system

Abstract

We provide the system. [Solution] A means for receiving user input and storing the information about clothing entered by the user in a database, A means for analyzing information about clothing stored in a database and evaluating the frequency of use and necessity of that clothing, A means for generating proposals regarding the disposal of the relevant clothing based on the evaluation results of frequency of use and necessity, Means for notifying the user of the aforementioned proposal and supporting the disposal procedure based on the user's choice, A means for automatically acquiring information about clothing using speech recognition and image recognition technology, 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention aims to solve problems related to space issues caused by an increase in the number of clothes owned by an individual and the labor of appropriately disposing of unnecessary clothes. Many individuals purchase clothes without considering limited space management or cost-effectiveness, resulting in storage compression. Furthermore, psychological barriers to throwing away and the labor of selling on free market applications are factors delaying the decluttering of clothes. These factors cause a decline in the quality of individual life and waste of production resources, and it is necessary to solve these problems.

Means for Solving the Problems

[0005] This invention proposes a system that provides an interface for users to input information about their clothing, and evaluates the frequency of use and necessity of clothing by storing and analyzing the input data on a server. This system uses an AI algorithm based on usage frequency and market data to suggest disposal of clothing deemed unnecessary. These suggestions include suggesting appropriate selling prices on flea market applications and providing guidance on donating to recycling centers, enabling users to organize their clothing efficiently and sustainably. In this way, it promotes the effective use of space and resource circulation, bringing benefits to individuals and society as a whole.

[0006] "User input" refers to information provided by the user through the interface, and in this invention, it refers to information such as the purchase date and frequency of use of clothing.

[0007] A "database" is an electronic information aggregation system used to systematically store and manage user-entered information.

[0008] "Analysis" refers to the process of evaluating information stored in a database, specifically the operation of calculating the frequency of clothing use and determining its necessity based on the results.

[0009] "Frequency of use" is an indicator that shows the actual number of times or the usage rate of clothing over a specific period, and is important data for evaluating the necessity of clothing.

[0010] "Necessity assessment" is the process of determining the value of each garment based on its frequency of use and market data.

[0011] A "suggestion" is a recommendation for user action generated based on the analysis results, and includes specific instructions on how to dispose of clothing.

[0012] "Notification" refers to a means of communication used to convey the results of the analysis and the proposed content to the user.

[0013] "Choice" refers to a decision made by the user of their own free will in response to a presented proposal.

[0014] "Disposal procedure" refers to a series of operations or steps taken to carry out the proposed method of disposing of clothing.

[0015] "Support measures" refer to functions that provide assistance to help users smoothly carry out the disposal procedures they have selected. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that evaluates the frequency of use and necessity of clothing based on information about the user's possessions and proposes appropriate disposal methods. This system supports efficient decluttering by allowing users to input clothing information through an easy-to-use interface, sending it to a server for analysis, and so on.

[0038] Configuration and Operation

[0039] 1. User input

[0040] Users access the application using devices such as smartphones or computers and enter information such as the purchase date, frequency of use, and price of their clothing. This information may also include details such as the item name, brand, and condition. The information entered by the user is immediately transmitted to the server from the interface.

[0041] 2. Data Analysis

[0042] The server stores information about received clothing in a database, and then uses an AI algorithm to calculate the frequency of use for each garment and evaluate its necessity. This includes calculating a "frequency of use index" that takes into account factors such as the number of days since purchase, the number of times it has been worn, and the purchase price. Furthermore, the server conducts market research and analyzes how much similar items are selling for at flea markets.

[0043] 3. Generating the proposal

[0044] Based on the data analysis, the server generates specific disposal suggestions for clothing deemed unnecessary. These suggestions include options such as donating to a recycling bin, setting a selling price on a flea market application, or using a dry cleaning service.

[0045] 4. User notifications and choices

[0046] The terminal notifies the user of the suggestions generated by the server. The user reviews the presented information and selects a disposal method of their choice. The notification appears either as a pop-up on the screen or as a message within the application.

[0047] 5. Support and Follow-up

[0048] The device provides the necessary links and steps based on the disposal method selected by the user. For example, it guides users on how to list items on a flea market application or displays the location of a recycling center. Furthermore, it sets reminders to confirm that the selected disposal method has been completed.

[0049] These processes allow users to efficiently manage their clothing, promoting the optimization of living space and the circular use of resources. This system offers ease of use and economic benefits, contributing to the formation of a sustainable consumer culture.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The user launches the application and enters clothing information (purchase date, number of times worn, price, etc.) into the interface. Once the input is complete, the terminal formats this data and sends it to the server.

[0053] Step 2:

[0054] The server stores the received clothing information in a database and checks for data formatting and missing values. Once the data is ready, it prepares to begin analysis using an AI algorithm.

[0055] Step 3:

[0056] The server calculates the frequency of use of clothing by estimating a wear frequency index based on the period from the purchase date to the present. It also assesses the necessity of each garment using purchase price and market price data. This assessment plays a crucial role in creating recommendations in the next step.

[0057] Step 4:

[0058] Based on the analysis results, the server generates specific disposal suggestions for clothing deemed to have little need. This includes suggesting appropriate selling prices based on market price analysis of similar products, and also includes the option of donating the items to recycling programs.

[0059] Step 5:

[0060] The terminal notifies the user of the suggestions from the server and displays them on the screen. The notification includes suggestions for each garment, recommended prices, and disposal methods. The user reviews this information and selects the best option from the suggested choices.

[0061] Step 6:

[0062] After reviewing the notification, the user selects the most suitable disposal method and sends their selection to the server via their device. Since selections can be made individually, users have flexible management options.

[0063] Step 7:

[0064] The server generates additional information based on the user's selections and provides links and detailed instructions to support disposal procedures. For example, it may provide instructions on how to register with a flea market application or guide users to the location of recycling facilities.

[0065] Step 8:

[0066] The device delivers support information to the user and sets reminders to confirm the execution of the selected disposal method. These reminders are useful for managing the progress of disposal.

[0067] These steps enable users to declutter efficiently, contributing to a more comfortable living space and sustainable consumption.

[0068] (Example 1)

[0069] 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."

[0070] Conventional clothing management systems have a problem in that they do not adequately evaluate the frequency and necessity of clothing use and suggest appropriate disposal methods. Furthermore, there is a challenge in effectively utilizing information about the clothing owned by users to reduce unnecessary clothing in an economical and sustainable way.

[0071] 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.

[0072] In this invention, the server includes means for collecting detailed information about individual items through terminals available to the user and transmitting this information to a hub; calculation means configured to store the received data in a data management device, perform automatic analysis based on the data, and evaluate the usage index of individual items; and means for proposing the optimal processing method using an automatic suggestion generation system based on the results of the data analysis. This enables the user to efficiently evaluate the frequency and necessity of clothing use and receive specific suggestions for properly disposing of unwanted clothing.

[0073] A "user-accessible terminal" is an electronic device that a user can access and operate, and that has functions for inputting information and confirming results.

[0074] "Detailed information about individual items" refers to information about a specific garment, including data such as purchase date, frequency of use, brand, and condition.

[0075] A "hub" is a central digital device that aggregates information and performs necessary processing, playing the role of receiving and transmitting data.

[0076] A "data management device" is a computer system for safely and efficiently storing and processing received information.

[0077] "Automated analysis" is the process of analyzing information provided using machine learning and algorithms to derive certain patterns and indicators.

[0078] A "usage index" is a numerical indicator calculated to evaluate the frequency of use of a particular garment, and it quantitatively represents the usage status.

[0079] An "automatic suggestion generation system" is a program that designs and provides users with the optimal solutions and options based on the results of analysis.

[0080] A "trading platform" is an online or offline marketplace service that users use to buy and sell goods.

[0081] A "circular use organization" is a business entity that provides facilities or services for reusing or recycling unwanted items.

[0082] This system is a platform designed to efficiently support users in managing their clothing. Users access the application using their smartphones or computers and enter detailed information about their clothing. This information includes purchase date, frequency of use, price, brand, and condition. The entered data is immediately sent to the server. The server stores this data in a data management device and analyzes it using AI algorithms provided within the system.

[0083] The server's AI algorithm calculates usage indices such as a "wear frequency index" to assess the necessity of each garment. This process considers factors such as the number of days since purchase, the number of times it has been worn, and the purchase price. It also analyzes market data obtained from flea markets and other trading platforms to assess the market value of similar items.

[0084] Based on the data analysis results, the server uses an automated suggestion generation system to propose the most suitable disposal method for the user. Specific suggestions include donating to a recycling center, selling on a flea market app, or using a cleaning service. The generated suggestions are notified to the user via their device and displayed as an on-screen message or pop-up. The user can then review these suggestions and choose a disposal method based on their own judgment.

[0085] For example, if a user has a pair of jeans they don't wear often, they input that information into the application. Based on the analysis results, the server suggests selling them at a flea market and also provides guidance on appropriate pricing.

[0086] An example of a prompt message might be, "I have a sweater in my closet that I bought three years ago but don't wear very often. What's the proper way to dispose of this sweater?"

[0087] Through this system, users can contribute to optimizing their living spaces and fostering a sustainable consumer culture by managing and rationally disposing of their clothing.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] Users access the application using their smartphones or computers and enter information about their clothing. Specifically, they fill in details such as the purchase date, frequency of use, price, brand, and condition in a form within the app. The entered information is immediately transmitted to the server in digital format.

[0091] Step 2:

[0092] The server stores the clothing information received from the user in a data management device. At this stage, the information is checked for duplication and format consistency before being stored in the database. The input information is organized and converted into an analyzable format to prepare it for the next analysis process.

[0093] Step 3:

[0094] The server executes an AI algorithm based on the stored clothing information. Here, data such as the number of days since purchase, the number of times worn, and the purchase price are processed to calculate the frequency of wear index, and the necessity is evaluated. As a result, a usage index for each clothing item is output.

[0095] Step 4:

[0096] The server combines the analysis results with data obtained from the market to evaluate the market value of the clothing. Here, it references the circulating prices of similar items on trading platforms and uses an AI model to output a suggested price to offer to the user.

[0097] Step 5:

[0098] The server uses an automated suggestion generation system to propose the best disposal method for clothing deemed unwanted. This suggestion lists feasible options for the user, such as donating to a recycling center, selling at a flea market, or using a dry cleaning service. These disposal methods are output as digital suggestions.

[0099] Step 6:

[0100] The device notifies the user of suggestions received from the server. This includes displaying messages within the application, push notifications, and using pop-ups to attract the user's attention. The user then considers and selects a disposal method based on this information.

[0101] Step 7:

[0102] The device assists with disposal procedures based on the user's choices. This includes specific action guidelines, such as directing users to flea market apps and providing links to recycling centers. After selection, follow-up reminders are set to confirm completion of disposal.

[0103] (Application Example 1)

[0104] 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."

[0105] Users own a lot of clothing, but it's difficult to easily keep track of how often they use it, what their needs are, and manage it efficiently. Furthermore, they often lack the information to make the best decisions when deciding how to dispose of their clothes. There's also a demand for a more intuitive and efficient system that eliminates the need for users to manually input clothing information.

[0106] 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.

[0107] In this invention, the server includes means for receiving user input and storing information in a database, means for performing information analysis based on the database to evaluate frequency of use and necessity, and means for automatically acquiring information about clothing using speech recognition and image recognition technology. This allows users to manage their clothing without hassle and to quickly obtain information on appropriate disposal methods.

[0108] "User input" refers to information provided by the user through their device, and forms the basis for detailed data about clothing.

[0109] A "database" is an information management system that systematically stores information about collected clothing and uses it for analysis.

[0110] The "analysis method" refers to an algorithm that evaluates the frequency of use and necessity of clothing based on information stored in a database, and provides an algorithm for making appropriate disposal decisions.

[0111] A "suggestion" is a specific set of options, based on the results generated by the analysis tool, that instructs the user on the most efficient way to dispose of clothing.

[0112] "Voice recognition" is a technology that converts a user's voice input into digital data and automatically extracts information about clothing.

[0113] "Image recognition" is a technology that analyzes visual information of clothing acquired through a camera and automatically determines the characteristics and condition of the item.

[0114] This invention is a system for streamlining clothing management within the home. The system collects information entered by the user about their clothing, uses that data to evaluate its frequency of use and necessity, and proposes an appropriate disposal method.

[0115] The server stores the clothing information entered by the user in a database. Users use devices such as smartphones or personal computers to enter information such as the purchase date, frequency of use, and price of the clothing. This information is immediately transmitted to the server through the interface.

[0116] The server uses AI algorithms to perform data analysis. This involves processing information obtained using speech recognition and image recognition libraries to evaluate the frequency of use and necessity of clothing. Specifically, it determines the need for disposal based on usage frequency indices and market data for similar products.

[0117] The server generates specific disposal suggestions for the user based on the analysis results. These suggestions include multiple options, such as selling at a flea market or using recycling services. Furthermore, it optimizes the suggestions by utilizing information automatically acquired through voice and image recognition technology.

[0118] The user receives a suggestion from the server and selects a disposal method on the screen. For example, a suggestion might say, "You've only worn this shirt once in the last three months. We recommend selling it at a flea market or recycling it."

[0119] This system allows users to manage their clothing without hassle and dispose of it efficiently and economically. It also contributes to the formation of a sustainable consumer culture.

[0120] An example of a prompt is: "Design a prompt for a robot application that uses a speech recognition engine to retrieve clothing information from the user's speech and compare it with market data for evaluation. Then, it will suggest efficient clothing management to the user."

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] Users input information such as the purchase date, frequency of use, and price of clothing via their smartphone or computer, and the device sends this information to the server through the system interface. The input data is in text format, and the server stores it in a database upon receipt. The output of this step is the clothing information recorded in the database.

[0124] Step 2:

[0125] The server analyzes the information stored in the database using an AI algorithm to calculate a clothing usage frequency index. This calculation takes into account factors such as the number of times the garment has been worn in the past and the number of days since purchase. The input is the database information, and the program evaluates the usage frequency and necessity of each garment based on the analysis results. The output of this step is the usage frequency index as a result of the analysis.

[0126] Step 3:

[0127] The server automatically acquires additional information about clothing using speech and image recognition technologies. The user speaks to the robot, the robot's camera takes pictures of the clothing, and the terminal sends the image information to the server. The input consists of image data from the camera and audio data from the microphone, which the program analyzes to extract detailed information about the clothing. The output of this step is detailed information about the clothing based on image and speech recognition.

[0128] Step 4:

[0129] The server generates disposal suggestions based on usage frequency index and market data. This includes referencing market prices and recommending appropriate sales platforms. The inputs are the usage frequency index, acquired detailed information, and market data, and the program optimizes and outputs disposal method suggestions. The output of this step is the specific disposal suggestion presented to the user.

[0130] Step 5:

[0131] The terminal notifies the user of disposal suggestions received from the server and presents them on the screen as options. The user selects a disposal method from the presented options, and the terminal sends the selection result back to the server. The input is the suggestion data from the server, and the output is the user's selection result.

[0132] Step 6:

[0133] The server provides necessary links and instructions as support information based on the user's selection. This includes links to flea market applications and directions to recycling locations. The input is the user's selection, and the output is the links and instructions as support information. This step ensures that the proposed disposal method can be carried out smoothly.

[0134] 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.

[0135] This invention is a system that combines an emotion engine with a system to support decluttering clothing, thereby providing suggestions that take the user's emotional state into account. The system aims to recognize the user's emotions using an emotion engine, based on data analysis of the user's clothing information, and then propose appropriate disposal methods.

[0136] Configuration and Operation

[0137] 1. User Input and Sentiment Recognition

[0138] Users input clothing information through the application. During this process, an emotion engine on the device analyzes the user's emotional state in real time based on their voice tone and input patterns. The emotional information obtained by the emotion engine is used to adjust subsequent suggestions.

[0139] 2. Data storage and analysis

[0140] The server stores clothing information submitted by the user and data from the emotion engine in a database. Next, an AI algorithm is used to calculate the frequency and necessity of clothing use, while simultaneously considering the user's emotional state. The tone and nature of the suggestions are adjusted according to the emotional state.

[0141] 3. Generating the proposal

[0142] The server generates optimal decluttering suggestions based on analysis results and sentiment data. For example, if a user is reluctant to let go of clothing, the suggestions are adjusted to be more careful and user-friendly. When suggesting appropriate prices, the server uses market data and employs language that takes user psychology into consideration.

[0143] 4. Notifications and Feedback

[0144] The device notifies the user of the generated suggestions. The suggestions include messages that are sensitive to the user's feelings and are presented in a way that respects flexibility in choices. This makes it easier for the user to make rational decisions while reducing emotional burden.

[0145] 5. Selection and Support

[0146] The user reviews the suggested disposal methods and selects their preferred method. Based on the chosen method, the server provides the user with the necessary instructions and support links. Sentimental data is also used in this support, for example, by including messages that encourage positive feedback.

[0147] This workflow allows users to manage their clothing efficiently and with reduced psychological burden while being aware of their own emotions. By leveraging emotional intelligence, this system provides a more personalized user experience and enables comfortable and sustainable clothing management.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The user launches the application and enters clothing information (purchase date, frequency of use, price, etc.) into the interface. The interface has voice input and text input options, and as input is being made, an emotion engine analyzes the user's voice tone through the device's microphone.

[0151] Step 2:

[0152] The terminal processes user input information using an emotion engine and analyzes the user's emotional state (e.g., joy, anxiety, indifference, etc.) in real time. This emotion data, along with other input data, is immediately sent to the server.

[0153] Step 3:

[0154] The server stores the received clothing information and emotional data in a database and then prepares to analyze it. The analysis uses an AI algorithm to calculate the frequency and necessity of clothing use, and the emotional data is used to adjust the recommendations.

[0155] Step 4:

[0156] The server assesses the need for each garment and generates disposal suggestions based on that assessment and emotional data. For example, if the user is reluctant to part with an item, the suggestion will be presented in a more cautious and positive tone. Furthermore, the appropriate price suggestion, based on market data, is also flexibly adjusted according to the user's emotional state.

[0157] Step 5:

[0158] The device notifies the user of the generated suggestions and displays detailed suggestions on the screen. These notifications include personalized messages that take the user's emotions into consideration, ensuring they can make decisions with confidence.

[0159] Step 6:

[0160] The user reviews the suggestions and selects the desired action from the disposal options. Selection can be easily done with a tap or click. The selected information is then sent back to the server.

[0161] Step 7:

[0162] The server generates additional support information based on the user's selection, providing detailed instructions and links regarding the chosen disposal method. This information is sent to the user via the terminal and may include emotionally responsive feedback to enhance user satisfaction.

[0163] Step 8:

[0164] The device uses the provided information to support the execution of the selected disposal method and plans follow-up after disposal is complete. This plan includes a reminder function to help the user proceed smoothly with the plan.

[0165] These steps allow users to manage their clothing efficiently and stress-free while being attentive to their own emotions.

[0166] (Example 2)

[0167] 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 as the "terminal".

[0168] Users often experience emotional burden when deciding how to dispose of their belongings and seek rational and emotionally considerate management methods. Traditional methods struggle to offer emotionally conscious suggestions, and relying solely on self-judgment has its limitations. Therefore, it is necessary to realize efficient and comfortable item management that takes users' emotions into consideration.

[0169] 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.

[0170] In this invention, the server includes means for storing information about items received from the user in a central storage location, means for performing analysis to detect the user's emotional state along with the information about the received items, and means for generating suggestions that take the emotional state into consideration based on the analysis results. As a result, the user receives emotionally sensitive suggestions and can dispose of items in a comfortable and rational manner.

[0171] A "user" is someone who uses the system to input information about goods and receives suggestions.

[0172] "Items" refers to clothing and other possessions that the user owns and is considering disposing of.

[0173] "Information" refers to data about an item, specifically including data such as its name, type, frequency of use, and emotional state.

[0174] A "central storage location" refers to a database system for securely recording and storing received information.

[0175] "Emotional state" refers to the psychological and emotional state of the user when entering item information, and is detected through voice and input patterns.

[0176] "Analysis" refers to a series of processes that involve processing received information to extract necessary patterns and trends.

[0177] "Suggestions" refer to messages generated based on analysis results regarding items, which present disposal policies and options to the user.

[0178] "Generating proposals" refers to the process of creating specific suggestions for actions to be presented to the user based on the analyzed data.

[0179] This invention generates suggestions regarding the disposal of items owned by a user, taking into account their emotional state.

[0180] 1. User input and sentiment analysis:

[0181] Users input information about their belongings, such as clothing, through a dedicated application. During this process, an emotion engine built into the device analyzes the user's voice tone and input patterns to measure their emotional state in real time. This analysis utilizes speech recognition and input pattern analysis technologies.

[0182] 2. Data collection and storage:

[0183] The server stores item information and sentiment data transmitted from the terminals in a cloud-based database. A database management system is used to ensure the consistency and security of the information.

[0184] 3. Data analysis and proposal generation:

[0185] The server uses AI algorithms and generative AI models to analyze clothing information and user sentiment data. It creates suggestions that reflect user emotions while considering usage frequency and trends. For example, it can list items that haven't been used for over six months and suggest disposal.

[0186] 4. Proposal notification and provision of options:

[0187] The device notifies the user of individually customized suggestions. This can include messages encouraging proactive action and flexible options.

[0188] 5. Providing support:

[0189] Once the user reviews the proposal and makes a selection, the server provides links to information on appropriate disposal methods, transaction applications, and reusable locations. This reduces the emotional burden on the user and allows them to dispose of items in a rational manner.

[0190] For example, if a user is getting tired of a "light blue sweater" but still feels attached to it, the system will detect this emotion and suggest to the user that "donating the sweater might mean someone else will cherish and wear it."

[0191] Example of a prompt:

[0192] "Tell us how you feel about clothing, and then add any items you feel you don't need."

[0193] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0194] Step 1:

[0195] Users use a terminal to input item information into a dedicated application. The information entered includes the type of item, frequency of use, and purchase date.

[0196] The device captures the user's voice tone and input speed in real time while they are typing, and uses an emotion engine to analyze the user's emotional state.

[0197] The entered item information and emotion data are converted into a digital format and temporarily stored.

[0198] Step 2:

[0199] The server receives item information and emotion data transmitted from the terminal and stores it in a cloud database.

[0200] The received data is organized and formatted into a standardized format. During this process, data consistency is verified, and unnecessary data is removed.

[0201] The output consists of organized item information and sentiment data, which are stored in a database.

[0202] Step 3:

[0203] Based on the stored information, the server uses an AI algorithm to analyze the frequency of use and necessity of items.

[0204] The analysis involves referencing past usage data and market data, and taking into account user sentiment patterns.

[0205] As output, an AI model will generate proposals for the disposal of items that take emotions into consideration.

[0206] Step 4:

[0207] The server sends the generated suggestions to the terminal.

[0208] The device notifies the user of received proposals and displays the proposal details. It also displays a message that includes flexible disposal options the user can choose from.

[0209] The output provides users with personalized suggestions based on their emotions.

[0210] Step 5:

[0211] The user reviews the proposal and selects their preferred disposal method. Options include holding, donating, and recycling.

[0212] Based on the user's selection, the server sends information links and procedures related to the disposal method to the terminal.

[0213] The output provides users with detailed information on the most suitable disposal method, offering support to reduce their emotional burden.

[0214] (Application Example 2)

[0215] 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 device 14 will be referred to as the "terminal."

[0216] In organizing and decluttering clothing, there is a challenge in enabling users to make sound decisions without experiencing emotional burden. Traditional systems do not take emotions into consideration, which can lead to users not feeling satisfied or content, and as a result, inefficient processing.

[0217] 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.

[0218] In this invention, the server includes means for receiving user input and storing information about clothing entered by the user in a data storage device; means for analyzing the information about clothing stored in the data storage device and evaluating the frequency of use and necessity of the clothing; means for acquiring the user's emotional state in real time using emotion analysis means; means for generating suggestions regarding the disposal of the clothing based on the evaluation results of frequency of use and necessity and the emotional state; and means for notifying the user of the suggestions, supporting the disposal procedure based on the user's selection, and providing feedback utilizing emotional data. This enables the user to organize and declutter their clothes in a way that is in line with their emotions.

[0219] "User input" refers to data provided by the user to the information processing device, and includes items related to clothing.

[0220] A "data storage device" is a device that stores information permanently or temporarily, and has the function of storing clothing information based on user input.

[0221] "Emotion analysis means" refers to technology that analyzes and acquires the user's emotional state in real time from their voice, input patterns, etc.

[0222] "Evaluation means" refers to analytical techniques for calculating the frequency and necessity of clothing use using information stored in a data storage device.

[0223] The "proposal generation means" is a process that, based on analysis and sentiment data, shows the user how to dispose of the target clothing.

[0224] A "notification method" is a technology equipped with communication functions to present generated suggestions to the user and prompt the user to make a choice.

[0225] A "disposal procedure support system" is a system configuration that provides necessary procedures and related information based on the disposal method selected by the user, and provides feedback that takes the user's feelings into consideration.

[0226] In the system for realizing this invention, the user first inputs information about their clothing using a terminal. This can be a smartphone or an interface device. The terminal has an emotion analysis engine built in, which acquires the user's emotional state in real time from their voice or text input.

[0227] The server stores and analyzes clothing information received via data storage. The analysis uses AI algorithms to evaluate the frequency of use and necessity of the entered clothing items. Sentiment analysis data is also incorporated, taking into account the user's feelings towards the items.

[0228] Next, the server generates an optimal disposal suggestion based on the analysis results and sentiment data. This suggestion is adjusted according to the user's emotional state, making it more likely to be accepted by the user. The suggestion also includes a fair price derived from market data.

[0229] This proposal is notified to the user via their device, where they can review and select the proposed options. Based on the user's selection, the system provides support regarding disposal procedures. For example, it may provide links to e-commerce platforms or resource recycling centers to support the user's decision. Furthermore, the system can use the collected sentiment data to deliver positive messages to the user.

[0230] For example, if a user asks, "Should I sell this jacket?", the system will consider the user's emotional state and suggest, "It seems to be comfortable for you and its value has increased significantly." An example of a prompt from the generative AI model would be, "Analyze the user's input tone and suggest a clothing management strategy considering their emotional state."

[0231] This system allows users to manage their clothing in a rational and personalized way while reducing emotional burden.

[0232] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0233] Step 1:

[0234] The terminal accepts user input. The user enters information about their clothing using a smart device. Here, they enter information such as the type of clothing, brand, and purchase date into input fields, and this data is collected by the terminal's user interface. The entered data is then prepared to be sent to the data storage device.

[0235] Step 2:

[0236] The device performs emotion analysis. It analyzes the user's emotional state in real time, using both the user's input and voice input / input patterns. The emotion analysis engine generates emotion data based on indicators such as voice tone and input speed, and sends this data as output to the next step.

[0237] Step 3:

[0238] The server stores the user's clothing information in a data storage device. It receives clothing information and emotional data transmitted from the terminal and saves it to a database. This makes the information easily accessible in subsequent processes.

[0239] Step 4:

[0240] The server analyzes the stored data. It retrieves clothing information from the database and uses an AI algorithm to evaluate usage frequency and necessity. Emotional data is also taken into account, and the analysis results output an evaluation score, which is then used to generate decision-making information.

[0241] Step 5:

[0242] The server generates suggestions. Based on analysis results and sentiment data, it generates optimal clothing disposal suggestions to present to the user. This process also references market data and incorporates attribute-based valuation. The generated suggestions are then sent to the next step.

[0243] Step 6:

[0244] The terminal notifies the user of the suggestion. It displays the suggested content received from the server to the user and provides multiple options through an interactive interface. An input interface for the user to make a selection is presented, and the terminal waits for the user's response.

[0245] Step 7:

[0246] The user makes a selection based on the suggestions. They operate a user interface on their device to review the suggestions presented and select the most suitable disposal method. The selected information is then sent to the server via the device.

[0247] Step 8:

[0248] The server provides support based on the user's choices. Based on the selected disposal method, the server generates and sends links to relevant e-commerce platforms and resource recycling centers to the user's device. Furthermore, it provides feedback messages utilizing sentiment data to improve user satisfaction.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] [Second Embodiment]

[0253] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0254] 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.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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).

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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".

[0265] This invention is a system that evaluates the frequency of use and necessity of clothing based on information about the user's possessions and proposes appropriate disposal methods. This system supports efficient decluttering by allowing users to input clothing information through an easy-to-use interface, sending it to a server for analysis, and so on.

[0266] Configuration and Operation

[0267] 1. User input

[0268] Users access the application using devices such as smartphones or computers and enter information such as the purchase date, frequency of use, and price of their clothing. This information may also include details such as the item name, brand, and condition. The information entered by the user is immediately transmitted to the server from the interface.

[0269] 2. Data Analysis

[0270] The server stores information about received clothing in a database, and then uses an AI algorithm to calculate the frequency of use for each garment and evaluate its necessity. This includes calculating a "frequency of use index" that takes into account factors such as the number of days since purchase, the number of times it has been worn, and the purchase price. Furthermore, the server conducts market research and analyzes how much similar items are selling for at flea markets.

[0271] 3. Generating the proposal

[0272] Based on the data analysis, the server generates specific disposal suggestions for clothing deemed unnecessary. These suggestions include options such as donating to a recycling bin, setting a selling price on a flea market application, or using a dry cleaning service.

[0273] 4. User notifications and choices

[0274] The terminal notifies the user of the suggestions generated by the server. The user reviews the presented information and selects a disposal method of their choice. The notification appears either as a pop-up on the screen or as a message within the application.

[0275] 5. Support and Follow-up

[0276] The device provides the necessary links and steps based on the disposal method selected by the user. For example, it guides users on how to list items on a flea market application or displays the location of a recycling center. Furthermore, it sets reminders to confirm that the selected disposal method has been completed.

[0277] Through these processes, users can efficiently manage their clothing, promoting the optimization of living space and the recycling of resources. This system provides ease of use and economic benefits, contributing to the formation of a sustainable consumption culture.

[0278] The following is an explanation of the processing flow.

[0279] Step 1:

[0280] The user launches the application and enters clothing information (purchase date, number of uses, price, etc.) into the interface. When the input is completed, the terminal formats this data and sends it to the server.

[0281] Step 2:

[0282] The server stores the received clothing information in the database and checks the data format and missing values. After the data is complete, it prepares to start analysis by the AI algorithm.

[0283] Step 3:

[0284] The server calculates the wearing frequency index based on the period from the purchase date to the present to calculate the usage frequency of the clothing. Also, to conduct a necessity assessment, it determines the necessity of each piece of clothing using the purchase price and market price data. This assessment plays an important role in generating proposals in the next step.

[0285] Step 4:

[0286] Based on the analysis results, the server generates specific disposal proposals for clothing judged to have low necessity. Here, in addition to presenting an appropriate selling price from the analysis of the market price of similar products, proposals for donations for recycling are also included in part of the proposals.

[0287] Step 5:

[0288] The terminal notifies the user of the suggestions from the server and displays them on the screen. The notification includes suggestions for each garment, recommended prices, and disposal methods. The user reviews this information and selects the best option from the suggested choices.

[0289] Step 6:

[0290] After reviewing the notification, the user selects the most suitable disposal method and sends their selection to the server via their device. Since selections can be made individually, users have flexible management options.

[0291] Step 7:

[0292] The server generates additional information based on the user's selections and provides links and detailed instructions to support disposal procedures. For example, it may provide instructions on how to register with a flea market application or guide users to the location of recycling facilities.

[0293] Step 8:

[0294] The device delivers support information to the user and sets reminders to confirm the execution of the selected disposal method. These reminders are useful for managing the progress of disposal.

[0295] These steps enable users to declutter efficiently, contributing to a more comfortable living space and sustainable consumption.

[0296] (Example 1)

[0297] 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".

[0298] Conventional clothing management systems have a problem in that they do not adequately evaluate the frequency and necessity of clothing use and suggest appropriate disposal methods. Furthermore, there is a challenge in effectively utilizing information about the clothing owned by users to reduce unnecessary clothing in an economical and sustainable way.

[0299] 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.

[0300] In this invention, the server includes means for collecting detailed information about individual items through terminals available to the user and transmitting this information to a hub; calculation means configured to store the received data in a data management device, perform automatic analysis based on the data, and evaluate the usage index of individual items; and means for proposing the optimal processing method using an automatic suggestion generation system based on the results of the data analysis. This enables the user to efficiently evaluate the frequency and necessity of clothing use and receive specific suggestions for properly disposing of unwanted clothing.

[0301] A "user-accessible terminal" is an electronic device that a user can access and operate, and that has functions for inputting information and confirming results.

[0302] "Detailed information about individual items" refers to information about a specific garment, including data such as purchase date, frequency of use, brand, and condition.

[0303] A "hub" is a central digital device that aggregates information and performs necessary processing, playing the role of receiving and transmitting data.

[0304] A "data management device" is a computer system for safely and efficiently storing and processing received information.

[0305] "Automatic analysis" is a process that analyzes the information provided using machine learning and algorithms to derive certain patterns and metrics.

[0306] "Usage index" is a numerical metric calculated to evaluate the frequency of use of specific clothing items and quantitatively represents the usage situation.

[0307] "Automatic proposal generation system" is a program that designs optimal solutions and options based on the results of analysis and provides them to users.

[0308] "Trading platform" is an online or offline market service used by users to buy and sell items.

[0309] "Recycling organization" is an entity that provides facilities and services for reusing or recycling unwanted items.

[0310] This system is a platform designed to efficiently support users' clothing management. Users access the application using terminals such as their smartphones or personal computers and input detailed information about their clothing. This information includes the purchase date, usage frequency, price, brand, condition, etc. The input data is immediately sent to the server. The server stores this data in a data management device and performs analysis using the AI algorithms prepared within the system.

[0311] The server's AI algorithms calculate usage indices such as the "wearing frequency index" and evaluate the necessity of each piece of clothing. In this process, factors such as the number of days elapsed since the purchase date, the number of times worn, and the purchase price are considered. Also, by analyzing market data obtained from free markets and other trading platforms, the market value of similar items is evaluated.

[0312] Based on the data analysis results, the server uses an automated suggestion generation system to propose the most suitable disposal method for the user. Specific suggestions include donating to a recycling center, selling on a flea market app, or using a cleaning service. The generated suggestions are notified to the user via their device and displayed as an on-screen message or pop-up. The user can then review these suggestions and choose a disposal method based on their own judgment.

[0313] For example, if a user has a pair of jeans they don't wear often, they input that information into the application. Based on the analysis results, the server suggests selling them at a flea market and also provides guidance on appropriate pricing.

[0314] An example of a prompt message might be, "I have a sweater in my closet that I bought three years ago but don't wear very often. What's the proper way to dispose of this sweater?"

[0315] Through this system, users can contribute to optimizing their living spaces and fostering a sustainable consumer culture by managing and rationally disposing of their clothing.

[0316] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0317] Step 1:

[0318] Users access the application using their smartphones or computers and enter information about their clothing. Specifically, they fill in details such as the purchase date, frequency of use, price, brand, and condition in a form within the app. The entered information is immediately transmitted to the server in digital format.

[0319] Step 2:

[0320] The server stores the clothing information received from the user in a data management device. At this stage, the information is checked for duplication and format consistency before being stored in the database. The input information is organized and converted into an analyzable format to prepare it for the next analysis process.

[0321] Step 3:

[0322] The server executes an AI algorithm based on the stored clothing information. Here, data such as the number of days since purchase, the number of times worn, and the purchase price are processed to calculate the frequency of wear index, and the necessity is evaluated. As a result, a usage index for each clothing item is output.

[0323] Step 4:

[0324] The server combines the analysis results with data obtained from the market to evaluate the market value of the clothing. Here, it references the circulating prices of similar items on trading platforms and uses an AI model to output a suggested price to offer to the user.

[0325] Step 5:

[0326] The server uses an automated suggestion generation system to propose the best disposal method for clothing deemed unwanted. This suggestion lists feasible options for the user, such as donating to a recycling center, selling at a flea market, or using a dry cleaning service. These disposal methods are output as digital suggestions.

[0327] Step 6:

[0328] The device notifies the user of suggestions received from the server. This includes displaying messages within the application, push notifications, and using pop-ups to attract the user's attention. The user then considers and selects a disposal method based on this information.

[0329] Step 7:

[0330] The device assists with disposal procedures based on the user's choices. This includes specific action guidelines, such as directing users to flea market apps and providing links to recycling centers. After selection, follow-up reminders are set to confirm completion of disposal.

[0331] (Application Example 1)

[0332] 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."

[0333] Users own a lot of clothing, but it's difficult to easily keep track of how often they use it, what their needs are, and manage it efficiently. Furthermore, they often lack the information to make the best decisions when deciding how to dispose of their clothes. There's also a demand for a more intuitive and efficient system that eliminates the need for users to manually input clothing information.

[0334] 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.

[0335] In this invention, the server includes means for receiving user input and storing information in a database, means for performing information analysis based on the database to evaluate frequency of use and necessity, and means for automatically acquiring information about clothing using speech recognition and image recognition technology. This allows users to manage their clothing without hassle and to quickly obtain information on appropriate disposal methods.

[0336] "User input" refers to information provided by the user through their device, and forms the basis for detailed data about clothing.

[0337] A "database" is an information management system that systematically stores information about collected clothing and uses it for analysis.

[0338] The "analysis method" refers to an algorithm that evaluates the frequency of use and necessity of clothing based on information stored in a database, and provides an algorithm for making appropriate disposal decisions.

[0339] A "suggestion" is a specific set of options, based on the results generated by the analysis tool, that instructs the user on the most efficient way to dispose of clothing.

[0340] "Voice recognition" is a technology that converts a user's voice input into digital data and automatically extracts information about clothing.

[0341] "Image recognition" is a technology that analyzes visual information of clothing acquired through a camera and automatically determines the characteristics and condition of the item.

[0342] This invention is a system for streamlining clothing management within the home. The system collects information entered by the user about their clothing, uses that data to evaluate its frequency of use and necessity, and proposes an appropriate disposal method.

[0343] The server stores the clothing information entered by the user in a database. Users use devices such as smartphones or personal computers to enter information such as the purchase date, frequency of use, and price of the clothing. This information is immediately transmitted to the server through the interface.

[0344] The server uses AI algorithms to perform data analysis. This involves processing information obtained using speech recognition and image recognition libraries to evaluate the frequency of use and necessity of clothing. Specifically, it determines the need for disposal based on usage frequency indices and market data for similar products.

[0345] The server generates specific disposal suggestions for the user based on the analysis results. These suggestions include multiple options, such as selling at a flea market or using recycling services. Furthermore, it optimizes the suggestions by utilizing information automatically acquired through voice and image recognition technology.

[0346] The user receives a suggestion from the server and selects a disposal method on the screen. For example, a suggestion might say, "You've only worn this shirt once in the last three months. We recommend selling it at a flea market or recycling it."

[0347] This system allows users to manage their clothing without hassle and dispose of it efficiently and economically. It also contributes to the formation of a sustainable consumer culture.

[0348] An example of a prompt is: "Design a prompt for a robot application that uses a speech recognition engine to retrieve clothing information from the user's speech and compare it with market data for evaluation. Then, it will suggest efficient clothing management to the user."

[0349] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0350] Step 1:

[0351] Users input information such as the purchase date, frequency of use, and price of clothing via their smartphone or computer, and the device sends this information to the server through the system interface. The input data is in text format, and the server stores it in a database upon receipt. The output of this step is the clothing information recorded in the database.

[0352] Step 2:

[0353] The server analyzes the information stored in the database using an AI algorithm to calculate a clothing usage frequency index. This calculation takes into account factors such as the number of times the garment has been worn in the past and the number of days since purchase. The input is the database information, and the program evaluates the usage frequency and necessity of each garment based on the analysis results. The output of this step is the usage frequency index as a result of the analysis.

[0354] Step 3:

[0355] The server automatically acquires additional information about clothing using speech and image recognition technologies. The user speaks to the robot, the robot's camera takes pictures of the clothing, and the terminal sends the image information to the server. The input consists of image data from the camera and audio data from the microphone, which the program analyzes to extract detailed information about the clothing. The output of this step is detailed information about the clothing based on image and speech recognition.

[0356] Step 4:

[0357] The server generates disposal suggestions based on usage frequency index and market data. This includes referencing market prices and recommending appropriate sales platforms. The inputs are the usage frequency index, acquired detailed information, and market data, and the program optimizes and outputs disposal method suggestions. The output of this step is the specific disposal suggestion presented to the user.

[0358] Step 5:

[0359] The terminal notifies the user of disposal suggestions received from the server and presents them on the screen as options. The user selects a disposal method from the presented options, and the terminal sends the selection result back to the server. The input is the suggestion data from the server, and the output is the user's selection result.

[0360] Step 6:

[0361] The server provides necessary links and instructions as support information based on the user's selection. This includes links to flea market applications and directions to recycling locations. The input is the user's selection, and the output is the links and instructions as support information. This step ensures that the proposed disposal method can be carried out smoothly.

[0362] 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.

[0363] This invention is a system that combines an emotion engine with a system to support decluttering clothing, thereby providing suggestions that take the user's emotional state into account. The system aims to recognize the user's emotions using an emotion engine, based on data analysis of the user's clothing information, and then propose appropriate disposal methods.

[0364] Configuration and Operation

[0365] 1. User Input and Sentiment Recognition

[0366] Users input clothing information through the application. During this process, an emotion engine on the device analyzes the user's emotional state in real time based on their voice tone and input patterns. The emotional information obtained by the emotion engine is used to adjust subsequent suggestions.

[0367] 2. Data storage and analysis

[0368] The server stores clothing information submitted by the user and data from the emotion engine in a database. Next, an AI algorithm is used to calculate the frequency and necessity of clothing use, while simultaneously considering the user's emotional state. The tone and nature of the suggestions are adjusted according to the emotional state.

[0369] 3. Generating the proposal

[0370] The server generates optimal decluttering suggestions based on analysis results and sentiment data. For example, if a user is reluctant to let go of clothing, the suggestions are adjusted to be more careful and user-friendly. When suggesting appropriate prices, the server uses market data and employs language that takes user psychology into consideration.

[0371] 4. Notifications and Feedback

[0372] The device notifies the user of the generated suggestions. The suggestions include messages that are sensitive to the user's feelings and are presented in a way that respects flexibility in choices. This makes it easier for the user to make rational decisions while reducing emotional burden.

[0373] 5. Selection and Support

[0374] The user reviews the suggested disposal methods and selects their preferred method. Based on the chosen method, the server provides the user with the necessary instructions and support links. Sentimental data is also used in this support, for example, by including messages that encourage positive feedback.

[0375] This workflow allows users to manage their clothing efficiently and with reduced psychological burden while being aware of their own emotions. By leveraging emotional intelligence, this system provides a more personalized user experience and enables comfortable and sustainable clothing management.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The user launches the application and enters clothing information (purchase date, frequency of use, price, etc.) into the interface. The interface has voice input and text input options, and as input is being made, an emotion engine analyzes the user's voice tone through the device's microphone.

[0379] Step 2:

[0380] The terminal processes user input information using an emotion engine and analyzes the user's emotional state (e.g., joy, anxiety, indifference, etc.) in real time. This emotion data, along with other input data, is immediately sent to the server.

[0381] Step 3:

[0382] The server stores the received clothing information and emotional data in a database and then prepares to analyze it. The analysis uses an AI algorithm to calculate the frequency and necessity of clothing use, and the emotional data is used to adjust the recommendations.

[0383] Step 4:

[0384] The server assesses the need for each garment and generates disposal suggestions based on that assessment and emotional data. For example, if the user is reluctant to part with an item, the suggestion will be presented in a more cautious and positive tone. Furthermore, the appropriate price suggestion, based on market data, is also flexibly adjusted according to the user's emotional state.

[0385] Step 5:

[0386] The device notifies the user of the generated suggestions and displays detailed suggestions on the screen. These notifications include personalized messages that take the user's emotions into consideration, ensuring they can make decisions with confidence.

[0387] Step 6:

[0388] The user reviews the suggestions and selects the desired action from the disposal options. Selection can be easily done with a tap or click. The selected information is then sent back to the server.

[0389] Step 7:

[0390] The server generates additional support information based on the user's selection, providing detailed instructions and links regarding the chosen disposal method. This information is sent to the user via the terminal and may include emotionally responsive feedback to enhance user satisfaction.

[0391] Step 8:

[0392] The device uses the provided information to support the execution of the selected disposal method and plans follow-up after disposal is complete. This plan includes a reminder function to help the user proceed smoothly with the plan.

[0393] These steps allow users to manage their clothing efficiently and stress-free while being attentive to their own emotions.

[0394] (Example 2)

[0395] 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".

[0396] Users often experience emotional burden when deciding how to dispose of their belongings and seek rational and emotionally considerate management methods. Traditional methods struggle to offer emotionally conscious suggestions, and relying solely on self-judgment has its limitations. Therefore, it is necessary to realize efficient and comfortable item management that takes users' emotions into consideration.

[0397] 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.

[0398] In this invention, the server includes means for storing information about items received from the user in a central storage location, means for performing analysis to detect the user's emotional state along with the information about the received items, and means for generating suggestions that take the emotional state into consideration based on the analysis results. As a result, the user receives emotionally sensitive suggestions and can dispose of items in a comfortable and rational manner.

[0399] A "user" is someone who uses the system to input information about goods and receives suggestions.

[0400] "Items" refers to clothing and other possessions that the user owns and is considering disposing of.

[0401] "Information" refers to data about an item, specifically including data such as its name, type, frequency of use, and emotional state.

[0402] A "central storage location" refers to a database system for securely recording and storing received information.

[0403] "Emotional state" refers to the psychological and emotional state of the user when entering item information, and is detected through voice and input patterns.

[0404] "Analysis" refers to a series of processes that involve processing received information to extract necessary patterns and trends.

[0405] "Suggestions" refer to messages generated based on analysis results regarding items, which present disposal policies and options to the user.

[0406] "Generating proposals" refers to the process of creating specific suggestions for actions to be presented to the user based on the analyzed data.

[0407] This invention generates suggestions regarding the disposal of items owned by a user, taking into account their emotional state.

[0408] 1. User input and sentiment analysis:

[0409] Users input information about their belongings, such as clothing, through a dedicated application. During this process, an emotion engine built into the device analyzes the user's voice tone and input patterns to measure their emotional state in real time. This analysis utilizes speech recognition and input pattern analysis technologies.

[0410] 2. Data collection and storage:

[0411] The server stores item information and sentiment data transmitted from the terminals in a cloud-based database. A database management system is used to ensure the consistency and security of the information.

[0412] 3. Data analysis and proposal generation:

[0413] The server uses AI algorithms and generative AI models to analyze clothing information and user sentiment data. It creates suggestions that reflect user emotions while considering usage frequency and trends. For example, it can list items that haven't been used for over six months and suggest disposal.

[0414] 4. Proposal notification and provision of options:

[0415] The device notifies the user of individually customized suggestions. This can include messages encouraging proactive action and flexible options.

[0416] 5. Providing support:

[0417] Once the user reviews the proposal and makes a selection, the server provides links to information on appropriate disposal methods, transaction applications, and reusable locations. This reduces the emotional burden on the user and allows them to dispose of items in a rational manner.

[0418] For example, if a user is getting tired of a "light blue sweater" but still feels attached to it, the system will detect this emotion and suggest to the user that "donating the sweater might mean someone else will cherish and wear it."

[0419] Example of a prompt:

[0420] "Tell us how you feel about clothing, and then add any items you feel you don't need."

[0421] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0422] Step 1:

[0423] Users use a terminal to input item information into a dedicated application. The information entered includes the type of item, frequency of use, and purchase date.

[0424] The device captures the user's voice tone and input speed in real time while they are typing, and uses an emotion engine to analyze the user's emotional state.

[0425] The entered item information and emotion data are converted into a digital format and temporarily stored.

[0426] Step 2:

[0427] The server receives item information and emotion data transmitted from the terminal and stores it in a cloud database.

[0428] The received data is organized and formatted into a standardized format. During this process, data consistency is verified, and unnecessary data is removed.

[0429] The output consists of organized item information and sentiment data, which are stored in a database.

[0430] Step 3:

[0431] Based on the stored information, the server uses an AI algorithm to analyze the frequency of use and necessity of items.

[0432] The analysis involves referencing past usage data and market data, and taking into account user sentiment patterns.

[0433] As output, an AI model will generate proposals for the disposal of items that take emotions into consideration.

[0434] Step 4:

[0435] The server sends the generated suggestions to the terminal.

[0436] The device notifies the user of received proposals and displays the proposal details. It also displays a message that includes flexible disposal options the user can choose from.

[0437] The output provides users with personalized suggestions based on their emotions.

[0438] Step 5:

[0439] The user reviews the proposal and selects their preferred disposal method. Options include holding, donating, and recycling.

[0440] Based on the user's selection, the server sends information links and procedures related to the disposal method to the terminal.

[0441] The output provides users with detailed information on the most suitable disposal method, offering support to reduce their emotional burden.

[0442] (Application Example 2)

[0443] 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."

[0444] In organizing and decluttering clothing, there is a challenge in enabling users to make sound decisions without experiencing emotional burden. Traditional systems do not take emotions into consideration, which can lead to users not feeling satisfied or content, and as a result, inefficient processing.

[0445] 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.

[0446] In this invention, the server includes means for receiving user input and storing information about clothing entered by the user in a data storage device; means for analyzing the information about clothing stored in the data storage device and evaluating the frequency of use and necessity of the clothing; means for acquiring the user's emotional state in real time using emotion analysis means; means for generating suggestions regarding the disposal of the clothing based on the evaluation results of frequency of use and necessity and the emotional state; and means for notifying the user of the suggestions, supporting the disposal procedure based on the user's selection, and providing feedback utilizing emotional data. This enables the user to organize and declutter their clothes in a way that is in line with their emotions.

[0447] "User input" refers to data provided by the user to the information processing device, and includes items related to clothing.

[0448] A "data storage device" is a device that stores information permanently or temporarily, and has the function of storing clothing information based on user input.

[0449] "Emotion analysis means" refers to technology that analyzes and acquires the user's emotional state in real time from their voice, input patterns, etc.

[0450] "Evaluation means" refers to analytical techniques for calculating the frequency and necessity of clothing use using information stored in a data storage device.

[0451] The "proposal generation means" is a process that, based on analysis and sentiment data, shows the user how to dispose of the target clothing.

[0452] A "notification method" is a technology equipped with communication functions to present generated suggestions to the user and prompt the user to make a choice.

[0453] A "disposal procedure support system" is a system configuration that provides necessary procedures and related information based on the disposal method selected by the user, and provides feedback that takes the user's feelings into consideration.

[0454] In the system for realizing this invention, the user first inputs information about their clothing using a terminal. This can be a smartphone or an interface device. The terminal has an emotion analysis engine built in, which acquires the user's emotional state in real time from their voice or text input.

[0455] The server stores and analyzes clothing information received via data storage. The analysis uses AI algorithms to evaluate the frequency of use and necessity of the entered clothing items. Sentiment analysis data is also incorporated, taking into account the user's feelings towards the items.

[0456] Next, the server generates an optimal disposal suggestion based on the analysis results and sentiment data. This suggestion is adjusted according to the user's emotional state, making it more likely to be accepted by the user. The suggestion also includes a fair price derived from market data.

[0457] This proposal is notified to the user via their device, where they can review and select the proposed options. Based on the user's selection, the system provides support regarding disposal procedures. For example, it may provide links to e-commerce platforms or resource recycling centers to support the user's decision. Furthermore, the system can use the collected sentiment data to deliver positive messages to the user.

[0458] For example, if a user asks, "Should I sell this jacket?", the system will consider the user's emotional state and suggest, "It seems to be comfortable for you and its value has increased significantly." An example of a prompt from the generative AI model would be, "Analyze the user's input tone and suggest a clothing management strategy considering their emotional state."

[0459] This system allows users to manage their clothing in a rational and personalized way while reducing emotional burden.

[0460] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0461] Step 1:

[0462] The terminal accepts user input. The user enters information about their clothing using a smart device. Here, they enter information such as the type of clothing, brand, and purchase date into input fields, and this data is collected by the terminal's user interface. The entered data is then prepared to be sent to the data storage device.

[0463] Step 2:

[0464] The device performs emotion analysis. It analyzes the user's emotional state in real time, using both the user's input and voice input / input patterns. The emotion analysis engine generates emotion data based on indicators such as voice tone and input speed, and sends this data as output to the next step.

[0465] Step 3:

[0466] The server stores the user's clothing information in a data storage device. It receives clothing information and emotional data transmitted from the terminal and saves it to a database. This makes the information easily accessible in subsequent processes.

[0467] Step 4:

[0468] The server analyzes the stored data. It retrieves clothing information from the database and uses an AI algorithm to evaluate usage frequency and necessity. Emotional data is also taken into account, and the analysis results output an evaluation score, which is then used to generate decision-making information.

[0469] Step 5:

[0470] The server generates suggestions. Based on analysis results and sentiment data, it generates optimal clothing disposal suggestions to present to the user. This process also references market data and incorporates attribute-based valuation. The generated suggestions are then sent to the next step.

[0471] Step 6:

[0472] The terminal notifies the user of the suggestion. It displays the suggested content received from the server to the user and provides multiple options through an interactive interface. An input interface for the user to make a selection is presented, and the terminal waits for the user's response.

[0473] Step 7:

[0474] The user makes a selection based on the suggestions. They operate a user interface on their device to review the suggestions presented and select the most suitable disposal method. The selected information is then sent to the server via the device.

[0475] Step 8:

[0476] The server provides support based on the user's choices. Based on the selected disposal method, the server generates and sends links to relevant e-commerce platforms and resource recycling centers to the user's device. Furthermore, it provides feedback messages utilizing sentiment data to improve user satisfaction.

[0477] 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.

[0478] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0479] 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.

[0480] [Third Embodiment]

[0481] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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".

[0493] This invention is a system that evaluates the frequency of use and necessity of clothing based on information about the user's possessions and proposes appropriate disposal methods. This system supports efficient decluttering by allowing users to input clothing information through an easy-to-use interface, sending it to a server for analysis, and so on.

[0494] Configuration and Operation

[0495] 1. User input

[0496] Users access the application using devices such as smartphones or computers and enter information such as the purchase date, frequency of use, and price of their clothing. This information may also include details such as the item name, brand, and condition. The information entered by the user is immediately transmitted to the server from the interface.

[0497] 2. Data Analysis

[0498] The server stores information about received clothing in a database, and then uses an AI algorithm to calculate the frequency of use for each garment and evaluate its necessity. This includes calculating a "frequency of use index" that takes into account factors such as the number of days since purchase, the number of times it has been worn, and the purchase price. Furthermore, the server conducts market research and analyzes how much similar items are selling for at flea markets.

[0499] 3. Generating the proposal

[0500] Based on the data analysis, the server generates specific disposal suggestions for clothing deemed unnecessary. These suggestions include options such as donating to a recycling bin, setting a selling price on a flea market application, or using a dry cleaning service.

[0501] 4. User notifications and choices

[0502] The terminal notifies the user of the suggestions generated by the server. The user reviews the presented information and selects a disposal method of their choice. The notification appears either as a pop-up on the screen or as a message within the application.

[0503] 5. Support and Follow-up

[0504] The device provides the necessary links and steps based on the disposal method selected by the user. For example, it guides users on how to list items on a flea market application or displays the location of a recycling center. Furthermore, it sets reminders to confirm that the selected disposal method has been completed.

[0505] These processes allow users to efficiently manage their clothing, promoting the optimization of living space and the circular use of resources. This system offers ease of use and economic benefits, contributing to the formation of a sustainable consumer culture.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The user launches the application and enters clothing information (purchase date, number of times worn, price, etc.) into the interface. Once the input is complete, the terminal formats this data and sends it to the server.

[0509] Step 2:

[0510] The server stores the received clothing information in a database and checks for data formatting and missing values. Once the data is ready, it prepares to begin analysis using an AI algorithm.

[0511] Step 3:

[0512] The server calculates the frequency of use of clothing by estimating a wear frequency index based on the period from the purchase date to the present. It also assesses the necessity of each garment using purchase price and market price data. This assessment plays a crucial role in creating recommendations in the next step.

[0513] Step 4:

[0514] Based on the analysis results, the server generates specific disposal suggestions for clothing deemed to have little need. This includes suggesting appropriate selling prices based on market price analysis of similar products, and also includes the option of donating the items to recycling programs.

[0515] Step 5:

[0516] The terminal notifies the user of the suggestions from the server and displays them on the screen. The notification includes suggestions for each garment, recommended prices, and disposal methods. The user reviews this information and selects the best option from the suggested choices.

[0517] Step 6:

[0518] After reviewing the notification, the user selects the most suitable disposal method and sends their selection to the server via their device. Since selections can be made individually, users have flexible management options.

[0519] Step 7:

[0520] The server generates additional information based on the user's selections and provides links and detailed instructions to support disposal procedures. For example, it may provide instructions on how to register with a flea market application or guide users to the location of recycling facilities.

[0521] Step 8:

[0522] The device delivers support information to the user and sets reminders to confirm the execution of the selected disposal method. These reminders are useful for managing the progress of disposal.

[0523] These steps enable users to declutter efficiently, contributing to a more comfortable living space and sustainable consumption.

[0524] (Example 1)

[0525] 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."

[0526] Conventional clothing management systems have a problem in that they do not adequately evaluate the frequency and necessity of clothing use and suggest appropriate disposal methods. Furthermore, there is a challenge in effectively utilizing information about the clothing owned by users to reduce unnecessary clothing in an economical and sustainable way.

[0527] 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.

[0528] In this invention, the server includes means for collecting detailed information about individual items through terminals available to the user and transmitting this information to a hub; calculation means configured to store the received data in a data management device, perform automatic analysis based on the data, and evaluate the usage index of individual items; and means for proposing the optimal processing method using an automatic suggestion generation system based on the results of the data analysis. This enables the user to efficiently evaluate the frequency and necessity of clothing use and receive specific suggestions for properly disposing of unwanted clothing.

[0529] A "user-accessible terminal" is an electronic device that a user can access and operate, and that has functions for inputting information and confirming results.

[0530] "Detailed information about individual items" refers to information about a specific garment, including data such as purchase date, frequency of use, brand, and condition.

[0531] A "hub" is a central digital device that aggregates information and performs necessary processing, playing the role of receiving and transmitting data.

[0532] A "data management device" is a computer system for safely and efficiently storing and processing received information.

[0533] "Automated analysis" is the process of analyzing information provided using machine learning and algorithms to derive certain patterns and indicators.

[0534] A "usage index" is a numerical indicator calculated to evaluate the frequency of use of a particular garment, and it quantitatively represents the usage status.

[0535] An "automatic suggestion generation system" is a program that designs and provides users with the optimal solutions and options based on the results of analysis.

[0536] A "trading platform" is an online or offline marketplace service that users use to buy and sell goods.

[0537] A "circular use organization" is a business entity that provides facilities or services for reusing or recycling unwanted items.

[0538] This system is a platform designed to efficiently support users in managing their clothing. Users access the application using their smartphones or computers and enter detailed information about their clothing. This information includes purchase date, frequency of use, price, brand, and condition. The entered data is immediately sent to the server. The server stores this data in a data management device and analyzes it using AI algorithms provided within the system.

[0539] The server's AI algorithm calculates usage indices such as a "wear frequency index" to assess the necessity of each garment. This process considers factors such as the number of days since purchase, the number of times it has been worn, and the purchase price. It also analyzes market data obtained from flea markets and other trading platforms to assess the market value of similar items.

[0540] Based on the data analysis results, the server uses an automated suggestion generation system to propose the most suitable disposal method for the user. Specific suggestions include donating to a recycling center, selling on a flea market app, or using a cleaning service. The generated suggestions are notified to the user via their device and displayed as an on-screen message or pop-up. The user can then review these suggestions and choose a disposal method based on their own judgment.

[0541] For example, if a user has a pair of jeans they don't wear often, they input that information into the application. Based on the analysis results, the server suggests selling them at a flea market and also provides guidance on appropriate pricing.

[0542] An example of a prompt message might be, "I have a sweater in my closet that I bought three years ago but don't wear very often. What's the proper way to dispose of this sweater?"

[0543] Through this system, users can contribute to optimizing their living spaces and fostering a sustainable consumer culture by managing and rationally disposing of their clothing.

[0544] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0545] Step 1:

[0546] Users access the application using their smartphones or computers and enter information about their clothing. Specifically, they fill in details such as the purchase date, frequency of use, price, brand, and condition in a form within the app. The entered information is immediately transmitted to the server in digital format.

[0547] Step 2:

[0548] The server stores the clothing information received from the user in a data management device. At this stage, the information is checked for duplication and format consistency before being stored in the database. The input information is organized and converted into an analyzable format to prepare it for the next analysis process.

[0549] Step 3:

[0550] The server executes an AI algorithm based on the stored clothing information. Here, data such as the number of days since purchase, the number of times worn, and the purchase price are processed to calculate the frequency of wear index, and the necessity is evaluated. As a result, a usage index for each clothing item is output.

[0551] Step 4:

[0552] The server combines the analysis results with data obtained from the market to evaluate the market value of the clothing. Here, it references the circulating prices of similar items on trading platforms and uses an AI model to output a suggested price to offer to the user.

[0553] Step 5:

[0554] The server uses an automated suggestion generation system to propose the best disposal method for clothing deemed unwanted. This suggestion lists feasible options for the user, such as donating to a recycling center, selling at a flea market, or using a dry cleaning service. These disposal methods are output as digital suggestions.

[0555] Step 6:

[0556] The device notifies the user of suggestions received from the server. This includes displaying messages within the application, push notifications, and using pop-ups to attract the user's attention. The user then considers and selects a disposal method based on this information.

[0557] Step 7:

[0558] The device assists with disposal procedures based on the user's choices. This includes specific action guidelines, such as directing users to flea market apps and providing links to recycling centers. After selection, follow-up reminders are set to confirm completion of disposal.

[0559] (Application Example 1)

[0560] 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."

[0561] Users own a lot of clothing, but it's difficult to easily keep track of how often they use it, what their needs are, and manage it efficiently. Furthermore, they often lack the information to make the best decisions when deciding how to dispose of their clothes. There's also a demand for a more intuitive and efficient system that eliminates the need for users to manually input clothing information.

[0562] 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.

[0563] In this invention, the server includes means for receiving user input and storing information in a database, means for performing information analysis based on the database to evaluate frequency of use and necessity, and means for automatically acquiring information about clothing using speech recognition and image recognition technology. This allows users to manage their clothing without hassle and to quickly obtain information on appropriate disposal methods.

[0564] "User input" refers to information provided by the user through their device, and forms the basis for detailed data about clothing.

[0565] A "database" is an information management system that systematically stores information about collected clothing and uses it for analysis.

[0566] The "analysis method" refers to an algorithm that evaluates the frequency of use and necessity of clothing based on information stored in a database, and provides an algorithm for making appropriate disposal decisions.

[0567] A "suggestion" is a specific set of options, based on the results generated by the analysis tool, that instructs the user on the most efficient way to dispose of clothing.

[0568] "Voice recognition" is a technology that converts a user's voice input into digital data and automatically extracts information about clothing.

[0569] "Image recognition" is a technology that analyzes visual information of clothing acquired through a camera and automatically determines the characteristics and condition of the item.

[0570] This invention is a system for streamlining clothing management within the home. The system collects information entered by the user about their clothing, uses that data to evaluate its frequency of use and necessity, and proposes an appropriate disposal method.

[0571] The server stores the clothing information entered by the user in a database. Users use devices such as smartphones or personal computers to enter information such as the purchase date, frequency of use, and price of the clothing. This information is immediately transmitted to the server through the interface.

[0572] The server uses AI algorithms to perform data analysis. This involves processing information obtained using speech recognition and image recognition libraries to evaluate the frequency of use and necessity of clothing. Specifically, it determines the need for disposal based on usage frequency indices and market data for similar products.

[0573] The server generates specific disposal suggestions for the user based on the analysis results. These suggestions include multiple options, such as selling at a flea market or using recycling services. Furthermore, it optimizes the suggestions by utilizing information automatically acquired through voice and image recognition technology.

[0574] The user receives a suggestion from the server and selects a disposal method on the screen. For example, a suggestion might say, "You've only worn this shirt once in the last three months. We recommend selling it at a flea market or recycling it."

[0575] This system allows users to manage their clothing without hassle and dispose of it efficiently and economically. It also contributes to the formation of a sustainable consumer culture.

[0576] An example of a prompt is: "Design a prompt for a robot application that uses a speech recognition engine to retrieve clothing information from the user's speech and compare it with market data for evaluation. Then, it will suggest efficient clothing management to the user."

[0577] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0578] Step 1:

[0579] Users input information such as the purchase date, frequency of use, and price of clothing via their smartphone or computer, and the device sends this information to the server through the system interface. The input data is in text format, and the server stores it in a database upon receipt. The output of this step is the clothing information recorded in the database.

[0580] Step 2:

[0581] The server analyzes the information stored in the database using an AI algorithm to calculate a clothing usage frequency index. This calculation takes into account factors such as the number of times the garment has been worn in the past and the number of days since purchase. The input is the database information, and the program evaluates the usage frequency and necessity of each garment based on the analysis results. The output of this step is the usage frequency index as a result of the analysis.

[0582] Step 3:

[0583] The server automatically acquires additional information about clothing using speech and image recognition technologies. The user speaks to the robot, the robot's camera takes pictures of the clothing, and the terminal sends the image information to the server. The input consists of image data from the camera and audio data from the microphone, which the program analyzes to extract detailed information about the clothing. The output of this step is detailed information about the clothing based on image and speech recognition.

[0584] Step 4:

[0585] The server generates disposal suggestions based on usage frequency index and market data. This includes referencing market prices and recommending appropriate sales platforms. The inputs are the usage frequency index, acquired detailed information, and market data, and the program optimizes and outputs disposal method suggestions. The output of this step is the specific disposal suggestion presented to the user.

[0586] Step 5:

[0587] The terminal notifies the user of disposal suggestions received from the server and presents them on the screen as options. The user selects a disposal method from the presented options, and the terminal sends the selection result back to the server. The input is the suggestion data from the server, and the output is the user's selection result.

[0588] Step 6:

[0589] The server provides necessary links and instructions as support information based on the user's selection. This includes links to flea market applications and directions to recycling locations. The input is the user's selection, and the output is the links and instructions as support information. This step ensures that the proposed disposal method can be carried out smoothly.

[0590] 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.

[0591] This invention is a system that combines an emotion engine with a system to support decluttering clothing, thereby providing suggestions that take the user's emotional state into account. The system aims to recognize the user's emotions using an emotion engine, based on data analysis of the user's clothing information, and then propose appropriate disposal methods.

[0592] Configuration and Operation

[0593] 1. User Input and Sentiment Recognition

[0594] Users input clothing information through the application. During this process, an emotion engine on the device analyzes the user's emotional state in real time based on their voice tone and input patterns. The emotional information obtained by the emotion engine is used to adjust subsequent suggestions.

[0595] 2. Data storage and analysis

[0596] The server stores clothing information submitted by the user and data from the emotion engine in a database. Next, an AI algorithm is used to calculate the frequency and necessity of clothing use, while simultaneously considering the user's emotional state. The tone and nature of the suggestions are adjusted according to the emotional state.

[0597] 3. Generating the proposal

[0598] The server generates optimal decluttering suggestions based on analysis results and sentiment data. For example, if a user is reluctant to let go of clothing, the suggestions are adjusted to be more careful and user-friendly. When suggesting appropriate prices, the server uses market data and employs language that takes user psychology into consideration.

[0599] 4. Notifications and Feedback

[0600] The device notifies the user of the generated suggestions. The suggestions include messages that are sensitive to the user's feelings and are presented in a way that respects flexibility in choices. This makes it easier for the user to make rational decisions while reducing emotional burden.

[0601] 5. Selection and Support

[0602] The user reviews the suggested disposal methods and selects their preferred method. Based on the chosen method, the server provides the user with the necessary instructions and support links. Sentimental data is also used in this support, for example, by including messages that encourage positive feedback.

[0603] This workflow allows users to manage their clothing efficiently and with reduced psychological burden while being aware of their own emotions. By leveraging emotional intelligence, this system provides a more personalized user experience and enables comfortable and sustainable clothing management.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The user launches the application and enters clothing information (purchase date, frequency of use, price, etc.) into the interface. The interface has voice input and text input options, and as input is being made, an emotion engine analyzes the user's voice tone through the device's microphone.

[0607] Step 2:

[0608] The terminal processes user input information using an emotion engine and analyzes the user's emotional state (e.g., joy, anxiety, indifference, etc.) in real time. This emotion data, along with other input data, is immediately sent to the server.

[0609] Step 3:

[0610] The server stores the received clothing information and emotional data in a database and then prepares to analyze it. The analysis uses an AI algorithm to calculate the frequency and necessity of clothing use, and the emotional data is used to adjust the recommendations.

[0611] Step 4:

[0612] The server assesses the need for each garment and generates disposal suggestions based on that assessment and emotional data. For example, if the user is reluctant to part with an item, the suggestion will be presented in a more cautious and positive tone. Furthermore, the appropriate price suggestion, based on market data, is also flexibly adjusted according to the user's emotional state.

[0613] Step 5:

[0614] The device notifies the user of the generated suggestions and displays detailed suggestions on the screen. These notifications include personalized messages that take the user's emotions into consideration, ensuring they can make decisions with confidence.

[0615] Step 6:

[0616] The user reviews the suggestions and selects the desired action from the disposal options. Selection can be easily done with a tap or click. The selected information is then sent back to the server.

[0617] Step 7:

[0618] The server generates additional support information based on the user's selection, providing detailed instructions and links regarding the chosen disposal method. This information is sent to the user via the terminal and may include emotionally responsive feedback to enhance user satisfaction.

[0619] Step 8:

[0620] The device uses the provided information to support the execution of the selected disposal method and plans follow-up after disposal is complete. This plan includes a reminder function to help the user proceed smoothly with the plan.

[0621] These steps allow users to manage their clothing efficiently and stress-free while being attentive to their own emotions.

[0622] (Example 2)

[0623] 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."

[0624] Users often experience emotional burden when deciding how to dispose of their belongings and seek rational and emotionally considerate management methods. Traditional methods struggle to offer emotionally conscious suggestions, and relying solely on self-judgment has its limitations. Therefore, it is necessary to realize efficient and comfortable item management that takes users' emotions into consideration.

[0625] 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.

[0626] In this invention, the server includes means for storing information about items received from the user in a central storage location, means for performing analysis to detect the user's emotional state along with the information about the received items, and means for generating suggestions that take the emotional state into consideration based on the analysis results. As a result, the user receives emotionally sensitive suggestions and can dispose of items in a comfortable and rational manner.

[0627] A "user" is someone who uses the system to input information about goods and receives suggestions.

[0628] "Items" refers to clothing and other possessions that the user owns and is considering disposing of.

[0629] "Information" refers to data about an item, specifically including data such as its name, type, frequency of use, and emotional state.

[0630] A "central storage location" refers to a database system for securely recording and storing received information.

[0631] "Emotional state" refers to the psychological and emotional state of the user when entering item information, and is detected through voice and input patterns.

[0632] "Analysis" refers to a series of processes that involve processing received information to extract necessary patterns and trends.

[0633] "Suggestions" refer to messages generated based on analysis results regarding items, which present disposal policies and options to the user.

[0634] "Generating proposals" refers to the process of creating specific suggestions for actions to be presented to the user based on the analyzed data.

[0635] This invention generates suggestions regarding the disposal of items owned by a user, taking into account their emotional state.

[0636] 1. User input and sentiment analysis:

[0637] Users input information about their belongings, such as clothing, through a dedicated application. During this process, an emotion engine built into the device analyzes the user's voice tone and input patterns to measure their emotional state in real time. This analysis utilizes speech recognition and input pattern analysis technologies.

[0638] 2. Data collection and storage:

[0639] The server stores item information and sentiment data transmitted from the terminals in a cloud-based database. A database management system is used to ensure the consistency and security of the information.

[0640] 3. Data analysis and proposal generation:

[0641] The server uses AI algorithms and generative AI models to analyze clothing information and user sentiment data. It creates suggestions that reflect user emotions while considering usage frequency and trends. For example, it can list items that haven't been used for over six months and suggest disposal.

[0642] 4. Proposal notification and provision of options:

[0643] The device notifies the user of individually customized suggestions. This can include messages encouraging proactive action and flexible options.

[0644] 5. Providing support:

[0645] Once the user reviews the proposal and makes a selection, the server provides links to information on appropriate disposal methods, transaction applications, and reusable locations. This reduces the emotional burden on the user and allows them to dispose of items in a rational manner.

[0646] For example, if a user is getting tired of a "light blue sweater" but still feels attached to it, the system will detect this emotion and suggest to the user that "donating the sweater might mean someone else will cherish and wear it."

[0647] Example of a prompt:

[0648] "Tell us how you feel about clothing, and then add any items you feel you don't need."

[0649] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0650] Step 1:

[0651] Users use a terminal to input item information into a dedicated application. The information entered includes the type of item, frequency of use, and purchase date.

[0652] The device captures the user's voice tone and input speed in real time while they are typing, and uses an emotion engine to analyze the user's emotional state.

[0653] The entered item information and emotion data are converted into a digital format and temporarily stored.

[0654] Step 2:

[0655] The server receives item information and emotion data transmitted from the terminal and stores it in a cloud database.

[0656] The received data is organized and formatted into a standardized format. During this process, data consistency is verified, and unnecessary data is removed.

[0657] The output consists of organized item information and sentiment data, which are stored in a database.

[0658] Step 3:

[0659] Based on the stored information, the server uses an AI algorithm to analyze the frequency of use and necessity of items.

[0660] The analysis involves referencing past usage data and market data, and taking into account user sentiment patterns.

[0661] As output, an AI model will generate proposals for the disposal of items that take emotions into consideration.

[0662] Step 4:

[0663] The server sends the generated suggestions to the terminal.

[0664] The device notifies the user of received proposals and displays the proposal details. It also displays a message that includes flexible disposal options the user can choose from.

[0665] The output provides users with personalized suggestions based on their emotions.

[0666] Step 5:

[0667] The user reviews the proposal and selects their preferred disposal method. Options include holding, donating, and recycling.

[0668] Based on the user's selection, the server sends information links and procedures related to the disposal method to the terminal.

[0669] The output provides users with detailed information on the most suitable disposal method, offering support to reduce their emotional burden.

[0670] (Application Example 2)

[0671] 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."

[0672] In organizing and decluttering clothing, there is a challenge in enabling users to make sound decisions without experiencing emotional burden. Traditional systems do not take emotions into consideration, which can lead to users not feeling satisfied or content, and as a result, inefficient processing.

[0673] 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.

[0674] In this invention, the server includes means for receiving user input and storing information about clothing entered by the user in a data storage device; means for analyzing the information about clothing stored in the data storage device and evaluating the frequency of use and necessity of the clothing; means for acquiring the user's emotional state in real time using emotion analysis means; means for generating suggestions regarding the disposal of the clothing based on the evaluation results of frequency of use and necessity and the emotional state; and means for notifying the user of the suggestions, supporting the disposal procedure based on the user's selection, and providing feedback utilizing emotional data. This enables the user to organize and declutter their clothes in a way that is in line with their emotions.

[0675] "User input" refers to data provided by the user to the information processing device, and includes items related to clothing.

[0676] A "data storage device" is a device that stores information permanently or temporarily, and has the function of storing clothing information based on user input.

[0677] "Emotion analysis means" refers to technology that analyzes and acquires the user's emotional state in real time from their voice, input patterns, etc.

[0678] "Evaluation means" refers to analytical techniques for calculating the frequency and necessity of clothing use using information stored in a data storage device.

[0679] The "proposal generation means" is a process that, based on analysis and sentiment data, shows the user how to dispose of the target clothing.

[0680] A "notification method" is a technology equipped with communication functions to present generated suggestions to the user and prompt the user to make a choice.

[0681] A "disposal procedure support system" is a system configuration that provides necessary procedures and related information based on the disposal method selected by the user, and provides feedback that takes the user's feelings into consideration.

[0682] In the system for realizing this invention, the user first inputs information about their clothing using a terminal. This can be a smartphone or an interface device. The terminal has an emotion analysis engine built in, which acquires the user's emotional state in real time from their voice or text input.

[0683] The server stores and analyzes clothing information received via data storage. The analysis uses AI algorithms to evaluate the frequency of use and necessity of the entered clothing items. Sentiment analysis data is also incorporated, taking into account the user's feelings towards the items.

[0684] Next, the server generates an optimal disposal suggestion based on the analysis results and sentiment data. This suggestion is adjusted according to the user's emotional state, making it more likely to be accepted by the user. The suggestion also includes a fair price derived from market data.

[0685] This proposal is notified to the user via their device, where they can review and select the proposed options. Based on the user's selection, the system provides support regarding disposal procedures. For example, it may provide links to e-commerce platforms or resource recycling centers to support the user's decision. Furthermore, the system can use the collected sentiment data to deliver positive messages to the user.

[0686] For example, if a user asks, "Should I sell this jacket?", the system will consider the user's emotional state and suggest, "It seems to be comfortable for you and its value has increased significantly." An example of a prompt from the generative AI model would be, "Analyze the user's input tone and suggest a clothing management strategy considering their emotional state."

[0687] This system allows users to manage their clothing in a rational and personalized way while reducing emotional burden.

[0688] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0689] Step 1:

[0690] The terminal accepts user input. The user enters information about their clothing using a smart device. Here, they enter information such as the type of clothing, brand, and purchase date into input fields, and this data is collected by the terminal's user interface. The entered data is then prepared to be sent to the data storage device.

[0691] Step 2:

[0692] The device performs emotion analysis. It analyzes the user's emotional state in real time, using both the user's input and voice input / input patterns. The emotion analysis engine generates emotion data based on indicators such as voice tone and input speed, and sends this data as output to the next step.

[0693] Step 3:

[0694] The server stores the user's clothing information in a data storage device. It receives clothing information and emotional data transmitted from the terminal and saves it to a database. This makes the information easily accessible in subsequent processes.

[0695] Step 4:

[0696] The server analyzes the stored data. It retrieves clothing information from the database and uses an AI algorithm to evaluate usage frequency and necessity. Emotional data is also taken into account, and the analysis results output an evaluation score, which is then used to generate decision-making information.

[0697] Step 5:

[0698] The server generates suggestions. Based on analysis results and sentiment data, it generates optimal clothing disposal suggestions to present to the user. This process also references market data and incorporates attribute-based valuation. The generated suggestions are then sent to the next step.

[0699] Step 6:

[0700] The terminal notifies the user of the suggestion. It displays the suggested content received from the server to the user and provides multiple options through an interactive interface. An input interface for the user to make a selection is presented, and the terminal waits for the user's response.

[0701] Step 7:

[0702] The user makes a selection based on the suggestions. They operate a user interface on their device to review the suggestions presented and select the most suitable disposal method. The selected information is then sent to the server via the device.

[0703] Step 8:

[0704] The server provides support based on the user's choices. Based on the selected disposal method, the server generates and sends links to relevant e-commerce platforms and resource recycling centers to the user's device. Furthermore, it provides feedback messages utilizing sentiment data to improve user satisfaction.

[0705] 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.

[0706] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0707] 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.

[0708] [Fourth Embodiment]

[0709] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0710] 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.

[0711] 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).

[0712] 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.

[0713] 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.

[0714] 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).

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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".

[0722] This invention is a system that evaluates the frequency of use and necessity of clothing based on information about the user's possessions and proposes appropriate disposal methods. This system supports efficient decluttering by allowing users to input clothing information through an easy-to-use interface, sending it to a server for analysis, and so on.

[0723] Configuration and Operation

[0724] 1. User input

[0725] Users access the application using devices such as smartphones or computers and enter information such as the purchase date, frequency of use, and price of their clothing. This information may also include details such as the item name, brand, and condition. The information entered by the user is immediately transmitted to the server from the interface.

[0726] 2. Data Analysis

[0727] The server stores information about received clothing in a database, and then uses an AI algorithm to calculate the frequency of use for each garment and evaluate its necessity. This includes calculating a "frequency of use index" that takes into account factors such as the number of days since purchase, the number of times it has been worn, and the purchase price. Furthermore, the server conducts market research and analyzes how much similar items are selling for at flea markets.

[0728] 3. Generating the proposal

[0729] Based on the data analysis, the server generates specific disposal suggestions for clothing deemed unnecessary. These suggestions include options such as donating to a recycling bin, setting a selling price on a flea market application, or using a dry cleaning service.

[0730] 4. User notifications and choices

[0731] The terminal notifies the user of the suggestions generated by the server. The user reviews the presented information and selects a disposal method of their choice. The notification appears either as a pop-up on the screen or as a message within the application.

[0732] 5. Support and Follow-up

[0733] The device provides the necessary links and steps based on the disposal method selected by the user. For example, it guides users on how to list items on a flea market application or displays the location of a recycling center. Furthermore, it sets reminders to confirm that the selected disposal method has been completed.

[0734] These processes allow users to efficiently manage their clothing, promoting the optimization of living space and the circular use of resources. This system offers ease of use and economic benefits, contributing to the formation of a sustainable consumer culture.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The user launches the application and enters clothing information (purchase date, number of times worn, price, etc.) into the interface. Once the input is complete, the terminal formats this data and sends it to the server.

[0738] Step 2:

[0739] The server stores the received clothing information in a database and checks for data formatting and missing values. Once the data is ready, it prepares to begin analysis using an AI algorithm.

[0740] Step 3:

[0741] The server calculates the frequency of use of clothing by estimating a wear frequency index based on the period from the purchase date to the present. It also assesses the necessity of each garment using purchase price and market price data. This assessment plays a crucial role in creating recommendations in the next step.

[0742] Step 4:

[0743] Based on the analysis results, the server generates specific disposal suggestions for clothing deemed to have little need. This includes suggesting appropriate selling prices based on market price analysis of similar products, and also includes the option of donating the items to recycling programs.

[0744] Step 5:

[0745] The terminal notifies the user of the suggestions from the server and displays them on the screen. The notification includes suggestions for each garment, recommended prices, and disposal methods. The user reviews this information and selects the best option from the suggested choices.

[0746] Step 6:

[0747] After reviewing the notification, the user selects the most suitable disposal method and sends their selection to the server via their device. Since selections can be made individually, users have flexible management options.

[0748] Step 7:

[0749] The server generates additional information based on the user's selections and provides links and detailed instructions to support disposal procedures. For example, it may provide instructions on how to register with a flea market application or guide users to the location of recycling facilities.

[0750] Step 8:

[0751] The device delivers support information to the user and sets reminders to confirm the execution of the selected disposal method. These reminders are useful for managing the progress of disposal.

[0752] These steps enable users to declutter efficiently, contributing to a more comfortable living space and sustainable consumption.

[0753] (Example 1)

[0754] 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".

[0755] Conventional clothing management systems have a problem in that they do not adequately evaluate the frequency and necessity of clothing use and suggest appropriate disposal methods. Furthermore, there is a challenge in effectively utilizing information about the clothing owned by users to reduce unnecessary clothing in an economical and sustainable way.

[0756] 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.

[0757] In this invention, the server includes means for collecting detailed information about individual items through terminals available to the user and transmitting this information to a hub; calculation means configured to store the received data in a data management device, perform automatic analysis based on the data, and evaluate the usage index of individual items; and means for proposing the optimal processing method using an automatic suggestion generation system based on the results of the data analysis. This enables the user to efficiently evaluate the frequency and necessity of clothing use and receive specific suggestions for properly disposing of unwanted clothing.

[0758] A "user-accessible terminal" is an electronic device that a user can access and operate, and that has functions for inputting information and confirming results.

[0759] "Detailed information about individual items" refers to information about a specific garment, including data such as purchase date, frequency of use, brand, and condition.

[0760] A "hub" is a central digital device that aggregates information and performs necessary processing, playing the role of receiving and transmitting data.

[0761] A "data management device" is a computer system for safely and efficiently storing and processing received information.

[0762] "Automated analysis" is the process of analyzing information provided using machine learning and algorithms to derive certain patterns and indicators.

[0763] A "usage index" is a numerical indicator calculated to evaluate the frequency of use of a particular garment, and it quantitatively represents the usage status.

[0764] An "automatic suggestion generation system" is a program that designs and provides users with the optimal solutions and options based on the results of analysis.

[0765] A "trading platform" is an online or offline marketplace service that users use to buy and sell goods.

[0766] A "circular use organization" is a business entity that provides facilities or services for reusing or recycling unwanted items.

[0767] This system is a platform designed to efficiently support users in managing their clothing. Users access the application using their smartphones or computers and enter detailed information about their clothing. This information includes purchase date, frequency of use, price, brand, and condition. The entered data is immediately sent to the server. The server stores this data in a data management device and analyzes it using AI algorithms provided within the system.

[0768] The server's AI algorithm calculates usage indices such as a "wear frequency index" to assess the necessity of each garment. This process considers factors such as the number of days since purchase, the number of times it has been worn, and the purchase price. It also analyzes market data obtained from flea markets and other trading platforms to assess the market value of similar items.

[0769] Based on the data analysis results, the server uses an automated suggestion generation system to propose the most suitable disposal method for the user. Specific suggestions include donating to a recycling center, selling on a flea market app, or using a cleaning service. The generated suggestions are notified to the user via their device and displayed as an on-screen message or pop-up. The user can then review these suggestions and choose a disposal method based on their own judgment.

[0770] For example, if a user has a pair of jeans they don't wear often, they input that information into the application. Based on the analysis results, the server suggests selling them at a flea market and also provides guidance on appropriate pricing.

[0771] An example of a prompt message might be, "I have a sweater in my closet that I bought three years ago but don't wear very often. What's the proper way to dispose of this sweater?"

[0772] Through this system, users can contribute to optimizing their living spaces and fostering a sustainable consumer culture by managing and rationally disposing of their clothing.

[0773] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0774] Step 1:

[0775] Users access the application using their smartphones or computers and enter information about their clothing. Specifically, they fill in details such as the purchase date, frequency of use, price, brand, and condition in a form within the app. The entered information is immediately transmitted to the server in digital format.

[0776] Step 2:

[0777] The server stores the clothing information received from the user in a data management device. At this stage, the information is checked for duplication and format consistency before being stored in the database. The input information is organized and converted into an analyzable format to prepare it for the next analysis process.

[0778] Step 3:

[0779] The server executes an AI algorithm based on the stored clothing information. Here, data such as the number of days since purchase, the number of times worn, and the purchase price are processed to calculate the frequency of wear index, and the necessity is evaluated. As a result, a usage index for each clothing item is output.

[0780] Step 4:

[0781] The server combines the analysis results with data obtained from the market to evaluate the market value of the clothing. Here, it references the circulating prices of similar items on trading platforms and uses an AI model to output a suggested price to offer to the user.

[0782] Step 5:

[0783] The server uses an automated suggestion generation system to propose the best disposal method for clothing deemed unwanted. This suggestion lists feasible options for the user, such as donating to a recycling center, selling at a flea market, or using a dry cleaning service. These disposal methods are output as digital suggestions.

[0784] Step 6:

[0785] The device notifies the user of suggestions received from the server. This includes displaying messages within the application, push notifications, and using pop-ups to attract the user's attention. The user then considers and selects a disposal method based on this information.

[0786] Step 7:

[0787] The device assists with disposal procedures based on the user's choices. This includes specific action guidelines, such as directing users to flea market apps and providing links to recycling centers. After selection, follow-up reminders are set to confirm completion of disposal.

[0788] (Application Example 1)

[0789] 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".

[0790] Users own a lot of clothing, but it's difficult to easily keep track of how often they use it, what their needs are, and manage it efficiently. Furthermore, they often lack the information to make the best decisions when deciding how to dispose of their clothes. There's also a demand for a more intuitive and efficient system that eliminates the need for users to manually input clothing information.

[0791] 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.

[0792] In this invention, the server includes means for receiving user input and storing information in a database, means for performing information analysis based on the database to evaluate frequency of use and necessity, and means for automatically acquiring information about clothing using speech recognition and image recognition technology. This allows users to manage their clothing without hassle and to quickly obtain information on appropriate disposal methods.

[0793] "User input" refers to information provided by the user through their device, and forms the basis for detailed data about clothing.

[0794] A "database" is an information management system that systematically stores information about collected clothing and uses it for analysis.

[0795] The "analysis method" refers to an algorithm that evaluates the frequency of use and necessity of clothing based on information stored in a database, and provides an algorithm for making appropriate disposal decisions.

[0796] A "suggestion" is a specific set of options, based on the results generated by the analysis tool, that instructs the user on the most efficient way to dispose of clothing.

[0797] "Voice recognition" is a technology that converts a user's voice input into digital data and automatically extracts information about clothing.

[0798] "Image recognition" is a technology that analyzes visual information of clothing acquired through a camera and automatically determines the characteristics and condition of the item.

[0799] This invention is a system for streamlining clothing management within the home. The system collects information entered by the user about their clothing, uses that data to evaluate its frequency of use and necessity, and proposes an appropriate disposal method.

[0800] The server stores the clothing information entered by the user in a database. Users use devices such as smartphones or personal computers to enter information such as the purchase date, frequency of use, and price of the clothing. This information is immediately transmitted to the server through the interface.

[0801] The server uses AI algorithms to perform data analysis. This involves processing information obtained using speech recognition and image recognition libraries to evaluate the frequency of use and necessity of clothing. Specifically, it determines the need for disposal based on usage frequency indices and market data for similar products.

[0802] The server generates specific disposal suggestions for the user based on the analysis results. These suggestions include multiple options, such as selling at a flea market or using recycling services. Furthermore, it optimizes the suggestions by utilizing information automatically acquired through voice and image recognition technology.

[0803] The user receives a suggestion from the server and selects a disposal method on the screen. For example, a suggestion might say, "You've only worn this shirt once in the last three months. We recommend selling it at a flea market or recycling it."

[0804] This system allows users to manage their clothing without hassle and dispose of it efficiently and economically. It also contributes to the formation of a sustainable consumer culture.

[0805] An example of a prompt is: "Design a prompt for a robot application that uses a speech recognition engine to retrieve clothing information from the user's speech and compare it with market data for evaluation. Then, it will suggest efficient clothing management to the user."

[0806] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0807] Step 1:

[0808] Users input information such as the purchase date, frequency of use, and price of clothing via their smartphone or computer, and the device sends this information to the server through the system interface. The input data is in text format, and the server stores it in a database upon receipt. The output of this step is the clothing information recorded in the database.

[0809] Step 2:

[0810] The server analyzes the information stored in the database using an AI algorithm to calculate a clothing usage frequency index. This calculation takes into account factors such as the number of times the garment has been worn in the past and the number of days since purchase. The input is the database information, and the program evaluates the usage frequency and necessity of each garment based on the analysis results. The output of this step is the usage frequency index as a result of the analysis.

[0811] Step 3:

[0812] The server automatically acquires additional information about clothing using speech and image recognition technologies. The user speaks to the robot, the robot's camera takes pictures of the clothing, and the terminal sends the image information to the server. The input consists of image data from the camera and audio data from the microphone, which the program analyzes to extract detailed information about the clothing. The output of this step is detailed information about the clothing based on image and speech recognition.

[0813] Step 4:

[0814] The server generates disposal suggestions based on usage frequency index and market data. This includes referencing market prices and recommending appropriate sales platforms. The inputs are the usage frequency index, acquired detailed information, and market data, and the program optimizes and outputs disposal method suggestions. The output of this step is the specific disposal suggestion presented to the user.

[0815] Step 5:

[0816] The terminal notifies the user of disposal suggestions received from the server and presents them on the screen as options. The user selects a disposal method from the presented options, and the terminal sends the selection result back to the server. The input is the suggestion data from the server, and the output is the user's selection result.

[0817] Step 6:

[0818] The server provides necessary links and instructions as support information based on the user's selection. This includes links to flea market applications and directions to recycling locations. The input is the user's selection, and the output is the links and instructions as support information. This step ensures that the proposed disposal method can be carried out smoothly.

[0819] 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.

[0820] This invention is a system that combines an emotion engine with a system to support decluttering clothing, thereby providing suggestions that take the user's emotional state into account. The system aims to recognize the user's emotions using an emotion engine, based on data analysis of the user's clothing information, and then propose appropriate disposal methods.

[0821] Configuration and Operation

[0822] 1. User Input and Sentiment Recognition

[0823] Users input clothing information through the application. During this process, an emotion engine on the device analyzes the user's emotional state in real time based on their voice tone and input patterns. The emotional information obtained by the emotion engine is used to adjust subsequent suggestions.

[0824] 2. Data storage and analysis

[0825] The server stores clothing information submitted by the user and data from the emotion engine in a database. Next, an AI algorithm is used to calculate the frequency and necessity of clothing use, while simultaneously considering the user's emotional state. The tone and nature of the suggestions are adjusted according to the emotional state.

[0826] 3. Generating the proposal

[0827] The server generates optimal decluttering suggestions based on analysis results and sentiment data. For example, if a user is reluctant to let go of clothing, the suggestions are adjusted to be more careful and user-friendly. When suggesting appropriate prices, the server uses market data and employs language that takes user psychology into consideration.

[0828] 4. Notifications and Feedback

[0829] The device notifies the user of the generated suggestions. The suggestions include messages that are sensitive to the user's feelings and are presented in a way that respects flexibility in choices. This makes it easier for the user to make rational decisions while reducing emotional burden.

[0830] 5. Selection and Support

[0831] The user reviews the suggested disposal methods and selects their preferred method. Based on the chosen method, the server provides the user with the necessary instructions and support links. Sentimental data is also used in this support, for example, by including messages that encourage positive feedback.

[0832] This workflow allows users to manage their clothing efficiently and with reduced psychological burden while being aware of their own emotions. By leveraging emotional intelligence, this system provides a more personalized user experience and enables comfortable and sustainable clothing management.

[0833] The following describes the processing flow.

[0834] Step 1:

[0835] The user launches the application and enters clothing information (purchase date, frequency of use, price, etc.) into the interface. The interface has voice input and text input options, and as input is being made, an emotion engine analyzes the user's voice tone through the device's microphone.

[0836] Step 2:

[0837] The terminal processes user input information using an emotion engine and analyzes the user's emotional state (e.g., joy, anxiety, indifference, etc.) in real time. This emotion data, along with other input data, is immediately sent to the server.

[0838] Step 3:

[0839] The server stores the received clothing information and emotional data in a database and then prepares to analyze it. The analysis uses an AI algorithm to calculate the frequency and necessity of clothing use, and the emotional data is used to adjust the recommendations.

[0840] Step 4:

[0841] The server assesses the need for each garment and generates disposal suggestions based on that assessment and emotional data. For example, if the user is reluctant to part with an item, the suggestion will be presented in a more cautious and positive tone. Furthermore, the appropriate price suggestion, based on market data, is also flexibly adjusted according to the user's emotional state.

[0842] Step 5:

[0843] The device notifies the user of the generated suggestions and displays detailed suggestions on the screen. These notifications include personalized messages that take the user's emotions into consideration, ensuring they can make decisions with confidence.

[0844] Step 6:

[0845] The user reviews the suggestions and selects the desired action from the disposal options. Selection can be easily done with a tap or click. The selected information is then sent back to the server.

[0846] Step 7:

[0847] The server generates additional support information based on the user's selection, providing detailed instructions and links regarding the chosen disposal method. This information is sent to the user via the terminal and may include emotionally responsive feedback to enhance user satisfaction.

[0848] Step 8:

[0849] The device uses the provided information to support the execution of the selected disposal method and plans follow-up after disposal is complete. This plan includes a reminder function to help the user proceed smoothly with the plan.

[0850] These steps allow users to manage their clothing efficiently and stress-free while being attentive to their own emotions.

[0851] (Example 2)

[0852] 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".

[0853] Users often experience emotional burden when deciding how to dispose of their belongings and seek rational and emotionally considerate management methods. Traditional methods struggle to offer emotionally conscious suggestions, and relying solely on self-judgment has its limitations. Therefore, it is necessary to realize efficient and comfortable item management that takes users' emotions into consideration.

[0854] 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.

[0855] In this invention, the server includes means for storing information about items received from the user in a central storage location, means for performing analysis to detect the user's emotional state along with the information about the received items, and means for generating suggestions that take the emotional state into consideration based on the analysis results. As a result, the user receives emotionally sensitive suggestions and can dispose of items in a comfortable and rational manner.

[0856] A "user" is someone who uses the system to input information about goods and receives suggestions.

[0857] "Items" refers to clothing and other possessions that the user owns and is considering disposing of.

[0858] "Information" refers to data about an item, specifically including data such as its name, type, frequency of use, and emotional state.

[0859] A "central storage location" refers to a database system for securely recording and storing received information.

[0860] "Emotional state" refers to the psychological and emotional state of the user when entering item information, and is detected through voice and input patterns.

[0861] "Analysis" refers to a series of processes that involve processing received information to extract necessary patterns and trends.

[0862] "Suggestions" refer to messages generated based on analysis results regarding items, which present disposal policies and options to the user.

[0863] "Generating proposals" refers to the process of creating specific suggestions for actions to be presented to the user based on the analyzed data.

[0864] This invention generates suggestions regarding the disposal of items owned by a user, taking into account their emotional state.

[0865] 1. User input and sentiment analysis:

[0866] Users input information about their belongings, such as clothing, through a dedicated application. During this process, an emotion engine built into the device analyzes the user's voice tone and input patterns to measure their emotional state in real time. This analysis utilizes speech recognition and input pattern analysis technologies.

[0867] 2. Data collection and storage:

[0868] The server stores item information and sentiment data transmitted from the terminals in a cloud-based database. A database management system is used to ensure the consistency and security of the information.

[0869] 3. Data analysis and proposal generation:

[0870] The server uses AI algorithms and generative AI models to analyze clothing information and user sentiment data. It creates suggestions that reflect user emotions while considering usage frequency and trends. For example, it can list items that haven't been used for over six months and suggest disposal.

[0871] 4. Proposal notification and provision of options:

[0872] The device notifies the user of individually customized suggestions. This can include messages encouraging proactive action and flexible options.

[0873] 5. Providing support:

[0874] Once the user reviews the proposal and makes a selection, the server provides links to information on appropriate disposal methods, transaction applications, and reusable locations. This reduces the emotional burden on the user and allows them to dispose of items in a rational manner.

[0875] For example, if a user is getting tired of a "light blue sweater" but still feels attached to it, the system will detect this emotion and suggest to the user that "donating the sweater might mean someone else will cherish and wear it."

[0876] Example of a prompt:

[0877] "Tell us how you feel about clothing, and then add any items you feel you don't need."

[0878] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0879] Step 1:

[0880] Users use a terminal to input item information into a dedicated application. The information entered includes the type of item, frequency of use, and purchase date.

[0881] The device captures the user's voice tone and input speed in real time while they are typing, and uses an emotion engine to analyze the user's emotional state.

[0882] The entered item information and emotion data are converted into a digital format and temporarily stored.

[0883] Step 2:

[0884] The server receives item information and emotion data transmitted from the terminal and stores it in a cloud database.

[0885] The received data is organized and formatted into a standardized format. During this process, data consistency is verified, and unnecessary data is removed.

[0886] The output consists of organized item information and sentiment data, which are stored in a database.

[0887] Step 3:

[0888] Based on the stored information, the server uses an AI algorithm to analyze the frequency of use and necessity of items.

[0889] The analysis involves referencing past usage data and market data, and taking into account user sentiment patterns.

[0890] As output, an AI model will generate proposals for the disposal of items that take emotions into consideration.

[0891] Step 4:

[0892] The server sends the generated suggestions to the terminal.

[0893] The device notifies the user of received proposals and displays the proposal details. It also displays a message that includes flexible disposal options the user can choose from.

[0894] The output provides users with personalized suggestions based on their emotions.

[0895] Step 5:

[0896] The user reviews the proposal and selects their preferred disposal method. Options include holding, donating, and recycling.

[0897] Based on the user's selection, the server sends information links and procedures related to the disposal method to the terminal.

[0898] The output provides users with detailed information on the most suitable disposal method, offering support to reduce their emotional burden.

[0899] (Application Example 2)

[0900] 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".

[0901] In organizing and decluttering clothing, there is a challenge in enabling users to make sound decisions without experiencing emotional burden. Traditional systems do not take emotions into consideration, which can lead to users not feeling satisfied or content, and as a result, inefficient processing.

[0902] 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.

[0903] In this invention, the server includes means for receiving user input and storing information about clothing entered by the user in a data storage device; means for analyzing the information about clothing stored in the data storage device and evaluating the frequency of use and necessity of the clothing; means for acquiring the user's emotional state in real time using emotion analysis means; means for generating suggestions regarding the disposal of the clothing based on the evaluation results of frequency of use and necessity and the emotional state; and means for notifying the user of the suggestions, supporting the disposal procedure based on the user's selection, and providing feedback utilizing emotional data. This enables the user to organize and declutter their clothes in a way that is in line with their emotions.

[0904] "User input" refers to data provided by the user to the information processing device, and includes items related to clothing.

[0905] A "data storage device" is a device that stores information permanently or temporarily, and has the function of storing clothing information based on user input.

[0906] "Emotion analysis means" refers to technology that analyzes and acquires the user's emotional state in real time from their voice, input patterns, etc.

[0907] "Evaluation means" refers to analytical techniques for calculating the frequency and necessity of clothing use using information stored in a data storage device.

[0908] The "proposal generation means" is a process that, based on analysis and sentiment data, shows the user how to dispose of the target clothing.

[0909] A "notification method" is a technology equipped with communication functions to present generated suggestions to the user and prompt the user to make a choice.

[0910] A "disposal procedure support system" is a system configuration that provides necessary procedures and related information based on the disposal method selected by the user, and provides feedback that takes the user's feelings into consideration.

[0911] In the system for realizing this invention, the user first inputs information about their clothing using a terminal. This can be a smartphone or an interface device. The terminal has an emotion analysis engine built in, which acquires the user's emotional state in real time from their voice or text input.

[0912] The server stores and analyzes clothing information received via data storage. The analysis uses AI algorithms to evaluate the frequency of use and necessity of the entered clothing items. Sentiment analysis data is also incorporated, taking into account the user's feelings towards the items.

[0913] Next, the server generates an optimal disposal suggestion based on the analysis results and sentiment data. This suggestion is adjusted according to the user's emotional state, making it more likely to be accepted by the user. The suggestion also includes a fair price derived from market data.

[0914] This proposal is notified to the user via their device, where they can review and select the proposed options. Based on the user's selection, the system provides support regarding disposal procedures. For example, it may provide links to e-commerce platforms or resource recycling centers to support the user's decision. Furthermore, the system can use the collected sentiment data to deliver positive messages to the user.

[0915] For example, if a user asks, "Should I sell this jacket?", the system will consider the user's emotional state and suggest, "It seems to be comfortable for you and its value has increased significantly." An example of a prompt from the generative AI model would be, "Analyze the user's input tone and suggest a clothing management strategy considering their emotional state."

[0916] This system allows users to manage their clothing in a rational and personalized way while reducing emotional burden.

[0917] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0918] Step 1:

[0919] The terminal accepts user input. The user enters information about their clothing using a smart device. Here, they enter information such as the type of clothing, brand, and purchase date into input fields, and this data is collected by the terminal's user interface. The entered data is then prepared to be sent to the data storage device.

[0920] Step 2:

[0921] The device performs emotion analysis. It analyzes the user's emotional state in real time, using both the user's input and voice input / input patterns. The emotion analysis engine generates emotion data based on indicators such as voice tone and input speed, and sends this data as output to the next step.

[0922] Step 3:

[0923] The server stores the user's clothing information in a data storage device. It receives clothing information and emotional data transmitted from the terminal and saves it to a database. This makes the information easily accessible in subsequent processes.

[0924] Step 4:

[0925] The server analyzes the stored data. It retrieves clothing information from the database and uses an AI algorithm to evaluate usage frequency and necessity. Emotional data is also taken into account, and the analysis results output an evaluation score, which is then used to generate decision-making information.

[0926] Step 5:

[0927] The server generates suggestions. Based on analysis results and sentiment data, it generates optimal clothing disposal suggestions to present to the user. This process also references market data and incorporates attribute-based valuation. The generated suggestions are then sent to the next step.

[0928] Step 6:

[0929] The terminal notifies the user of the suggestion. It displays the suggested content received from the server to the user and provides multiple options through an interactive interface. An input interface for the user to make a selection is presented, and the terminal waits for the user's response.

[0930] Step 7:

[0931] The user makes a selection based on the suggestions. They operate a user interface on their device to review the suggestions presented and select the most suitable disposal method. The selected information is then sent to the server via the device.

[0932] Step 8:

[0933] The server provides support based on the user's choices. Based on the selected disposal method, the server generates and sends links to relevant e-commerce platforms and resource recycling centers to the user's device. Furthermore, it provides feedback messages utilizing sentiment data to improve user satisfaction.

[0934] 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.

[0935] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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."

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0955] The following is further disclosed regarding the embodiments described above.

[0956] (Claim 1)

[0957] A means for receiving user input and storing the information about clothing entered by the user in a database,

[0958] A means for analyzing information about clothing stored in a database and evaluating the frequency of use and necessity of that clothing,

[0959] A means for generating proposals regarding the disposal of the relevant clothing based on the evaluation results of frequency of use and necessity,

[0960] Means for notifying the user of the aforementioned proposal and supporting the disposal procedure based on the user's choice,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, characterized in that the analysis means calculates the average selling price of similar products by referring to market data and presents a suggested price to the user.

[0964] (Claim 3)

[0965] The system according to claim 1, characterized in that the support means provides the user with a link to a flea market application or a recycling center, depending on the user's choice.

[0966] "Example 1"

[0967] (Claim 1)

[0968] A means of collecting detailed information about individual items through the user's available devices and transmitting this information to the hub,

[0969] A calculation means configured to store received data in a data management device, perform automatic analysis based on that data, and evaluate the usage index of individual items,

[0970] A means for proposing the optimal processing method using an automated suggestion generation system based on the results of the aforementioned data analysis,

[0971] A means of notifying the proposed content and coordinating and supporting subsequent work based on user selection,

[0972] A system that includes this.

[0973] (Claim 2)

[0974] The system according to claim 1, characterized in that the calculation means analyzes market research data to determine the market value of similar items and shows a competitive price to the user.

[0975] (Claim 3)

[0976] The system according to claim 1, characterized in that the user support system provides access information to a trading platform or a recycling institution in response to an automated suggestion.

[0977] "Application Example 1"

[0978] (Claim 1)

[0979] A means for receiving user input and storing the information about clothing entered by the user in a database,

[0980] A means for analyzing information about clothing stored in a database and evaluating the frequency of use and necessity of that clothing,

[0981] A means for generating proposals regarding the disposal of the relevant clothing based on the evaluation results of frequency of use and necessity,

[0982] Means for notifying the user of the aforementioned proposal and supporting the disposal procedure based on the user's choice,

[0983] A means for automatically acquiring information about clothing using speech recognition and image recognition technology,

[0984] A system that includes this.

[0985] (Claim 2)

[0986] The system according to claim 1, characterized in that the analysis means calculates the average selling price of similar products by referring to market data and presents a suggested price to the user.

[0987] (Claim 3)

[0988] The system according to claim 1, characterized in that the support means provides the user with a link to an electronic marketplace application or a resource recycling center, depending on the user's choice.

[0989] "Example 2 of combining an emotion engine"

[0990] (Claim 1)

[0991] A means for storing information about items received from users in a central storage location,

[0992] A means for performing analysis to detect the user's emotional state along with information about the received item,

[0993] A means for generating suggestions that take emotional states into consideration based on the analysis results,

[0994] A means of notifying the user of the generated suggestions and providing relevant information based on their selection,

[0995] A system that includes this.

[0996] (Claim 2)

[0997] The system according to claim 1, characterized in that the analysis means performs a standard evaluation of the item by referring to various data and adjusts the proposed content based on that evaluation.

[0998] (Claim 3)

[0999] The system according to claim 1, characterized in that the information provision means provides an information link to an goods trading application or a recycling facility, depending on the user's selection.

[1000] "Application example 2 when combining with an emotional engine"

[1001] (Claim 1)

[1002] A means for receiving user input and storing the information about clothing entered by the user in a data storage device,

[1003] A means for analyzing information about clothing stored in a data storage device and evaluating the frequency of use and necessity of that clothing,

[1004] A means of acquiring the user's emotional state in real time using emotion analysis tools,

[1005] A means for generating suggestions regarding the disposal of the relevant clothing based on the evaluation results of frequency of use, necessity, and emotional state,

[1006] A means of notifying the user of the aforementioned proposal, supporting the disposal procedure based on the user's choice, and providing feedback utilizing emotional data,

[1007] A system that includes this.

[1008] (Claim 2)

[1009] The system according to claim 1, characterized in that the analysis means calculates the average selling price of similar items by referring to market data and presents a suggested price to the user.

[1010] (Claim 3)

[1011] The system according to claim 1, characterized in that the support means provides the user with a link to an e-commerce application or a resource recycling center, depending on the user's choice, and includes an emotionally sensitive message. [Explanation of Symbols]

[1012] 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

1. A means for receiving user input and storing the information about clothing entered by the user in a database, A means for analyzing information about clothing stored in a database and evaluating the frequency of use and necessity of that clothing, A means for generating proposals regarding the disposal of the relevant clothing based on the evaluation results of frequency of use and necessity, Means for notifying the user of the aforementioned proposal and supporting the disposal procedure based on the user's choice, A means for automatically acquiring information about clothing using speech recognition and image recognition technology, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means calculates the average selling price of similar products by referring to market data and presents a suggested price to the user.

3. The system according to claim 1, characterized in that the support means provides the user with a link to an electronic marketplace application or a resource recycling center, depending on the user's choice.

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