evaluate the clothing usage habits and importance, and means for analyzing the user's emotional state using emotion recognition technology and adapting the suggested content to the user's emotions. This optimizes decision-making in clothing management and enables suggestions that take the user's emotions into consideration. 【0184】 "Clothing data" refers to the collective information about clothing entered by the user, such as the purchase date, price, frequency of wear, and weather information. 【0185】 A "machine learning model" is an artificial intelligence algorithm used to evaluate clothing usage habits and importance. 【0186】 "Emotion recognition technology" is a technology that determines a user's emotional state by analyzing their voice tone and facial expressions. 【0187】 "Means of adapting suggested content to the user's emotions" refers to a method of customizing clothing management suggestions according to the user's emotions, based on emotion recognition technology. 【0188】 "Suggestions to promote relaxation" refer to suggestions made to alleviate the user's burden when it is determined that the user is experiencing stress. 【0189】 "Other systems" refers to external platforms or services that are used in conjunction with the disposal or reuse of clothing using the selected processing method. 【0190】 In this invention, a server plays a central role in the system for managing clothing. Users input information about their clothing into the server from a device such as a smartphone or tablet. This includes the purchase date, price, frequency of wear, and weather information. The server receives this information and uses a machine learning model (for example, a model using Python and the scikit-learn library) to evaluate the clothing's usage habits and importance. 【0191】 Furthermore, the server uses the camera and microphone on the device to capture the user's voice tone and facial expressions, and analyzes the user's emotions using emotion recognition technology (e.g., an emotion recognition model using the TensorFlow library). Based on the emotion evaluation, the server adapts suggestions regarding clothing management to the user's emotions. 【0192】 As a result, the generated suggestions can go beyond mere functional suggestions and include relaxation-promoting suggestions that take into account the user's emotions and mental state. For example, if the user is feeling stressed, the system might display a message such as, "Considering your current stress level, we recommend you take a break for a while. Afterwards, we can check your clothes together." 【0193】 Example prompt: "Consider the user's current emotional state and generate optimal clothing management suggestions. How is the user feeling?" 【0194】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0195】 Step 1: 【0196】 The user enters clothing data (purchase date, price, frequency of wear, and weather information) using a terminal. This input data is sent to the server. The server verifies the received data and stores it in a database. 【0197】 Step 2: 【0198】 The server inputs clothing data stored in a database into a machine learning model to evaluate clothing usage habits and importance. The model analyzes the input data and outputs a score indicating the frequency and importance of clothing use. This score serves as an indicator of how often clothing is used or needs to be stored. 【0199】 Step 3: 【0200】 The user provides emotional data to the server through the device's camera and microphone. The server inputs this data into an emotion recognition model to analyze the user's current emotional state. This process analyzes voice tone and facial expression data to evaluate what the user is feeling. 【0201】 Step 4: 【0202】 The server integrates clothing usage scores with the user's emotional state to generate optimal suggestions. These suggestions are tailored to the user's mood; for example, a user experiencing stress might receive suggestions promoting relaxation. The server then returns these results to the user's device, displaying more detailed suggestions. 【0203】 Step 5: 【0204】 Depending on the user's selection, the server assists in executing the proposed processing method. For example, if the user chooses to donate clothing, the server will coordinate with the appropriate platform to proceed with the process. It will also provide market price information as needed, presenting the user with the best possible option. 【0205】 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. 【0206】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0207】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0208】 [Second Embodiment] 【0209】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0210】 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. 【0211】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0212】 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. 【0213】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0214】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0215】 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. 【0216】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0217】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0218】 The 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. 【0219】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0220】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0221】 This invention is a system designed for efficient clothing management. The system primarily aims to accurately track information about the user's clothing, analyze its frequency of use and necessity, and then propose appropriate disposal methods. Users input information about their clothing using a terminal. Specifically, by registering information such as purchase date, price, number of times worn, and weather conditions, the system gains a detailed understanding of the clothing's usage. 【0222】 The terminal sends information entered by the user to the server. After receiving this information, the server analyzes the data using an internal artificial intelligence model. This model utilizes machine learning algorithms to evaluate the frequency of use and importance of each registered garment. The evaluation results are calculated as a score based on the usage patterns and future usage predictions for each garment. 【0223】 After the analysis is complete, the server generates suggestions for each garment. These suggestions include whether the garment needs to be disposed of and recommendations for storage. The suggestions also include price information for similar garments based on market transaction data, helping users set appropriate prices when selling on a flea market application. 【0224】 As a concrete example, consider a case where a user registers a suit that was purchased more than two years ago and worn five times or less. Analysis reveals that this suit has been used very little and is likely to be used infrequently in the future. The server encourages donation to a recycling bin and suggests a fair price based on market research for selling it on a flea market application. In this way, users can choose the best way to dispose of their unwanted clothing based on the server's suggestions. 【0225】 The system promotes environmental considerations through clothing management and supports user decision-making, thereby helping consumers practice sustainable living. Through this process, this invention provides a practical means for consumers to manage their clothing efficiently and effectively. 【0226】 The following describes the processing flow. 【0227】 Step 1: 【0228】 Users input clothing information using their devices. Specifically, users enter data such as the purchase date, price, number of times worn, and weather conditions into a dedicated input form. This information is registered for each garment, and users can confirm and submit it as instructed. 【0229】 Step 2: 【0230】 The terminal temporarily stores the clothing information entered by the user and then transmits it to the server via the internet. Security is considered during this process, and encrypted communication protocols such as HTTPS are used. 【0231】 Step 3: 【0232】 The server receives data sent from the terminal and records it in a database. It then prepares the recorded data for use as input for an artificial intelligence model. 【0233】 Step 4: 【0234】 The server analyzes the received data using an artificial intelligence model. Based on the registered information, the model evaluates the frequency of clothing use and calculates a score to determine its necessity. This score is then used in subsequent suggestion generation. 【0235】 Step 5: 【0236】 The server generates suggestions for the user based on the analysis results. Specifically, these suggestions include the need to declutter clothing, appropriate disposal methods, the possibility of donating to a recycling bin, and recommended selling prices on flea market applications. 【0237】 Step 6: 【0238】 The server sends the generated proposal to the terminal. The terminal receives it and displays the proposal to the user in an easy-to-understand format. The user then decides what action to take based on the information provided. 【0239】 Step 7: 【0240】 If the user chooses to take action according to the suggestion, the device will initiate the next step, depending on the choice, such as listing an item on a flea market application or donating it to a recycling box. The server updates the status and records the results to confirm that these actions have been completed. 【0241】 (Example 1) 【0242】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0243】 Currently, many consumers lack efficient ways to manage their clothing, resulting in an increase in unused garments, wasted expenses, and environmental burdens. Furthermore, a lack of information on appropriate disposal methods often leads consumers to make unhelpful decisions. Against this backdrop, there is a need for efficient and effective methods to propose clothing management and disposal strategies. 【0244】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0245】 In this invention, the server includes means for acquiring attribute information about clothing entered by the user, means for transferring the attribute information to an information processing device via a network, and means for evaluating the frequency of use and importance of clothing based on the attribute information using an internal generating AI model. This makes it possible to propose appropriate management and efficient disposal methods for clothing. 【0246】 A "user" is an individual or organization that inputs clothing attribute information into the system. 【0247】 "Clothing attribute information" refers to data such as purchase date, price, number of times worn, and weather conditions, and is necessary information for evaluating the usage status of clothing. 【0248】 "Means of transferring information to an information processing device via a network" refers to a communication method for sending data from a terminal to a server and analyzing the information. 【0249】 A "database" is an information system used to organize and securely store received attribute information. 【0250】 A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and evaluate the frequency and importance of clothing use. 【0251】 "Disposal methods" refer to ways of dealing with clothing, such as recycling, reuse, or selling it on the market. 【0252】 "Market value of similar items" refers to market price information for other products with similar attributes to the clothing item in question, and is used as a reference when users sell or trade clothing. 【0253】 "Means for exchanging data with data systems" refers to communication means for exchanging information with other platforms and applications in order to implement the proposed disposal method. 【0254】 This invention provides a system for efficiently managing a user's clothing. This system primarily consists of a terminal, a server, and a generative AI model. Specific embodiments are described below. 【0255】 First, users use their devices to input detailed attribute information about their clothing. Specifically, they can register information such as the purchase date, price, number of times worn, and weather conditions. This allows users to accurately understand how their clothing is being used. 【0256】 Next, the terminal structures the input information as digital data and sends it to the server via the network. Universal data formats such as JSON are often used in this process. Furthermore, compliance with security protocols ensures data integrity and protection. 【0257】 After receiving the data, the server stores it in a database for subsequent analysis. The server's internal generative AI model is used for data analysis. This model employs machine learning algorithms to evaluate the frequency of use and importance of each garment. For example, a coat that has been worn infrequently over the years is presumed to have decreased value. 【0258】 Based on the analysis results, the server suggests the best way for the user to store or dispose of their clothing. For example, for suits worn infrequently, it can encourage donation to a recycling bin and suggest a selling price at a flea market based on market value. 【0259】 An example of a prompt message is: "This jacket was purchased in March 2021 and has been worn 10 times. Please suggest how to dispose of this garment." This allows the user to make a data-driven, rational decision. 【0260】 This system allows users to efficiently manage their clothing and helps them choose appropriate disposal methods. This promotes sustainable consumption behavior and reduces environmental impact. 【0261】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0262】 Step 1: 【0263】 The user uses the terminal to enter detailed attribute information about the clothing. Specifically, they enter data such as the purchase date, price, number of times worn, and relevant weather conditions through the application screen, and then press the submit button. Once this input is complete, the terminal prepares the data for the next step. 【0264】 Step 2: 【0265】 The terminal structures the attribute information received from the user as digital data. This data is typically in JSON format, a format that facilitates subsequent parsing. The terminal sends the structured data to the server via the internet. During this process, the data is appropriately encoded and security protocols are applied to ensure the integrity of the information. 【0266】 Step 3: 【0267】 The server receives digital data transmitted from the terminal and stores it in the database. Here, the server validates the input data to check for inconsistencies and errors. It then saves the information to the appropriate table in the database, preparing it for analysis. Once the received data is correctly stored, the server proceeds with the analysis process. 【0268】 Step 4: 【0269】 The server uses a generative AI model to perform data analysis. Based on the received clothing attribute information, it uses a machine learning algorithm to evaluate the frequency of use and importance of each garment. This analysis process also references past usage data and market data to predict future usage. The output of the analysis is generated as an evaluation score for each garment. 【0270】 Step 5: 【0271】 Based on the analysis results, the server generates suggestions for how to store or dispose of clothing for the user. Using the evaluation score obtained from the generated AI model as a reference, it presents options such as recycling, reuse, or selling. The suggestions also include the prices of similar items based on market data, which are presented as reference information when selling. 【0272】 Step 6: 【0273】 The server sends the generated suggestions back to the terminal and notifies the user. The terminal receives the data from the server and displays the suggestions in the user interface. Based on this information, the user can make specific decisions regarding the management and disposal of clothing. 【0274】 (Application Example 1) 【0275】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0276】 In modern times, it has become common for individuals to own a large amount of clothing, which has made clothing management more complex. Many clothes remain unused in closets, without proper use or disposal. This situation can clutter living spaces and potentially encourage overconsumption, thus highlighting the need for efficient clothing management methods. 【0277】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0278】 In this invention, the server includes means for receiving information about clothing entered by the user, means for using an artificial intelligence model to analyze the information and evaluate the frequency of use and necessity of the clothing, and means for proposing a method of disposal or storage of the clothing based on the evaluation results. This enables efficient registration and management of clothing using new image recognition technology, and allows for suggestions based on the frequency of use and necessity of individual clothing items from among a large number of items. 【0279】 "User" refers to an individual or user who utilizes the clothing management system. 【0280】 "Information" refers to detailed data related to clothing, including purchase date, price, number of times worn, and other relevant information. 【0281】 "Artificial intelligence model" is a system designed using machine learning algorithms to analyze the usage frequency and necessity of clothing. 【0282】 "Proposal" is specific advice on clothing disposal and storage methods provided to the user based on the analysis results. 【0283】 "Market" refers to an economic venue or free market where transactions of similar goods occur. 【0284】 "Image recognition technology" is a technology used to extract specific information from image data and is utilized for clothing registration and classification. 【0285】 "Application software" is a computer program that runs the system and provides a user interface. 【0286】 "Platform" is a system or application that serves as a foundation for providing specific functions or services. 【0287】 This invention is a system for managing clothing and proposing optimal disposal or storage methods. The system is composed of a user's terminal, a server, and a cloud architecture. 【0288】 The user's device is a hardware device such as a smartphone, tablet, or home robot. This device uses image recognition technology to scan the user's clothing and provides a means to input relevant information such as the purchase date and number of times worn. For example, TensorFlow is used to classify images of clothing and register the information in a database. 【0289】 The server is located in the cloud and processes information using cloud computing services such as AWS Lambda. It receives clothing data submitted by users and performs data analysis using an internal artificial intelligence model. This model is built using machine learning frameworks such as PyTorch and evaluates the frequency and necessity of clothing use. Based on the analysis results, it suggests specific disposal and storage methods to the user. 【0290】 Based on the suggestions provided, users can decide whether to donate or sell unwanted clothing. The suggestions include pricing information for similar items based on market research, allowing users to set appropriate prices. For example, if a user hasn't worn a jacket in their closet for more than three months, they will be presented with the option to donate or sell it, along with its market value. 【0291】 As a concrete example, when a user scans their summer shirt and registers it in the system, the AI ​​model can analyze the usage frequency data and recommend donations. Furthermore, it can confirm the user's intentions using prompts such as, "Have you worn this shirt recently? Do you plan to wear it again?" 【0292】 In this way, the system can efficiently manage clothing and support sustainable consumption. 【0293】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0294】 Step 1: 【0295】 The user's device captures images of clothing via its camera. The user enters detailed information such as the purchase date and price. The entered information is organized as image data and text data and sent to a database. 【0296】 Step 2: 【0297】 The server analyzes image and text data received from the database. Image recognition technology is used to identify clothing items, and machine learning algorithms evaluate factors such as frequency of wear and type. The resulting data is output as a score reflecting the user's clothing usage. 【0298】 Step 3: 【0299】 The server generates management suggestions for each garment based on the evaluation results. Using market price information and usage frequency data, specific action plans are created that suggest disposal and storage methods. These suggestions are stored in a database and notified to the user's terminal. 【0300】 Step 4: 【0301】 The user's device displays suggestions sent from the server and provides an interface for selecting specific actions. Users can choose to donate or sell items at a flea market, and their selections are returned to the server. 【0302】 Step 5: 【0303】 The server notifies the corresponding platform of the necessary data in order to perform actions based on the user's selection. For example, a process is initiated to contact a recycling company according to the selected donation method. 【0304】 Through the above processing steps, users can efficiently manage their clothing, enabling them to either continue using it or dispose of it properly. 【0305】 Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions. 【0306】 The present invention is a system that personalizes proposals to the user by combining emotion recognition technology with a proposal for a method of managing and disposing of clothing. This system receives clothing information from the user and proposes a method of disposing of the clothing based on the analysis result. Also, by using an emotion engine that recognizes the user's emotional state, it is possible to flexibly adapt the content of the proposal to the user's emotions. 【0307】 First, the user uses a terminal to input information related to clothing. The information items include basic data such as the purchase date, price, number of times worn, and weather conditions. The terminal transmits this information to the server. 【0308】 The server receives the transmitted information and analyzes the usage frequency and necessity of the clothing using an artificial intelligence model. The analysis result is digitized, and a score for determining how much the clothing is actually used and how necessary it is is generated. 【0309】 Furthermore, the server analyzes the user's emotions with an emotion engine based on the user's voice tone and expression. The analysis result is used to determine what emotional state (e.g., happiness, sadness, stress, etc.) the user is in at that moment. As a result, the server can adjust the proposal content according to the user's emotions. 【0310】 As a concrete example, consider a scenario where a user is using a device equipped with a camera and microphone for emotion recognition, and unconsciously reveals their emotions after entering clothing information. In this case, if the emotion engine detects that the user is experiencing stress, the system will provide messages to alleviate the emotions and suggestions to promote relaxation, in addition to the default suggestions. For example, "Considering your current stress level, we recommend that you first enjoy your favorite clothing." 【0311】 This invention enables flexible suggestions that take into account the user's emotional state, streamlining clothing management and improving the user experience. This system provides concrete means to support sustainable clothing management while paying greater attention to the user's emotions. 【0312】 The following describes the processing flow. 【0313】 Step 1: 【0314】 The user uses a terminal to enter information about their clothing. They enter data such as the purchase date, price, number of times worn, and weather conditions, and then press the submit button to prepare the data for sending to the server. 【0315】 Step 2: 【0316】 The terminal saves the entered information to a database and sends it to the server using a secure protocol. User input data is transferred to the server in the format necessary for analysis. 【0317】 Step 3: 【0318】 The server receives data sent from the terminal and prepares to input the data into the artificial intelligence model. Preprocessing is performed to analyze the registered clothing information. 【0319】 Step 4: 【0320】 The server uses an artificial intelligence model to analyze the received data and generate a score that evaluates the frequency of use and necessity of clothing. This score is an important indicator that influences suggestions for disposing of or storing clothing. 【0321】 Step 5: 【0322】 On the other hand, the device collects the user's voice tone and facial expression data. The device uses its camera and microphone to send this data to the emotion engine. 【0323】 Step 6: 【0324】 The server uses an emotion engine to analyze the user's emotional state. It recognizes the user's emotional state (happiness, sadness, stress, etc.) and incorporates the analysis results into the disposal suggestions. 【0325】 Step 7: 【0326】 The server integrates the output of the artificial intelligence model with the analysis results of the emotion engine to generate suggestions for the optimal disposal method and pricing for the user. These suggestions are tailored to take the user's emotional state into consideration. 【0327】 Step 8: 【0328】 The generated suggestions are sent to the device and presented to the user visually or audibly. Based on the presented information, the user can decide on their actions and proceed to the next step. 【0329】 (Example 2) 【0330】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0331】 In recent years, as the amount of personal possessions has increased, there has been a growing need to properly manage them and choose appropriate disposal methods as needed. However, there is no system in place that takes into account not only the value and usage of the items but also the owner's emotional state when making suggestions, making it difficult to provide flexible proposals that meet individual needs. 【0332】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0333】 In this invention, the server includes means for receiving information about items entered by the user, means for using a machine learning model to analyze the information and evaluate the frequency and necessity of using the items, and means for recognizing the user's emotional state from voice and image data and adjusting the suggestions based on that emotional state. This enables flexible and appropriate suggestions that take into account the usage status of the items and the owner's emotions. 【0334】 A "user" is an individual or legal entity that uses this system to input information about goods and receives management and suggestions. 【0335】 "Items" refer to specific objects that users input into the system and which are the subject of management and suggestions. 【0336】 "Information" includes detailed data about an item, such as the purchase date, price, number of uses, and weather information. 【0337】 A "machine learning model" is a collection of artificially constructed algorithms used to analyze information about an item and evaluate its frequency of use and necessity. 【0338】 "Emotional state" refers to the psychological and emotional state identified based on data such as the user's voice tone and facial expressions. 【0339】 "Audio and image data" refers to audio and visual data collected to identify the user's emotional state. 【0340】 "Suggestions" refer to information and recommended actions that the system provides to the user regarding how to dispose of or store items. 【0341】 A "digital platform" refers to a collection of online resources and services used for the disposal and coordination of goods. 【0342】 To implement this invention, the following processes are performed via the user, terminal, and server. First, the user uses the terminal to input information about the items they own. This information includes the purchase date, price, number of uses, and related weather information. This information is transmitted from the terminal to the server. 【0343】 The server analyzes the received information using a machine learning model. This analysis utilizes programming languages ​​such as Python and R, as well as data analysis software libraries such as NumPy and Pandas. The machine learning model evaluates the frequency of use and necessity of items, and scores the results. The resulting score is an important indicator of how often or how necessary an item is. 【0344】 In addition, the server uses emotion recognition technology to analyze the user's emotional state from their voice tone and facial expressions. This process utilizes machine learning frameworks such as TensorFlow and PyTorch to process audio and image data. For example, if the user's voice is unstable or their facial expression shows signs of fatigue, the system will determine that the user is experiencing stress. 【0345】 Based on the analyzed data, the server generates suggestions regarding the disposal and storage of items, taking into account the user's emotional state. A generative AI model is used to generate these suggestions, presenting the user with personalized messages. An example of a prompt might be, "Consider the user's emotional state and generate suggestions to alleviate stress." 【0346】 For example, if a user inputs information about clothing and the device shows a significant stress level, the server will generate a suggestion such as, "Considering your current stress level, we recommend that you first enjoy your favorite clothing," and provide feedback to the user through the device. 【0347】 In this way, the present invention aims to provide flexible solutions that meet user needs, make the management of goods more efficient, and improve the owner's experience. 【0348】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0349】 Step 1: 【0350】 The user uses a terminal to input information about items. Specific input data includes purchase date, price, usage count, and weather information. On the input screen, the user enters the required information for each item and, after confirmation, presses the submit button. This action causes the terminal to compile the entered information into a data packet and send it to the server. 【0351】 Step 2: 【0352】 The server receives data packets sent from the terminal. After verifying the accuracy of the received data, the server stores information about each item in its database. This data is then ready for subsequent analysis. After being stored in the database, a confirmation response is sent to the terminal indicating that the data is ready for analysis. 【0353】 Step 3: 【0354】 The server uses machine learning models to analyze data. Using the item information received as input data, it first formats and cleans the data using libraries such as NumPy and Pandas. Then, the formatted data is input into a generating AI model to generate scores based on the frequency of use and necessity of each item. These scores are then saved again in the database. 【0355】 Step 4: 【0356】 The server begins processing to recognize the user's emotional state from audio and image data. The device sends the collected audio tone and facial image data to the server for emotion recognition. The server analyzes this data using TensorFlow or PyTorch to determine the user's emotional state. The analysis results are stored as a numerical representation of the user's emotions. 【0357】 Step 5: 【0358】 The server combines the item's usage frequency score and the user's emotional state score to generate a suggestion for disposal or storage. A generative AI model forms appropriate suggestions in natural language based on the input prompt. For example, a suggestion might be presented as, "Considering your stress level, we recommend enjoying your favorite items first." 【0359】 Step 6: 【0360】 The server sends the generated suggestion text to the terminal. The terminal displays the received suggestion to the user and provides voice guidance as needed. The user reviews the suggestion on the screen and uses it as a reference when choosing their next action. This feedback also contributes to improvements for the next data analysis. 【0361】 (Application Example 2) 【0362】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0363】 In modern society, the amount of clothing owned has increased, making its management more complex. This makes it difficult for users to decide which clothes to keep and which to discard. Furthermore, the lack of flexible suggestions that take into account the user's emotions and mental state makes clothing management even more inconvenient and stressful. To address this challenge, a system is needed that streamlines clothing management and provides personalized suggestions. 【0364】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0365】 In this invention, the server includes means for receiving clothing data entered by the user, means for using a machine learning model to analyze the data and evaluate the clothing usage habits and importance, and means for analyzing the user's emotional state using emotion recognition technology and adapting the suggested content to the user's emotions. This optimizes decision-making in clothing management and enables suggestions that take the user's emotions into consideration. 【0366】 "Clothing data" refers to the collective information about clothing entered by the user, such as the purchase date, price, frequency of wear, and weather information. 【0367】 A "machine learning model" is an artificial intelligence algorithm used to evaluate clothing usage habits and importance. 【0368】 "Emotion recognition technology" is a technology that determines a user's emotional state by analyzing their voice tone and facial expressions. 【0369】 "Means of adapting suggested content to the user's emotions" refers to a method of customizing clothing management suggestions according to the user's emotions, based on emotion recognition technology. 【0370】 "Suggestions to promote relaxation" refer to suggestions made to alleviate the user's burden when it is determined that the user is experiencing stress. 【0371】 "Other systems" refers to external platforms or services that are used in conjunction with the disposal or reuse of clothing using the selected processing method. 【0372】 In this invention, a server plays a central role in the system for managing clothing. Users input information about their clothing into the server from a device such as a smartphone or tablet. This includes the purchase date, price, frequency of wear, and weather information. The server receives this information and uses a machine learning model (for example, a model using Python and the scikit-learn library) to evaluate the clothing's usage habits and importance. 【0373】 Furthermore, the server uses the camera and microphone on the device to capture the user's voice tone and facial expressions, and analyzes the user's emotions using emotion recognition technology (e.g., an emotion recognition model using the TensorFlow library). Based on the emotion evaluation, the server adapts suggestions regarding clothing management to the user's emotions. 【0374】 As a result, the generated suggestions can go beyond mere functional suggestions and include relaxation-promoting suggestions that take into account the user's emotions and mental state. For example, if the user is feeling stressed, the system might display a message such as, "Considering your current stress level, we recommend you take a break for a while. Afterwards, we can check your clothes together." 【0375】 Example prompt: "Consider the user's current emotional state and generate optimal clothing management suggestions. How is the user feeling?" 【0376】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0377】 Step 1: 【0378】 The user enters clothing data (purchase date, price, frequency of wear, and weather information) using a terminal. This input data is sent to the server. The server verifies the received data and stores it in a database. 【0379】 Step 2: 【0380】 The server inputs clothing data stored in a database into a machine learning model to evaluate clothing usage habits and importance. The model analyzes the input data and outputs a score indicating the frequency and importance of clothing use. This score serves as an indicator of how often clothing is used or needs to be stored. 【0381】 Step 3: 【0382】 The user provides emotional data to the server through the device's camera and microphone. The server inputs this data into an emotion recognition model to analyze the user's current emotional state. This process analyzes voice tone and facial expression data to evaluate what the user is feeling. 【0383】 Step 4: 【0384】 The server integrates clothing usage scores with the user's emotional state to generate optimal suggestions. These suggestions are tailored to the user's mood; for example, a user experiencing stress might receive suggestions promoting relaxation. The server then returns these results to the user's device, displaying more detailed suggestions. 【0385】 Step 5: 【0386】 Depending on the user's selection, the server assists in executing the proposed processing method. For example, if the user chooses to donate clothing, the server will coordinate with the appropriate platform to proceed with the process. It will also provide market price information as needed, presenting the user with the best possible option. 【0387】 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. 【0388】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0389】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0390】 [Third Embodiment] 【0391】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0392】 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. 【0393】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0394】 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. 【0395】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0396】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0397】 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. 【0398】 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. 【0399】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0400】 The 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. 【0401】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0402】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0403】 This invention is a system designed for efficient clothing management. The system primarily aims to accurately track information about the user's clothing, analyze its frequency of use and necessity, and then propose appropriate disposal methods. Users input information about their clothing using a terminal. Specifically, by registering information such as purchase date, price, number of times worn, and weather conditions, the system gains a detailed understanding of the clothing's usage. 【0404】 The terminal sends information entered by the user to the server. After receiving this information, the server analyzes the data using an internal artificial intelligence model. This model utilizes machine learning algorithms to evaluate the frequency of use and importance of each registered garment. The evaluation results are calculated as a score based on the usage patterns and future usage predictions for each garment. 【0405】 After the analysis is complete, the server generates suggestions for each garment. These suggestions include whether the garment needs to be disposed of and recommendations for storage. The suggestions also include price information for similar garments based on market transaction data, helping users set appropriate prices when selling on a flea market application. 【0406】 As a concrete example, consider a case where a user registers a suit that was purchased more than two years ago and worn five times or less. Analysis reveals that this suit has been used very little and is likely to be used infrequently in the future. The server encourages donation to a recycling bin and suggests a fair price based on market research for selling it on a flea market application. In this way, users can choose the best way to dispose of their unwanted clothing based on the server's suggestions. 【0407】 The system promotes environmental considerations through clothing management and supports user decision-making, thereby helping consumers practice sustainable living. Through this process, this invention provides a practical means for consumers to manage their clothing efficiently and effectively. 【0408】 The following describes the processing flow. 【0409】 Step 1: 【0410】 Users input clothing information using their devices. Specifically, users enter data such as the purchase date, price, number of times worn, and weather conditions into a dedicated input form. This information is registered for each garment, and users can confirm and submit it as instructed. 【0411】 Step 2: 【0412】 The terminal temporarily stores the clothing information entered by the user and then transmits it to the server via the internet. Security is considered during this process, and encrypted communication protocols such as HTTPS are used. 【0413】 Step 3: 【0414】 The server receives data sent from the terminal and records it in a database. It then prepares the recorded data for use as input for an artificial intelligence model. 【0415】 Step 4: 【0416】 The server analyzes the received data using an artificial intelligence model. Based on the registered information, the model evaluates the frequency of clothing use and calculates a score to determine its necessity. This score is then used in subsequent suggestion generation. 【0417】 Step 5: 【0418】 The server generates suggestions for the user based on the analysis results. Specifically, these suggestions include the need to declutter clothing, appropriate disposal methods, the possibility of donating to a recycling bin, and recommended selling prices on flea market applications. 【0419】 Step 6: 【0420】 The server sends the generated proposal to the terminal. The terminal receives it and displays the proposal to the user in an easy-to-understand format. The user then decides what action to take based on the information provided. 【0421】 Step 7: 【0422】 If the user chooses to take action according to the suggestion, the device will initiate the next step, depending on the choice, such as listing an item on a flea market application or donating it to a recycling box. The server updates the status and records the results to confirm that these actions have been completed. 【0423】 (Example 1) 【0424】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0425】 Currently, many consumers lack efficient ways to manage their clothing, resulting in an increase in unused garments, wasted expenses, and environmental burdens. Furthermore, a lack of information on appropriate disposal methods often leads consumers to make unhelpful decisions. Against this backdrop, there is a need for efficient and effective methods to propose clothing management and disposal strategies. 【0426】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0427】 In this invention, the server includes means for acquiring attribute information about clothing entered by the user, means for transferring the attribute information to an information processing device via a network, and means for evaluating the frequency of use and importance of clothing based on the attribute information using an internal generating AI model. This makes it possible to propose appropriate management and efficient disposal methods for clothing. 【0428】 A "user" is an individual or organization that inputs clothing attribute information into the system. 【0429】 "Clothing attribute information" refers to data such as purchase date, price, number of times worn, and weather conditions, and is necessary information for evaluating the usage status of clothing. 【0430】 "Means of transferring information to an information processing device via a network" refers to a communication method for sending data from a terminal to a server and analyzing the information. 【0431】 A "database" is an information system used to organize and securely store received attribute information. 【0432】 A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and evaluate the frequency and importance of clothing use. 【0433】 "Disposal methods" refer to ways of dealing with clothing, such as recycling, reuse, or selling it on the market. 【0434】 "Market value of similar items" refers to market price information for other products with similar attributes to the clothing item in question, and is used as a reference when users sell or trade clothing. 【0435】 "Means for exchanging data with data systems" refers to communication means for exchanging information with other platforms and applications in order to implement the proposed disposal method. 【0436】 This invention provides a system for efficiently managing a user's clothing. This system primarily consists of a terminal, a server, and a generative AI model. Specific embodiments are described below. 【0437】 First, users use their devices to input detailed attribute information about their clothing. Specifically, they can register information such as the purchase date, price, number of times worn, and weather conditions. This allows users to accurately understand how their clothing is being used. 【0438】 Next, the terminal structures the input information as digital data and sends it to the server via the network. Universal data formats such as JSON are often used in this process. Furthermore, compliance with security protocols ensures data integrity and protection. 【0439】 After receiving the data, the server stores it in a database for subsequent analysis. The server's internal generative AI model is used for data analysis. This model employs machine learning algorithms to evaluate the frequency of use and importance of each garment. For example, a coat that has been worn infrequently over the years is presumed to have decreased value. 【0440】 Based on the analysis results, the server suggests the best way for the user to store or dispose of their clothing. For example, for suits worn infrequently, it can encourage donation to a recycling bin and suggest a selling price at a flea market based on market value. 【0441】 An example of a prompt message is: "This jacket was purchased in March 2021 and has been worn 10 times. Please suggest how to dispose of this garment." This allows the user to make a data-driven, rational decision. 【0442】 This system allows users to efficiently manage their clothing and helps them choose appropriate disposal methods. This promotes sustainable consumption behavior and reduces environmental impact. 【0443】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0444】 Step 1: 【0445】 The user uses the terminal to enter detailed attribute information about the clothing. Specifically, they enter data such as the purchase date, price, number of times worn, and relevant weather conditions through the application screen, and then press the submit button. Once this input is complete, the terminal prepares the data for the next step. 【0446】 Step 2: 【0447】 The terminal structures the attribute information received from the user as digital data. This data is typically in JSON format, a format that facilitates subsequent parsing. The terminal sends the structured data to the server via the internet. During this process, the data is appropriately encoded and security protocols are applied to ensure the integrity of the information. 【0448】 Step 3: 【0449】 The server receives digital data transmitted from the terminal and stores it in the database. Here, the server validates the input data to check for inconsistencies and errors. It then saves the information to the appropriate table in the database, preparing it for analysis. Once the received data is correctly stored, the server proceeds with the analysis process. 【0450】 Step 4: 【0451】 The server uses a generative AI model to perform data analysis. Based on the received clothing attribute information, it uses a machine learning algorithm to evaluate the frequency of use and importance of each garment. This analysis process also references past usage data and market data to predict future usage. The output of the analysis is generated as an evaluation score for each garment. 【0452】 Step 5: 【0453】 Based on the analysis results, the server generates suggestions for how to store or dispose of clothing for the user. Using the evaluation score obtained from the generated AI model as a reference, it presents options such as recycling, reuse, or selling. The suggestions also include the prices of similar items based on market data, which are presented as reference information when selling. 【0454】 Step 6: 【0455】 The server sends the generated suggestions back to the terminal and notifies the user. The terminal receives the data from the server and displays the suggestions in the user interface. Based on this information, the user can make specific decisions regarding the management and disposal of clothing. 【0456】 (Application Example 1) 【0457】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0458】 In modern times, it has become common for individuals to own a large amount of clothing, which has made clothing management more complex. Many clothes remain unused in closets, without proper use or disposal. This situation can clutter living spaces and potentially encourage overconsumption, thus highlighting the need for efficient clothing management methods. 【0459】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0460】 In this invention, the server includes means for receiving information about clothing entered by the user, means for using an artificial intelligence model to analyze the information and evaluate the frequency of use and necessity of the clothing, and means for proposing a method of disposal or storage of the clothing based on the evaluation results. This enables efficient registration and management of clothing using new image recognition technology, and allows for suggestions based on the frequency of use and necessity of individual clothing items from among a large number of items. 【0461】 "User" refers to an individual or user who utilizes the clothing management system. 【0462】 "Information" refers to detailed data related to clothing, including purchase date, price, number of times worn, and other relevant information. 【0463】 An "artificial intelligence model" is a system designed using machine learning algorithms to analyze the frequency and necessity of clothing use. 【0464】 "Suggestions" refer to specific advice provided to users based on the analysis results regarding how to dispose of and store clothing. 【0465】 "Market" refers to an economic space or flea market where similar goods are traded. 【0466】 "Image recognition technology" is a technology used to extract specific information from image data, and is used for registering and classifying clothing. 【0467】 "Application software" refers to computer programs that run systems and provide user interfaces. 【0468】 A "platform" is a foundational system or application that provides specific functions or services. 【0469】 This invention is a system for managing clothing and suggesting optimal disposal or storage methods. The system consists of user terminals, servers, and a cloud architecture. 【0470】 The user's device is a hardware device such as a smartphone, tablet, or home robot. This device uses image recognition technology to scan the user's clothing and provides a means to input relevant information such as the purchase date and number of times worn. For example, TensorFlow is used to classify images of clothing and register the information in a database. 【0471】 The server is located in the cloud and processes information using cloud computing services such as AWS Lambda. It receives clothing data submitted by users and performs data analysis using an internal artificial intelligence model. This model is built using machine learning frameworks such as PyTorch and evaluates the frequency and necessity of clothing use. Based on the analysis results, it suggests specific disposal and storage methods to the user. 【0472】 Based on the suggestions provided, users can decide whether to donate or sell unwanted clothing. The suggestions include pricing information for similar items based on market research, allowing users to set appropriate prices. For example, if a user hasn't worn a jacket in their closet for more than three months, they will be presented with the option to donate or sell it, along with its market value. 【0473】 As a concrete example, when a user scans their summer shirt and registers it in the system, the AI ​​model can analyze the usage frequency data and recommend donations. Furthermore, it can confirm the user's intentions using prompts such as, "Have you worn this shirt recently? Do you plan to wear it again?" 【0474】 In this way, the system can efficiently manage clothing and support sustainable consumption. 【0475】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0476】 Step 1: 【0477】 The user's device captures images of clothing via its camera. The user enters detailed information such as the purchase date and price. The entered information is organized as image data and text data and sent to a database. 【0478】 Step 2: 【0479】 The server analyzes image and text data received from the database. Image recognition technology is used to identify clothing items, and machine learning algorithms evaluate factors such as frequency of wear and type. The resulting data is output as a score reflecting the user's clothing usage. 【0480】 Step 3: 【0481】 The server generates management suggestions for each garment based on the evaluation results. Using market price information and usage frequency data, specific action plans are created that suggest disposal and storage methods. These suggestions are stored in a database and notified to the user's terminal. 【0482】 Step 4: 【0483】 The user's device displays suggestions sent from the server and provides an interface for selecting specific actions. Users can choose to donate or sell items at a flea market, and their selections are returned to the server. 【0484】 Step 5: 【0485】 The server notifies the corresponding platform of the necessary data in order to perform actions based on the user's selection. For example, a process is initiated to contact a recycling company according to the selected donation method. 【0486】 Through the above processing steps, users can efficiently manage their clothing, enabling them to either continue using it or dispose of it properly. 【0487】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0488】 This invention is a system that personalizes suggestions to users by combining emotion recognition technology with suggestions for clothing management and disposal methods. The system receives clothing information from the user and, based on the analysis results, suggests disposal methods. Furthermore, by using an emotion engine that recognizes the user's emotional state, the system can flexibly adapt the suggestions to the user's emotions. 【0489】 First, the user uses a device to enter information related to their clothing. This information includes basic data such as the purchase date, price, number of times worn, and weather conditions. The device then sends this information to the server. 【0490】 The server receives the transmitted information and uses an artificial intelligence model to analyze the frequency and necessity of clothing use. The results of the analysis are quantified, and a score is generated to determine how often the clothing is actually used and how necessary it is. 【0491】 Furthermore, the server uses an emotion engine to analyze the user's emotions based on their voice tone and facial expressions. The analysis results are used to determine the user's emotional state at that moment (e.g., happiness, sadness, stress, etc.). This allows the server to adjust its suggestions according to the user's emotions. 【0492】 As a concrete example, consider a scenario where a user is using a device equipped with a camera and microphone for emotion recognition, and unconsciously reveals their emotions after entering clothing information. In this case, if the emotion engine detects that the user is experiencing stress, the system will provide messages to alleviate the emotions and suggestions to promote relaxation, in addition to the default suggestions. For example, "Considering your current stress level, we recommend that you first enjoy your favorite clothing." 【0493】 This invention enables flexible suggestions that take into account the user's emotional state, streamlining clothing management and improving the user experience. This system provides concrete means to support sustainable clothing management while paying greater attention to the user's emotions. 【0494】 The following describes the processing flow. 【0495】 Step 1: 【0496】 The user uses a terminal to enter information about their clothing. They enter data such as the purchase date, price, number of times worn, and weather conditions, and then press the submit button to prepare the data for sending to the server. 【0497】 Step 2: 【0498】 The terminal saves the entered information to a database and sends it to the server using a secure protocol. User input data is transferred to the server in the format necessary for analysis. 【0499】 Step 3: 【0500】 The server receives data sent from the terminal and prepares to input the data into the artificial intelligence model. Preprocessing is performed to analyze the registered clothing information. 【0501】 Step 4: 【0502】 The server uses an artificial intelligence model to analyze the received data and generate a score that evaluates the frequency of use and necessity of clothing. This score is an important indicator that influences suggestions for disposing of or storing clothing. 【0503】 Step 5: 【0504】 On the other hand, the device collects the user's voice tone and facial expression data. The device uses its camera and microphone to send this data to the emotion engine. 【0505】 Step 6: 【0506】 The server uses an emotion engine to analyze the user's emotional state. It recognizes the user's emotional state (happiness, sadness, stress, etc.) and incorporates the analysis results into the disposal suggestions. 【0507】 Step 7: 【0508】 The server integrates the output of the artificial intelligence model with the analysis results of the emotion engine to generate suggestions for the optimal disposal method and pricing for the user. These suggestions are tailored to take the user's emotional state into consideration. 【0509】 Step 8: 【0510】 The generated suggestions are sent to the device and presented to the user visually or audibly. Based on the presented information, the user can decide on their actions and proceed to the next step. 【0511】 (Example 2) 【0512】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0513】 In recent years, as the amount of personal possessions has increased, there has been a growing need to properly manage them and choose appropriate disposal methods as needed. However, there is no system in place that takes into account not only the value and usage of the items but also the owner's emotional state when making suggestions, making it difficult to provide flexible proposals that meet individual needs. 【0514】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0515】 In this invention, the server includes means for receiving information about items entered by the user, means for using a machine learning model to analyze the information and evaluate the frequency and necessity of using the items, and means for recognizing the user's emotional state from voice and image data and adjusting the suggestions based on that emotional state. This enables flexible and appropriate suggestions that take into account the usage status of the items and the owner's emotions. 【0516】 A "user" is an individual or legal entity that uses this system to input information about goods and receives management and suggestions. 【0517】 "Items" refer to specific objects that users input into the system and which are the subject of management and suggestions. 【0518】 "Information" includes detailed data about an item, such as the purchase date, price, number of uses, and weather information. 【0519】 A "machine learning model" is a collection of artificially constructed algorithms used to analyze information about an item and evaluate its frequency of use and necessity. 【0520】 "Emotional state" refers to the psychological and emotional state identified based on data such as the user's voice tone and facial expressions. 【0521】 "Audio and image data" refers to audio and visual data collected to identify the user's emotional state. 【0522】 "Suggestions" refer to information and recommended actions that the system provides to the user regarding how to dispose of or store items. 【0523】 A "digital platform" refers to a collection of online resources and services used for the disposal and coordination of goods. 【0524】 To implement this invention, the following processes are performed via the user, terminal, and server. First, the user uses the terminal to input information about the items they own. This information includes the purchase date, price, number of uses, and related weather information. This information is transmitted from the terminal to the server. 【0525】 The server analyzes the received information using a machine learning model. This analysis utilizes programming languages ​​such as Python and R, as well as data analysis software libraries such as NumPy and Pandas. The machine learning model evaluates the frequency of use and necessity of items, and scores the results. The resulting score is an important indicator of how often or how necessary an item is. 【0526】 In addition, the server uses emotion recognition technology to analyze the user's emotional state from their voice tone and facial expressions. This process utilizes machine learning frameworks such as TensorFlow and PyTorch to process audio and image data. For example, if the user's voice is unstable or their facial expression shows signs of fatigue, the system will determine that the user is experiencing stress. 【0527】 Based on the analyzed data, the server generates suggestions regarding the disposal and storage of items, taking into account the user's emotional state. A generative AI model is used to generate these suggestions, presenting the user with personalized messages. An example of a prompt might be, "Consider the user's emotional state and generate suggestions to alleviate stress." 【0528】 For example, if a user inputs information about clothing and the device shows a significant stress level, the server will generate a suggestion such as, "Considering your current stress level, we recommend that you first enjoy your favorite clothing," and provide feedback to the user through the device. 【0529】 In this way, the present invention aims to provide flexible solutions that meet user needs, make the management of goods more efficient, and improve the owner's experience. 【0530】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0531】 Step 1: 【0532】 The user uses a terminal to input information about items. Specific input data includes purchase date, price, usage count, and weather information. On the input screen, the user enters the required information for each item and, after confirmation, presses the submit button. This action causes the terminal to compile the entered information into a data packet and send it to the server. 【0533】 Step 2: 【0534】 The server receives data packets sent from the terminal. After verifying the accuracy of the received data, the server stores information about each item in its database. This data is then ready for subsequent analysis. After being stored in the database, a confirmation response is sent to the terminal indicating that the data is ready for analysis. 【0535】 Step 3: 【0536】 The server uses machine learning models to analyze data. Using the item information received as input data, it first formats and cleans the data using libraries such as NumPy and Pandas. Then, the formatted data is input into a generating AI model to generate scores based on the frequency of use and necessity of each item. These scores are then saved again in the database. 【0537】 Step 4: 【0538】 The server begins processing to recognize the user's emotional state from audio and image data. The device sends the collected audio tone and facial image data to the server for emotion recognition. The server analyzes this data using TensorFlow or PyTorch to determine the user's emotional state. The analysis results are stored as a numerical representation of the user's emotions. 【0539】 Step 5: 【0540】 The server combines the item's usage frequency score and the user's emotional state score to generate a suggestion for disposal or storage. A generative AI model forms appropriate suggestions in natural language based on the input prompt. For example, a suggestion might be presented as, "Considering your stress level, we recommend enjoying your favorite items first." 【0541】 Step 6: 【0542】 The server sends the generated suggestion text to the terminal. The terminal displays the received suggestion to the user and provides voice guidance as needed. The user reviews the suggestion on the screen and uses it as a reference when choosing their next action. This feedback also contributes to improvements for the next data analysis. 【0543】 (Application Example 2) 【0544】 Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0545】 In modern society, the amount of clothing owned has increased, making its management more complex. This makes it difficult for users to decide which clothes to keep and which to discard. Furthermore, the lack of flexible suggestions that take into account the user's emotions and mental state makes clothing management even more inconvenient and stressful. To address this challenge, a system is needed that streamlines clothing management and provides personalized suggestions. 【0546】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0547】 In this invention, the server includes means for receiving clothing data entered by the user, means for using a machine learning model to analyze the data and evaluate the clothing usage habits and importance, and means for analyzing the user's emotional state using emotion recognition technology and adapting the suggested content to the user's emotions. This optimizes decision-making in clothing management and enables suggestions that take the user's emotions into consideration. 【0548】 "Clothing data" refers to the collective information about clothing entered by the user, such as the purchase date, price, frequency of wear, and weather information. 【0549】 A "machine learning model" is an artificial intelligence algorithm used to evaluate clothing usage habits and importance. 【0550】 "Emotion recognition technology" is a technology that determines a user's emotional state by analyzing their voice tone and facial expressions. 【0551】 "Means of adapting suggested content to the user's emotions" refers to a method of customizing clothing management suggestions according to the user's emotions, based on emotion recognition technology. 【0552】 "Suggestions to promote relaxation" refer to suggestions made to alleviate the user's burden when it is determined that the user is experiencing stress. 【0553】 "Other systems" refers to external platforms or services that are used in conjunction with the disposal or reuse of clothing using the selected processing method. 【0554】 In this invention, a server plays a central role in the system for managing clothing. Users input information about their clothing into the server from a device such as a smartphone or tablet. This includes the purchase date, price, frequency of wear, and weather information. The server receives this information and uses a machine learning model (for example, a model using Python and the scikit-learn library) to evaluate the clothing's usage habits and importance. 【0555】 Furthermore, the server uses the camera and microphone on the device to capture the user's voice tone and facial expressions, and analyzes the user's emotions using emotion recognition technology (e.g., an emotion recognition model using the TensorFlow library). Based on the emotion evaluation, the server adapts suggestions regarding clothing management to the user's emotions. 【0556】 As a result, the generated suggestions can go beyond mere functional suggestions and include relaxation-promoting suggestions that take into account the user's emotions and mental state. For example, if the user is feeling stressed, the system might display a message such as, "Considering your current stress level, we recommend you take a break for a while. Afterwards, we can check your clothes together." 【0557】 Example prompt: "Consider the user's current emotional state and generate optimal clothing management suggestions. How is the user feeling?" 【0558】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0559】 Step 1: 【0560】 The user enters clothing data (purchase date, price, frequency of wear, and weather information) using a terminal. This input data is sent to the server. The server verifies the received data and stores it in a database. 【0561】 Step 2: 【0562】 The server inputs clothing data stored in a database into a machine learning model to evaluate clothing usage habits and importance. The model analyzes the input data and outputs a score indicating the frequency and importance of clothing use. This score serves as an indicator of how often clothing is used or needs to be stored. 【0563】 Step 3: 【0564】 The user provides emotional data to the server through the device's camera and microphone. The server inputs this data into an emotion recognition model to analyze the user's current emotional state. This process analyzes voice tone and facial expression data to evaluate what the user is feeling. 【0565】 Step 4: 【0566】 The server integrates clothing usage scores with the user's emotional state to generate optimal suggestions. These suggestions are tailored to the user's mood; for example, a user experiencing stress might receive suggestions promoting relaxation. The server then returns these results to the user's device, displaying more detailed suggestions. 【0567】 Step 5: 【0568】 Depending on the user's selection, the server assists in executing the proposed processing method. For example, if the user chooses to donate clothing, the server will coordinate with the appropriate platform to proceed with the process. It will also provide market price information as needed, presenting the user with the best possible option. 【0569】 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. 【0570】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0571】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0572】 [Fourth Embodiment] 【0573】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0574】 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. 【0575】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0576】 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. 【0577】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0578】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0579】 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. 【0580】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0581】 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. 【0582】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0583】 The 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. 【0584】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0585】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0586】 This invention is a system designed for efficient clothing management. The system primarily aims to accurately track information about the user's clothing, analyze its frequency of use and necessity, and then propose appropriate disposal methods. Users input information about their clothing using a terminal. Specifically, by registering information such as purchase date, price, number of times worn, and weather conditions, the system gains a detailed understanding of the clothing's usage. 【0587】 The terminal sends information entered by the user to the server. After receiving this information, the server analyzes the data using an internal artificial intelligence model. This model utilizes machine learning algorithms to evaluate the frequency of use and importance of each registered garment. The evaluation results are calculated as a score based on the usage patterns and future usage predictions for each garment. 【0588】 After the analysis is complete, the server generates suggestions for each garment. These suggestions include whether the garment needs to be disposed of and recommendations for storage. The suggestions also include price information for similar garments based on market transaction data, helping users set appropriate prices when selling on a flea market application. 【0589】 As a concrete example, consider a case where a user registers a suit that was purchased more than two years ago and worn five times or less. Analysis reveals that this suit has been used very little and is likely to be used infrequently in the future. The server encourages donation to a recycling bin and suggests a fair price based on market research for selling it on a flea market application. In this way, users can choose the best way to dispose of their unwanted clothing based on the server's suggestions. 【0590】 The system promotes environmental considerations through clothing management and supports user decision-making, thereby helping consumers practice sustainable living. Through this process, this invention provides a practical means for consumers to manage their clothing efficiently and effectively. 【0591】 The following describes the processing flow. 【0592】 Step 1: 【0593】 Users input clothing information using their devices. Specifically, users enter data such as the purchase date, price, number of times worn, and weather conditions into a dedicated input form. This information is registered for each garment, and users can confirm and submit it as instructed. 【0594】 Step 2: 【0595】 The terminal temporarily stores the clothing information entered by the user and then transmits it to the server via the internet. Security is considered during this process, and encrypted communication protocols such as HTTPS are used. 【0596】 Step 3: 【0597】 The server receives data sent from the terminal and records it in a database. It then prepares the recorded data for use as input for an artificial intelligence model. 【0598】 Step 4: 【0599】 The server analyzes the received data using an artificial intelligence model. Based on the registered information, the model evaluates the frequency of clothing use and calculates a score to determine its necessity. This score is then used in subsequent suggestion generation. 【0600】 Step 5: 【0601】 The server generates suggestions for the user based on the analysis results. Specifically, these suggestions include the need to declutter clothing, appropriate disposal methods, the possibility of donating to a recycling bin, and recommended selling prices on flea market applications. 【0602】 Step 6: 【0603】 The server sends the generated proposal to the terminal. The terminal receives it and displays the proposal to the user in an easy-to-understand format. The user then decides what action to take based on the information provided. 【0604】 Step 7: 【0605】 If the user chooses to take action according to the suggestion, the device will initiate the next step, depending on the choice, such as listing an item on a flea market application or donating it to a recycling box. The server updates the status and records the results to confirm that these actions have been completed. 【0606】 (Example 1) 【0607】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0608】 Currently, many consumers lack efficient ways to manage their clothing, resulting in an increase in unused garments, wasted expenses, and environmental burdens. Furthermore, a lack of information on appropriate disposal methods often leads consumers to make unhelpful decisions. Against this backdrop, there is a need for efficient and effective methods to propose clothing management and disposal strategies. 【0609】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0610】 In this invention, the server includes means for acquiring attribute information about clothing entered by the user, means for transferring the attribute information to an information processing device via a network, and means for evaluating the frequency of use and importance of clothing based on the attribute information using an internal generating AI model. This makes it possible to propose appropriate management and efficient disposal methods for clothing. 【0611】 A "user" is an individual or organization that inputs clothing attribute information into the system. 【0612】 "Clothing attribute information" refers to data such as purchase date, price, number of times worn, and weather conditions, and is necessary information for evaluating the usage status of clothing. 【0613】 "Means of transferring information to an information processing device via a network" refers to a communication method for sending data from a terminal to a server and analyzing the information. 【0614】 A "database" is an information system used to organize and securely store received attribute information. 【0615】 A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and evaluate the frequency and importance of clothing use. 【0616】 "Disposal methods" refer to ways of dealing with clothing, such as recycling, reuse, or selling it on the market. 【0617】 "Market value of similar items" refers to market price information for other products with similar attributes to the clothing item in question, and is used as a reference when users sell or trade clothing. 【0618】 "Means for exchanging data with data systems" refers to communication means for exchanging information with other platforms and applications in order to implement the proposed disposal method. 【0619】 This invention provides a system for efficiently managing a user's clothing. This system primarily consists of a terminal, a server, and a generative AI model. Specific embodiments are described below. 【0620】 First, users use their devices to input detailed attribute information about their clothing. Specifically, they can register information such as the purchase date, price, number of times worn, and weather conditions. This allows users to accurately understand how their clothing is being used. 【0621】 Next, the terminal structures the input information as digital data and sends it to the server via the network. Universal data formats such as JSON are often used in this process. Furthermore, compliance with security protocols ensures data integrity and protection. 【0622】 After receiving the data, the server stores it in a database for subsequent analysis. The server's internal generative AI model is used for data analysis. This model employs machine learning algorithms to evaluate the frequency of use and importance of each garment. For example, a coat that has been worn infrequently over the years is presumed to have decreased value. 【0623】 Based on the analysis results, the server suggests the best way for the user to store or dispose of their clothing. For example, for suits worn infrequently, it can encourage donation to a recycling bin and suggest a selling price at a flea market based on market value. 【0624】 An example of a prompt message is: "This jacket was purchased in March 2021 and has been worn 10 times. Please suggest how to dispose of this garment." This allows the user to make a data-driven, rational decision. 【0625】 This system allows users to efficiently manage their clothing and helps them choose appropriate disposal methods. This promotes sustainable consumption behavior and reduces environmental impact. 【0626】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0627】 Step 1: 【0628】 The user uses the terminal to enter detailed attribute information about the clothing. Specifically, they enter data such as the purchase date, price, number of times worn, and relevant weather conditions through the application screen, and then press the submit button. Once this input is complete, the terminal prepares the data for the next step. 【0629】 Step 2: 【0630】 The terminal structures the attribute information received from the user as digital data. This data is typically in JSON format, a format that facilitates subsequent parsing. The terminal sends the structured data to the server via the internet. During this process, the data is appropriately encoded and security protocols are applied to ensure the integrity of the information. 【0631】 Step 3: 【0632】 The server receives digital data transmitted from the terminal and stores it in the database. Here, the server validates the input data to check for inconsistencies and errors. It then saves the information to the appropriate table in the database, preparing it for analysis. Once the received data is correctly stored, the server proceeds with the analysis process. 【0633】 Step 4: 【0634】 The server uses a generative AI model to perform data analysis. Based on the received clothing attribute information, it uses a machine learning algorithm to evaluate the frequency of use and importance of each garment. This analysis process also references past usage data and market data to predict future usage. The output of the analysis is generated as an evaluation score for each garment. 【0635】 Step 5: 【0636】 Based on the analysis results, the server generates suggestions for how to store or dispose of clothing for the user. Using the evaluation score obtained from the generated AI model as a reference, it presents options such as recycling, reuse, or selling. The suggestions also include the prices of similar items based on market data, which are presented as reference information when selling. 【0637】 Step 6: 【0638】 The server sends the generated suggestions back to the terminal and notifies the user. The terminal receives the data from the server and displays the suggestions in the user interface. Based on this information, the user can make specific decisions regarding the management and disposal of clothing. 【0639】 (Application Example 1) 【0640】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0641】 In modern times, it has become common for individuals to own a large amount of clothing, which has made clothing management more complex. Many clothes remain unused in closets, without proper use or disposal. This situation can clutter living spaces and potentially encourage overconsumption, thus highlighting the need for efficient clothing management methods. 【0642】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0643】 In this invention, the server includes means for receiving information about clothing entered by the user, means for using an artificial intelligence model to analyze the information and evaluate the frequency of use and necessity of the clothing, and means for proposing a method of disposal or storage of the clothing based on the evaluation results. This enables efficient registration and management of clothing using new image recognition technology, and allows for suggestions based on the frequency of use and necessity of individual clothing items from among a large number of items. 【0644】 "User" refers to an individual or user who utilizes the clothing management system. 【0645】 "Information" refers to detailed data related to clothing, including purchase date, price, number of times worn, and other relevant information. 【0646】 An "artificial intelligence model" is a system designed using machine learning algorithms to analyze the frequency and necessity of clothing use. 【0647】 "Suggestions" refer to specific advice provided to users based on the analysis results regarding how to dispose of and store clothing. 【0648】 "Market" refers to an economic space or flea market where similar goods are traded. 【0649】 "Image recognition technology" is a technology used to extract specific information from image data, and is used for registering and classifying clothing. 【0650】 "Application software" refers to computer programs that run systems and provide user interfaces. 【0651】 A "platform" is a foundational system or application that provides specific functions or services. 【0652】 This invention is a system for managing clothing and suggesting optimal disposal or storage methods. The system consists of user terminals, servers, and a cloud architecture. 【0653】 The user's device is a hardware device such as a smartphone, tablet, or home robot. This device uses image recognition technology to scan the user's clothing and provides a means to input relevant information such as the purchase date and number of times worn. For example, TensorFlow is used to classify images of clothing and register the information in a database. 【0654】 The server is located in the cloud and processes information using cloud computing services such as AWS Lambda. It receives clothing data submitted by users and performs data analysis using an internal artificial intelligence model. This model is built using machine learning frameworks such as PyTorch and evaluates the frequency and necessity of clothing use. Based on the analysis results, it suggests specific disposal and storage methods to the user. 【0655】 Based on the suggestions provided, users can decide whether to donate or sell unwanted clothing. The suggestions include pricing information for similar items based on market research, allowing users to set appropriate prices. For example, if a user hasn't worn a jacket in their closet for more than three months, they will be presented with the option to donate or sell it, along with its market value. 【0656】 As a concrete example, when a user scans their summer shirt and registers it in the system, the AI ​​model can analyze the usage frequency data and recommend donations. Furthermore, it can confirm the user's intentions using prompts such as, "Have you worn this shirt recently? Do you plan to wear it again?" 【0657】 In this way, the system can efficiently manage clothing and support sustainable consumption. 【0658】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0659】 Step 1: 【0660】 The user's device captures images of clothing via its camera. The user enters detailed information such as the purchase date and price. The entered information is organized as image data and text data and sent to a database. 【0661】 Step 2: 【0662】 The server analyzes image and text data received from the database. Image recognition technology is used to identify clothing items, and machine learning algorithms evaluate factors such as frequency of wear and type. The resulting data is output as a score reflecting the user's clothing usage. 【0663】 Step 3: 【0664】 The server generates management suggestions for each garment based on the evaluation results. Using market price information and usage frequency data, specific action plans are created that suggest disposal and storage methods. These suggestions are stored in a database and notified to the user's terminal. 【0665】 Step 4: 【0666】 The user's device displays suggestions sent from the server and provides an interface for selecting specific actions. Users can choose to donate or sell items at a flea market, and their selections are returned to the server. 【0667】 Step 5: 【0668】 The server notifies the corresponding platform of the necessary data in order to perform actions based on the user's selection. For example, a process is initiated to contact a recycling company according to the selected donation method. 【0669】 Through the above processing steps, users can efficiently manage their clothing, enabling them to either continue using it or dispose of it properly. 【0670】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0671】 This invention is a system that personalizes suggestions to users by combining emotion recognition technology with suggestions for clothing management and disposal methods. The system receives clothing information from the user and, based on the analysis results, suggests disposal methods. Furthermore, by using an emotion engine that recognizes the user's emotional state, the system can flexibly adapt the suggestions to the user's emotions. 【0672】 First, the user uses a device to enter information related to their clothing. This information includes basic data such as the purchase date, price, number of times worn, and weather conditions. The device then sends this information to the server. 【0673】 The server receives the transmitted information and uses an artificial intelligence model to analyze the frequency and necessity of clothing use. The results of the analysis are quantified, and a score is generated to determine how often the clothing is actually used and how necessary it is. 【0674】 Furthermore, the server uses an emotion engine to analyze the user's emotions based on their voice tone and facial expressions. The analysis results are used to determine the user's emotional state at that moment (e.g., happiness, sadness, stress, etc.). This allows the server to adjust its suggestions according to the user's emotions. 【0675】 As a concrete example, consider a scenario where a user is using a device equipped with a camera and microphone for emotion recognition, and unconsciously reveals their emotions after entering clothing information. In this case, if the emotion engine detects that the user is experiencing stress, the system will provide messages to alleviate the emotions and suggestions to promote relaxation, in addition to the default suggestions. For example, "Considering your current stress level, we recommend that you first enjoy your favorite clothing." 【0676】 This invention enables flexible suggestions that take into account the user's emotional state, streamlining clothing management and improving the user experience. This system provides concrete means to support sustainable clothing management while paying greater attention to the user's emotions. 【0677】 The following describes the processing flow. 【0678】 Step 1: 【0679】 The user uses a terminal to enter information about their clothing. They enter data such as the purchase date, price, number of times worn, and weather conditions, and then press the submit button to prepare the data for sending to the server. 【0680】 Step 2: 【0681】 The terminal saves the entered information to a database and sends it to the server using a secure protocol. User input data is transferred to the server in the format necessary for analysis. 【0682】 Step 3: 【0683】 The server receives data sent from the terminal and prepares to input the data into the artificial intelligence model. Preprocessing is performed to analyze the registered clothing information. 【0684】 Step 4: 【0685】 The server uses an artificial intelligence model to analyze the received data and generate a score that evaluates the frequency of use and necessity of clothing. This score is an important indicator that influences suggestions for disposing of or storing clothing. 【0686】 Step 5: 【0687】 On the other hand, the device collects the user's voice tone and facial expression data. The device uses its camera and microphone to send this data to the emotion engine. 【0688】 Step 6: 【0689】 The server uses an emotion engine to analyze the user's emotional state. It recognizes the user's emotional state (happiness, sadness, stress, etc.) and incorporates the analysis results into the disposal suggestions. 【0690】 Step 7: 【0691】 The server integrates the output of the artificial intelligence model with the analysis results of the emotion engine to generate suggestions for the optimal disposal method and pricing for the user. These suggestions are tailored to take the user's emotional state into consideration. 【0692】 Step 8: 【0693】 The generated suggestions are sent to the device and presented to the user visually or audibly. Based on the presented information, the user can decide on their actions and proceed to the next step. 【0694】 (Example 2) 【0695】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0696】 In recent years, as the amount of personal possessions has increased, there has been a growing need to properly manage them and choose appropriate disposal methods as needed. However, there is no system in place that takes into account not only the value and usage of the items but also the owner's emotional state when making suggestions, making it difficult to provide flexible proposals that meet individual needs. 【0697】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0698】 In this invention, the server includes means for receiving information about items entered by the user, means for using a machine learning model to analyze the information and evaluate the frequency and necessity of using the items, and means for recognizing the user's emotional state from voice and image data and adjusting the suggestions based on that emotional state. This enables flexible and appropriate suggestions that take into account the usage status of the items and the owner's emotions. 【0699】 A "user" is an individual or legal entity that uses this system to input information about goods and receives management and suggestions. 【0700】 "Items" refer to specific objects that users input into the system and which are the subject of management and suggestions. 【0701】 "Information" includes detailed data about an item, such as the purchase date, price, number of uses, and weather information. 【0702】 A "machine learning model" is a collection of artificially constructed algorithms used to analyze information about an item and evaluate its frequency of use and necessity. 【0703】 "Emotional state" refers to the psychological and emotional state identified based on data such as the user's voice tone and facial expressions. 【0704】 "Audio and image data" refers to audio and visual data collected to identify the user's emotional state. 【0705】 "Suggestions" refer to information and recommended actions that the system provides to the user regarding how to dispose of or store items. 【0706】 A "digital platform" refers to a collection of online resources and services used for the disposal and coordination of goods. 【0707】 To implement this invention, the following processes are performed via the user, terminal, and server. First, the user uses the terminal to input information about the items they own. This information includes the purchase date, price, number of uses, and related weather information. This information is transmitted from the terminal to the server. 【0708】 The server analyzes the received information using a machine learning model. This analysis utilizes programming languages ​​such as Python and R, as well as data analysis software libraries such as NumPy and Pandas. The machine learning model evaluates the frequency of use and necessity of items, and scores the results. The resulting score is an important indicator of how often or how necessary an item is. 【0709】 In addition, the server uses emotion recognition technology to analyze the user's emotional state from their voice tone and facial expressions. This process utilizes machine learning frameworks such as TensorFlow and PyTorch to process audio and image data. For example, if the user's voice is unstable or their facial expression shows signs of fatigue, the system will determine that the user is experiencing stress. 【0710】 Based on the analyzed data, the server generates suggestions regarding the disposal and storage of items, taking into account the user's emotional state. A generative AI model is used to generate these suggestions, presenting the user with personalized messages. An example of a prompt might be, "Consider the user's emotional state and generate suggestions to alleviate stress." 【0711】 For example, if a user inputs information about clothing and the device shows a significant stress level, the server will generate a suggestion such as, "Considering your current stress level, we recommend that you first enjoy your favorite clothing," and provide feedback to the user through the device. 【0712】 In this way, the present invention aims to provide flexible solutions that meet user needs, make the management of goods more efficient, and improve the owner's experience. 【0713】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0714】 Step 1: 【0715】 The user uses a terminal to input information about items. Specific input data includes purchase date, price, usage count, and weather information. On the input screen, the user enters the required information for each item and, after confirmation, presses the submit button. This action causes the terminal to compile the entered information into a data packet and send it to the server. 【0716】 Step 2: 【0717】 The server receives data packets sent from the terminal. After verifying the accuracy of the received data, the server stores information about each item in its database. This data is then ready for subsequent analysis. After being stored in the database, a confirmation response is sent to the terminal indicating that the data is ready for analysis. 【0718】 Step 3: 【0719】 The server uses machine learning models to analyze data. Using the item information received as input data, it first formats and cleans the data using libraries such as NumPy and Pandas. Then, the formatted data is input into a generating AI model to generate scores based on the frequency of use and necessity of each item. These scores are then saved again in the database. 【0720】 Step 4: 【0721】 The server begins processing to recognize the user's emotional state from audio and image data. The device sends the collected audio tone and facial image data to the server for emotion recognition. The server analyzes this data using TensorFlow or PyTorch to determine the user's emotional state. The analysis results are stored as a numerical representation of the user's emotions. 【0722】 Step 5: 【0723】 The server combines the item's usage frequency score and the user's emotional state score to generate a suggestion for disposal or storage. A generative AI model forms appropriate suggestions in natural language based on the input prompt. For example, a suggestion might be presented as, "Considering your stress level, we recommend enjoying your favorite items first." 【0724】 Step 6: 【0725】 The server sends the generated suggestion text to the terminal. The terminal displays the received suggestion to the user and provides voice guidance as needed. The user reviews the suggestion on the screen and uses it as a reference when choosing their next action. This feedback also contributes to improvements for the next data analysis. 【0726】 (Application Example 2) 【0727】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0728】 In modern society, the amount of clothing owned has increased, making its management more complex. This makes it difficult for users to decide which clothes to keep and which to discard. Furthermore, the lack of flexible suggestions that take into account the user's emotions and mental state makes clothing management even more inconvenient and stressful. To address this challenge, a system is needed that streamlines clothing management and provides personalized suggestions. 【0729】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0730】 In this invention, the server includes means for receiving clothing data entered by the user, means for using a machine learning model to analyze the data and evaluate the clothing usage habits and importance, and means for analyzing the user's emotional state using emotion recognition technology and adapting the suggested content to the user's emotions. This optimizes decision-making in clothing management and enables suggestions that take the user's emotions into consideration. 【0731】 "Clothing data" refers to the collective information about clothing entered by the user, such as the purchase date, price, frequency of wear, and weather information. 【0732】 A "machine learning model" is an artificial intelligence algorithm used to evaluate clothing usage habits and importance. 【0733】 "Emotion recognition technology" is a technology that determines a user's emotional state by analyzing their voice tone and facial expressions. 【0734】 "Means of adapting suggested content to the user's emotions" refers to a method of customizing clothing management suggestions according to the user's emotions, based on emotion recognition technology. 【0735】 "Suggestions to promote relaxation" refer to suggestions made to alleviate the user's burden when it is determined that the user is experiencing stress. 【0736】 "Other systems" refers to external platforms or services that are used in conjunction with the disposal or reuse of clothing using the selected processing method. 【0737】 In this invention, a server plays a central role in the system for managing clothing. Users input information about their clothing into the server from a device such as a smartphone or tablet. This includes the purchase date, price, frequency of wear, and weather information. The server receives this information and uses a machine learning model (for example, a model using Python and the scikit-learn library) to evaluate the clothing's usage habits and importance. 【0738】 Furthermore, the server uses the camera and microphone on the device to capture the user's voice tone and facial expressions, and analyzes the user's emotions using emotion recognition technology (e.g., an emotion recognition model using the TensorFlow library). Based on the emotion evaluation, the server adapts suggestions regarding clothing management to the user's emotions. 【0739】 As a result, the generated suggestions can go beyond mere functional suggestions and include relaxation-promoting suggestions that take into account the user's emotions and mental state. For example, if the user is feeling stressed, the system might display a message such as, "Considering your current stress level, we recommend you take a break for a while. Afterwards, we can check your clothes together." 【0740】 Example prompt: "Consider the user's current emotional state and generate optimal clothing management suggestions. How is the user feeling?" 【0741】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0742】 Step 1: 【0743】 The user enters clothing data (purchase date, price, frequency of wear, and weather information) using a terminal. This input data is sent to the server. The server verifies the received data and stores it in a database. 【0744】 Step 2: 【0745】 The server inputs clothing data stored in a database into a machine learning model to evaluate clothing usage habits and importance. The model analyzes the input data and outputs a score indicating the frequency and importance of clothing use. This score serves as an indicator of how often clothing is used or needs to be stored. 【0746】 Step 3: 【0747】 The user provides emotional data to the server through the device's camera and microphone. The server inputs this data into an emotion recognition model to analyze the user's current emotional state. This process analyzes voice tone and facial expression data to evaluate what the user is feeling. 【0748】 Step 4: 【0749】 The server integrates clothing usage scores with the user's emotional state to generate optimal suggestions. These suggestions are tailored to the user's mood; for example, a user experiencing stress might receive suggestions promoting relaxation. The server then returns these results to the user's device, displaying more detailed suggestions. 【0750】 Step 5: 【0751】 Depending on the user's selection, the server assists in executing the proposed processing method. For example, if the user chooses to donate clothing, the server will coordinate with the appropriate platform to proceed with the process. It will also provide market price information as needed, presenting the user with the best possible option. 【0752】 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. 【0753】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0754】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0755】 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. 【0756】 Figure 9 shows an 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. 【0757】 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. 【0758】 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. 【0759】 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, motorcycles, etc., 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, for example, based 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. 【0760】 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." 【0761】 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. 【0762】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0763】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0764】 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. 【0765】 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. 【0766】 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. 【0767】 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. 【0768】 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. 【0769】 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. 【0770】 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. 【0771】 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 the like 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. 【0772】 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 as being incorporated by reference. 【0773】 The following is further disclosed regarding the embodiments described above. 【0774】 (Claim 1) 【0775】 A means of receiving clothing information entered by the user, 【0776】 A means of using an artificial intelligence model to analyze the aforementioned information and evaluate the frequency and necessity of clothing use, 【0777】 A means for proposing a method of disposing of or storing clothing based on the evaluation results, 【0778】 A means for providing price information of similar products in the market in accordance with the above proposal, 【0779】 A means of linking the aforementioned clothing to other platforms corresponding to the selected disposal method, 【0780】 A system that includes this. 【0781】 (Claim 2) 【0782】 The system according to claim 1, wherein the artificial intelligence model performs evaluations based on the date of purchase, price, number of times worn, and weather information of the clothing. 【0783】 (Claim 3) 【0784】 The proposed disposal method includes options for donation to a recycling box and sale through a flea market application, according to claim 1. 【0785】 "Example 1" 【0786】 (Claim 1) 【0787】 A means of obtaining attribute information about clothing entered by the user, 【0788】 Means for transferring the attribute information to an information processing device via a network, 【0789】 A means for managing a database to store received attribute information and guarantee its reliability, 【0790】 A means for evaluating the frequency of use and importance of clothing based on the attribute information using an internal generative AI model, 【0791】 A means for determining and proposing a method for storing or disposing of clothing based on the aforementioned evaluation results, 【0792】 A means of calculating the market value of similar products in accordance with the proposed disposal method and providing information, 【0793】 A means of exchanging data with other data systems based on the disposal method selected by the user, 【0794】 A system that includes this. 【0795】 (Claim 2) 【0796】 The system according to claim 1, wherein the generating AI model performs analysis based on the purchase date of clothing, acquisition price, number of times worn, and climate condition data. 【0797】 (Claim 3) 【0798】 The system according to claim 1, wherein the proposed disposal means includes options for donation to a reusable container and exchange in a market data processing application. 【0799】 "Application Example 1" 【0800】 (Claim 1) 【0801】 A means of receiving clothing information entered by the user, 【0802】 A means of using an artificial intelligence model to analyze the aforementioned information and evaluate the frequency and necessity of clothing use, 【0803】 A means for proposing a method of disposing of or storing clothing based on the evaluation results, 【0804】 A means for providing price information of similar products in the market in accordance with the above proposal, 【0805】 A means of linking the aforementioned clothing to other platforms corresponding to the selected disposal method, 【0806】 A means of using image recognition technology to recognize clothing and register information, 【0807】 One method is to use application software to guide you through clothing management as an assistant, 【0808】 A system that includes this. 【0809】 (Claim 2) 【0810】 The system according to claim 1, wherein the artificial intelligence model performs evaluations based on the date of purchase, price, number of times worn, and weather information of the clothing. 【0811】 (Claim 3) 【0812】 The proposed disposal method includes options for donation to a recycling box and sale on a flea market platform, according to claim 1. 【0813】 "Example 2 of combining an emotion engine" 【0814】 (Claim 1) 【0815】 A means of receiving information about items entered by the user, 【0816】 A means of using a machine learning model to analyze the aforementioned information and evaluate the frequency of use and necessity of the items, 【0817】 A means for proposing a method of disposing of or storing the goods based on the evaluation results, 【0818】 A means for recognizing the user's emotional state from voice and image data, and adjusting the proposal based on that emotional state, 【0819】 A means for providing price information of similar products in the market in accordance with the above proposal, 【0820】 A means for linking the aforementioned items to other digital platforms corresponding to the selected disposal method, 【0821】 A system that includes this. 【0822】 (Claim 2) 【0823】 The system according to claim 1, wherein the machine learning model performs evaluations based on the purchase date, price, number of uses, and weather information of an item. 【0824】 (Claim 3) 【0825】 The proposed disposal method includes options for donation to a reusable container and sale in a flea market program, according to claim 1. 【0826】 "Application example 2 when combining with an emotional engine" 【0827】 (Claim 1) 【0828】 A means of receiving clothing data entered by the user, 【0829】 A means for using a machine learning model to analyze the aforementioned data and evaluate clothing usage habits and importance, 【0830】 A means for proposing a method for processing or preserving clothing based on the evaluation results, 【0831】 A means for analyzing the user's emotional state using emotion recognition technology and adapting the proposed content to the user's emotions, 【0832】 A means of making suggestions to promote relaxation, 【0833】 A means for providing price data of similar products in the market in accordance with the above proposal, 【0834】 Means for linking the aforementioned garment to another system corresponding to the selected processing method, 【0835】 A system that includes this. 【0836】 (Claim 2) 【0837】 The system according to claim 1, wherein the machine learning model performs evaluations based on the date of purchase of clothing, price, frequency of wear, and weather information. 【0838】 (Claim 3) 【0839】 The proposed processing method includes the option of providing the materials to a reusable container and selling them in a system for a flea market, according to claim 1. [Explanation of symbols] 【0840】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of receiving clothing information entered by the user, A means of using an artificial intelligence model to analyze the aforementioned information and evaluate the frequency and necessity of clothing use, A means for proposing a method of disposing of or storing clothing based on the evaluation results, A means for providing price information of similar products in the market in accordance with the above proposal, A means of linking the aforementioned clothing to other platforms corresponding to the selected disposal method, A system that includes this. [Claim 2] The system according to claim 1, wherein the artificial intelligence model performs evaluations based on the date of purchase, price, number of times worn, and weather information of the clothing. [Claim 3] The proposed disposal method includes options for donation to a recycling box and sale through a flea market application, according to claim 1.

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