System

Abstract

A system is provided.SOLUTION: A system including a means for a user to input information such as a schedule, weather, temperature, and a preferred style, a means for acquiring past clothing history and data of clothes owned by the user from a database, a means for acquiring fashion information and trend information, a means for acquiring weather forecast information, a generation AI means for generating a clothing proposal based on the acquired information, a means for transmitting the generated proposal to a terminal of the user, and a means for recording a user's feedback and reflecting the feedback in a next proposal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] For working people living alone, choosing clothes every day can be a hassle, time-consuming, and labor-intensive. The following issues are particularly common:

[0005] 1. You have trouble choosing what to wear and always end up wearing the same outfit.

[0006] 2. It's difficult to keep track of what clothes you've worn in the past, and there's a risk of meeting the same person wearing the same clothes again.

[0007] 3. When trying out new clothes, you feel insecure and want someone to help you, but you don't have anyone to talk to.

[0008] 4. It's difficult to create the perfect outfit for yourself, taking into account the weather and the latest trends.

[0009] The objective of this project is to solve these problems, improve the efficiency of users' clothing selection, and enable them to enjoyably create unique outfits. [Means for solving the problem]

[0010] The present invention provides a system that solves the above problems by the following means:

[0011] 1. Provide a means for users to input information such as schedules, weather, temperature, and preferred style.

[0012] 2. The server provides a means to retrieve data on past clothing history and clothing owned from the database.

[0013] 3. Provide a means for the server to obtain fashion and trend information.

[0014] 4. Provide a means for the server to obtain weather forecast information.

[0015] 5. Provide a generation AI means to generate clothing suggestions based on the information acquired by the server.

[0016] 6. Provide a means for the server to send the generated proposals to the user's terminal.

[0017] 7. Provide a way to record user feedback and incorporate it into future suggestions.

[0018] 8. Based on the information acquired, it will have a function to prevent a person from meeting the same person wearing the same clothes again.

[0019] 9. It has the ability to learn from users' historical data and feedback to improve the accuracy of its suggestions.

[0020] This will significantly reduce the time and effort that users spend choosing their daily outfits, and will also provide a system that allows users to easily create the perfect outfit that matches the latest trends and the weather.

[0021] "Plan information" is information input by the user about plans and schedules for the current day or the near future.

[0022] "Weather information" refers to information about the weather forecast for the user's current location or a location they are visiting, including temperature, probability of precipitation, wind speed, and the like.

[0023] "Preferred style" is information that indicates the user's personal tastes and preferences regarding clothing and fashion.

[0024] "Past clothing history" refers to information that indicates a record of what clothes the user has worn in the past.

[0025] "Data on clothes owned" is comprehensive data on all clothes and accessories owned by the user.

[0026] "Fashion information" is a general term for the latest fashion news, advice, style guides, etc.

[0027] "Trend information" is information about fashion trends and movements that are currently attracting attention.

[0028] "Generative AI" refers to artificial intelligence technology that automatically generates optimal clothing suggestions based on input information.

[0029] "User's device" refers to an information communication device such as a smartphone or tablet used by the user.

[0030] "Feedback" refers to the evaluations and opinions provided by users regarding the proposed outfits and use of the system.

[0031] A "database" is an information system for storing and managing information about a user's history and the clothes they own. [Brief explanation of the drawings]

[0032] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0033] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0034] First, the terms used in the following description will be explained.

[0035] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0036] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0037] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0038] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0039] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0040] [First embodiment]

[0041] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0042] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0043] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0044] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0045] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0046] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0047] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0048] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0049] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0050] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0051] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0052] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0053] The present invention is a system for assisting a user in selecting clothing, and is implemented through the following steps. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc. Specific embodiments of the system are described in detail below.

[0054] ---

[0055] System Overview

[0056] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. The system integrates the user's input information, the database on the server, generation AI, trend information, and weather information to suggest optimal outfits.

[0057] ---

[0058] Specific Embodiments of the System

[0059] 1. User input

[0060] The user enters the following information using a device such as a smartphone or tablet.

[0061] Today's Schedule

[0062] Weather information

[0063] temperature

[0064] Your preferred style

[0065] Example: User types, "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[0066] ---

[0067] 2. Sending a request to the server

[0068] The device sends the information the user has entered, including schedule information, weather, temperature, and style preferences, to a server.

[0069] ---

[0070] 3. Obtaining user history

[0071] The server accesses the database to obtain the user's clothing history and the clothing they own, which allows them to check which clothes they have worn at other events and to obtain information to prevent duplication.

[0072] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[0073] ---

[0074] 4. Obtaining trend information

[0075] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and fashionable styles.

[0076] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[0077] ---

[0078] 5. Obtaining Weather Information

[0079] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[0080] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[0081] ---

[0082] 6. Generating clothing suggestions using generative AI

[0083] The server uses a generation AI to generate optimal clothing suggestions based on the information it has acquired (the user's past clothing data, trend information, weather information, and schedule).

[0084] For example, suggest a navy suit, white shirt, and navy tie.

[0085] ---

[0086] 7. Sending the proposal to the device

[0087] The server transmits the generated clothing suggestions to the user's terminal.

[0088] ---

[0089] 8. User notification of proposals

[0090] The terminal notifies the user of the proposal received from the server.

[0091] Example: Show the user "Today's suggestion: navy suit with a white shirt and a navy tie."

[0092] ---

[0093] 9. Getting User Feedback

[0094] The user checks the proposed outfits and inputs their satisfaction and feedback via the terminal, including their rating of the suggestions and requests for additions.

[0095] Example: A user gives feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[0096] ---

[0097] 10. Record and learn from feedback

[0098] The server records feedback from users and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user.

[0099] Example: Record that "The combination of navy suit + white shirt + navy tie was well received" and reflect this in future proposals.

[0100] ---

[0101] Specific examples

[0102] One day, a user enters that they have a client meeting scheduled, the weather is sunny, and the temperature is 20 degrees. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it for the next suggestion.

[0103] In this way, the system makes optimal suggestions based on user information and improves accuracy through feedback.

[0104] The processing flow will be explained below.

[0105] Step 1:

[0106] A user accesses a device such as a smartphone or tablet and launches an application. The user enters information such as today's schedule, weather, temperature, and preferred style. Example: A user enters, "Today I'm meeting with a client. The weather is sunny and the temperature is 20 degrees."

[0107] Step 2:

[0108] The terminal sends the information entered by the user to the server. Specifically, it sends the data "Today's plan = meet with client," "Weather = sunny," and "Temperature = 20 degrees" to the server.

[0109] Step 3:

[0110] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[0111] Step 4:

[0112] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[0113] Step 5:

[0114] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[0115] Step 6:

[0116] The server uses the AI ​​to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule). Example: The server suggests "navy suit, white shirt, and navy tie."

[0117] Step 7:

[0118] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[0119] Step 8:

[0120] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[0121] Step 9:

[0122] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[0123] Step 10:

[0124] The server records user feedback and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user. Specifically, it records that "the combination of navy suit + white shirt + navy tie was well received" and reflects this in future suggestions.

[0125] Example 1

[0126] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] Conventional clothing selection support systems require users to take the time and effort to choose their outfits each time, and it is difficult to suggest outfits that take into account past clothing history and the latest trends. In addition, they lacked the functionality to prevent users from wearing the same outfit when meeting a specific person again, and the functionality to utilize user feedback to improve the accuracy of suggestions. Furthermore, it was difficult to incorporate changing weather information in real time.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0129] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving data on past clothing history and clothing owned from a database; a means for retrieving external fashion and trend information; a means for retrieving weather forecast information; a means including a generative AI model that generates clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; and a means for recording user feedback and reflecting it in next suggestions. This allows the user to efficiently select optimal clothing and receive suggestions that take past history, the latest trends, and weather information into consideration. Furthermore, the server can avoid wearing the same clothes when meeting a specific person again, and can improve the accuracy of suggestions by utilizing feedback.

[0130] "User" refers to an individual who uses the system to receive clothing suggestions.

[0131] "Terminal" means a device through which a User inputs information and receives suggestions, including a smartphone, tablet, or PC.

[0132] "Server" refers to the central computer system that receives information from users, accesses various databases and APIs to generate outfit suggestions, and sends them to the device.

[0133] "Information input means" refers to an interface that allows a user to input information such as schedules, weather, temperature, and preferred style into the terminal.

[0134] "Clothing history acquisition means" refers to a function for acquiring the user's past clothing history and data on clothing owned by the user from a database.

[0135] "Means for obtaining fashion information" refers to the function for obtaining information from external fashion sites and trends via API.

[0136] "Means for obtaining weather information" refers to the function for obtaining the latest weather information using the weather forecast API.

[0137] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal clothing suggestions based on acquired information.

[0138] "Terminal transmission means" refers to a function that transmits the generated clothing suggestions to the user's terminal.

[0139] "Feedback recording means" refers to a function for recording feedback from users and reflecting that information in the next proposal.

[0140] The present invention is a system for assisting a user in selecting clothes, and has the following configuration.

[0141] System Overview

[0142] This system consists of a series of processes in which the user inputs information through a terminal, and the server then suggests appropriate clothing based on that information. Specific hardware used as the terminal is a smartphone or tablet for inputting user information. The server uses a virtual server on the cloud or a dedicated data center.

[0143] Hardware and software used

[0144] Devices: Smartphones, tablets, PCs

[0145] Server: Cloud server, database server

[0146] Software: API for HTTP requests, generative AI models (Python and machine learning frameworks such as TensorFlow)

[0147] Specific form of implementation

[0148] User input

[0149] A user uses a smartphone or tablet application to input information such as schedules, weather information, temperature, preferred style, etc. For example, a user inputs information such as "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[0150] Sending a request to the server

[0151] The device converts the user's input information into JSON format and sends it to the server via an HTTP request. Specifically, it sends a POST request to the API endpoint.

[0152] Retrieving user history

[0153] After receiving the request, the server accesses the database to retrieve data on the user's clothing history and the clothes they own. Specifically, it retrieves the data using SQL queries.

[0154] Obtaining trend information

[0155] The server obtains the latest information from external fashion sites and trend information services, using a RESTful API to understand the latest fashion trends and fashion trends.

[0156] Get weather information

[0157] The server retrieves the latest weather information from the weather forecast service, specifically, the current temperature and weather using the weather API.

[0158] Generative AI generates clothing suggestions

[0159] The server then sends prompts to the generative AI model based on the information it has acquired to generate optimal outfit suggestions, for example using prompts like the following:

[0160] To help users choose what to wear, please suggest the best outfit based on the following information:

[0161] Upcoming: Client Meeting

[0162] Weather: Sunny

[0163] Temperature: 20°C

[0164] User history: Navy suit, gray suit

[0165] Clothes I own: 4 suits, 8 shirts, 10 ties

[0166] Latest trends: business casual, monochrome

[0167] Once the generative AI model generates a proposal, the server receives it.

[0168] Sending the proposal to the device

[0169] The server sends the generated outfit suggestions to the user's device as an HTTP response. Specifically, the suggestion content is included in the POST request response in JSON format.

[0170] User notification of proposals

[0171] The terminal displays the suggestions received from the server on the application screen, for example, notifying the user of "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[0172] Get user feedback

[0173] The user inputs their satisfaction level and feedback about the suggested outfits. Specifically, they enter feedback such as "I'm satisfied with the suggestion" or "I would like to receive similar suggestions in the future" into the application form and click the submit button.

[0174] Record and learn from feedback

[0175] The server records the user feedback in a database and uses it as training data for the AI ​​model in future. The server saves the feedback in the database using an SQL query.

[0176] As a result, the present invention can provide efficient and personalized clothing suggestions, thereby increasing user satisfaction.

[0177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0178] System program processing flow

[0179] Step 1: Enter your information

[0180] Users enter the following information using an application on their smartphone or tablet:

[0181] Input: Schedule, weather information, temperature, preferred style

[0182] Specific actions: Enter the information "Client meeting. Weather is sunny, temperature is 20 degrees" into the application's input form and press the send button.

[0183] Step 2: Submitting a request

[0184] The terminal converts the information entered by the user into JSON format and sends it to the server via an HTTP request.

[0185] Input: User input information

[0186] Output: JSON format data to send to the server

[0187] Specific behavior: Serializes the input information into JSON and sends a POST request to the API endpoint.

[0188] Step 3: Retrieving User History

[0189] After receiving the request, the server accesses the database to retrieve data on the user's past clothing history and the clothes they own.

[0190] Input: User ID

[0191] Output: Past clothing history and clothing owned data

[0192] Specific operation: Execute the SQL query "SELECT FROM user_clothing_history WHERE user_id = 'User ID'" and retrieve the results.

[0193] Step 4: Obtaining trend information

[0194] The server obtains the latest information from external fashion sites and trend information services.

[0195] Input: Fashion Information API endpoint

[0196] Output: Trend information

[0197] Specific behavior: Uses the RESTful API to send a "GET / fashion-trends" request and receives trend information in JSON format.

[0198] Step 5: Get Weather Information

[0199] The server retrieves the latest weather information from a weather forecast service.

[0200] Input: Weather API endpoint and specified location

[0201] Output: Weather information (current temperature and weather)

[0202] Specific operation: Sends a request "GET / weather?location=specified location" and receives weather information in JSON format.

[0203] Step 6: Generative AI generates outfit suggestions

[0204] The server sends prompts to the generative AI model based on the acquired information to generate optimal outfit suggestions.

[0205] Input: User's past clothing data, latest trend information, weather information, schedule information

[0206] Output: Generated outfit suggestions

[0207] How it works: Using a Python script, the generative AI model is fed the following prompt: "Client meeting scheduled. Weather: sunny, temperature: 20 degrees. Past outfits: navy suit, gray suit. Owned: four suits, eight shirts, ten ties. Current trends: business casual, monochrome," and the resulting suggestions are then analyzed.

[0208] Step 7: Send the proposal to the device

[0209] The server sends the generated clothing suggestions to the user's terminal as an HTTP response.

[0210] Input: Generated outfit suggestions

[0211] Output: Response to the user's device

[0212] Specific operation: The proposal content is converted into JSON format and returned in the HTTP response.

[0213] Step 8: Notify users of the proposal

[0214] The terminal displays the proposal received from the server on the application screen.

[0215] Input: Response from the server (generated outfit suggestions)

[0216] Output: Display on the application screen

[0217] Specific operation: Analyze the received suggestion and display "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[0218] Step 9: Get user feedback

[0219] The user inputs their satisfaction and feedback about the suggested outfit.

[0220] Input: Satisfaction and comments about the proposal

[0221] Output: Feedback sent from the device to the server

[0222] Specific actions: Enter "I'm satisfied with the proposal" and "I would like to receive similar proposals in the future" in the feedback form and click the submit button.

[0223] Step 10: Record and learn from feedback

[0224] The server records user feedback in a database and uses it as learning data for the AI ​​model in future runs.

[0225] Input: User feedback

[0226] Output: Save to database and training data for AI model

[0227] Specific behavior: Execute the SQL query "INSERT INTO feedback (user_id, feedback_text) VALUES ('user_id', 'Satisfied with the suggestion')" to save the feedback.

[0228] The above is the specific processing flow of the system program.

[0229] (Application example 1)

[0230] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0231] In conventional clothing suggestion systems, even if a user inputs information such as their schedule, weather, temperature, and preferred style, the suggested outfits may not necessarily suit the user's preferences or the situation of the day. Furthermore, suggesting outfits in physical stores places a heavy burden on store staff, making it difficult to make efficient suggestions. Furthermore, the system lacks the functionality to prevent users from wearing the same outfit when meeting a specific person from the past, and the feedback functionality to improve the accuracy of suggestions.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0233] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving past clothing history and clothing data from a database; a means for retrieving fashion and trend information; a means for retrieving weather forecast information; a generation AI means for generating clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; a means for displaying the suggestions using a smart mirror; and a means for recording user feedback and reflecting it in future suggestions. This allows users to receive optimal clothing suggestions through a smart mirror installed in a physical store, thereby reducing the burden on store staff and enabling efficient clothing suggestions. Furthermore, by utilizing past history and feedback, the accuracy of suggestions can be improved, increasing user satisfaction.

[0234] "Means for users to input information such as schedules, weather, temperature, and preferred styles" refers to an interface that allows users to input information about the day's schedule, current weather, temperature, and preferred fashion style.

[0235] "Means for retrieving data on past clothing history and owned clothing from a database" is a function for retrieving data on the clothing history that a user has worn and the clothing in their closet from a database.

[0236] "Means for obtaining fashion information and trend information" refers to a function for obtaining the latest fashion information and trend information from sources on the Internet.

[0237] "Means for obtaining weather forecast information" refers to a function for obtaining current and future weather information from a weather forecast API, etc.

[0238] The "generative AI means for generating clothing suggestions based on acquired information" is an artificial intelligence model that comprehensively analyzes information entered by the user, past clothing history, trend information, weather information, etc., to generate optimal clothing suggestions.

[0239] "Means for sending generated suggestions to the user's device" refers to a function for notifying the user of clothing suggestions created by the generation AI on their device, such as a smartphone or tablet.

[0240] The "means for displaying suggestions using a smart mirror" is a function for displaying the generated clothing suggestions on a smart mirror installed in a physical store.

[0241] "Means for recording user feedback and reflecting it in the next proposal" is a function for collecting the evaluations and opinions given by users on the proposals and using them in the next clothing proposal.

[0242] This invention is a system that allows users to choose clothes more efficiently and effectively, and is implemented based on the following procedures and configuration: In this system, a server generates optimal outfit suggestions based on user input information and displays them to the user through a smart mirror.

[0243] System configuration

[0244] The system consists of a terminal where users input information, a server that processes the data, and a smart mirror that displays the output, allowing the system to suggest the most suitable outfit for the user.

[0245] Hardware and software used

[0246] Hardware

[0247] Smart Mirror

[0248] server

[0249] software

[0250] Weather Forecast API

[0251] Trend information acquisition API

[0252] Generative AI models (e.g., OpenAI GPT, fashion-specific generative models)

[0253] Implementation procedures and processing details

[0254] Users can stand in front of a smart mirror installed in a store and use voice or touch input to input information about their plans for the day, the weather, the temperature, and their preferred style. This information is collected by the smart mirror and sent to the server.

[0255] The server processes the data using the following means:

[0256] 1. User information input processing: Information such as schedule, weather, temperature, and style is acquired from the smart mirror. This information is sent to the server and analyzed.

[0257] Example: Say "I have a business meeting today" and input that the weather is sunny and the temperature is 20 degrees.

[0258] Example prompt: "What is the best outfit to wear for a business meeting on a sunny 20 degree day?"

[0259] 2. Database reference process: The server accesses the database to retrieve the user's clothing history and the clothing they own. This allows the server to make optimal recommendations while preventing duplication of past clothing.

[0260] 3. Trend information acquisition: The server acquires information from fashion sites and trends on the Internet through the API. This is an important element in suggesting the latest trends and stylish styles to users.

[0261] 4. Weather information acquisition process: The latest weather information is acquired using a weather forecast API. This information, along with the user's schedule for the day and preferred style, is input into the generation AI.

[0262] 5. Proposal generation process using generative AI: Based on the acquired data, a generative AI model generates optimal clothing suggestions. For example, OpenAI GPT or a fashion-specific generative model can be used to suggest specific outfits that the user should wear.

[0263] 6. Display of suggested outfits: The generated outfit suggestions are displayed on the smart mirror, allowing the user to see the specific outfit image.

[0264] 7. Feedback acquisition process: The user inputs their satisfaction with the clothing suggestions and any additional requests they may have. This feedback is sent to the server and will be used for future suggestions. For example, possible feedback is "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[0265] 8. Learning process: The server records user feedback as learning data to improve the accuracy of the generative AI model, allowing it to make suggestions that are more tailored to the user's preferences and tendencies.

[0266] Specific examples

[0267] When a user stands in front of a smart mirror and inputs that they have a business meeting and that the weather is sunny and 20 degrees, the server prompts the generation AI based on past clothing history, trend information, and weather information. The generation AI generates a suggestion of "navy suit, white shirt, navy tie" and displays it on the smart mirror. If the user is satisfied with this suggestion, their feedback will be reflected in the next suggestion. In this way, the system can make clothing suggestions that suit the user and continuously improve its accuracy through feedback.

[0268] This system provides support to users to effectively select clothing, improving the efficiency and accuracy of clothing suggestions in physical stores.

[0269] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0270] Step 1:

[0271] The user stands in front of the smart mirror and inputs information such as their schedule for the day, the weather, the temperature, and their preferred style. This information is input using voice or the touch panel. This information is entered into the smart mirror and sent to the terminal as a single data package.

[0272] Input: Schedule, weather, temperature, preferred style

[0273] Output: User input data package

[0274] Step 2:

[0275] The terminal transmits the information entered by the user to the server, which receives the information and prepares it for analysis.

[0276] Input: User input data package

[0277] Output: Data sent to the server

[0278] Step 3:

[0279] The server accesses the database to retrieve the user's clothing history and the clothing they own, allowing them to check which clothing items they have used in the past and which they currently have on hand.

[0280] Input: User ID

[0281] Output: User's past clothing history data and clothing data

[0282] Step 4:

[0283] The server obtains the latest fashion and trend information through an external API, and uses the information as reference data to suggest optimal clothing.

[0284] Input: Fashion information API request

[0285] Output: Latest fashion and trend information

[0286] Step 5:

[0287] The server uses a weather forecast API to obtain current weather and temperature information, which is necessary to suggest comfortable clothing for the user.

[0288] Input: Weather API request

[0289] Output: Weather forecast information

[0290] Step 6:

[0291] The server uses a generative AI model to generate outfit suggestions based on the user's past clothing history data, clothing data, the latest fashion and trend information, weather forecast information, and user input data. For example, a prompt such as "What is the best outfit to wear for a business meeting on a sunny 20-degree day?" is input to the generative AI model.

[0292] Input: User's past clothing history data, clothing data, fashion information, trend information, weather forecast information, user input data, prompt text

[0293] Output: Generated outfit suggestions

[0294] Step 7:

[0295] The generated outfit suggestions are sent from the server to the smart mirror, which displays the suggested outfits on its screen for the user to visually confirm.

[0296] Input: Generated outfit suggestions

[0297] Output: Outfit suggestions displayed on a smart mirror

[0298] Step 8:

[0299] Users can input their satisfaction and feedback about the suggested outfits through the smart mirror, which will then be sent to the server and reflected in the next outfit suggestions.

[0300] Input: User feedback

[0301] Output: Feedback data sent to the server

[0302] Step 9:

[0303] The server records the collected feedback in a database and uses it as training data for the generative AI model, thereby improving the accuracy of future suggestions.

[0304] Input: User feedback data

[0305] Output: An updated generative AI model

[0306] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0307] This invention is a system for assisting users in choosing clothing, and in particular, it recognizes the user's emotions and suggests appropriate clothing based on those emotions. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc., and also combining it with an emotion engine that recognizes the user's emotions.

[0308] ---

[0309] System Overview

[0310] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. This system integrates the user's input information, the database on the server, generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits.

[0311] ---

[0312] Specific Embodiments of the System

[0313] 1. User input

[0314] The user enters the following information using a device such as a smartphone or tablet.

[0315] Today's Schedule

[0316] Weather information

[0317] temperature

[0318] Your preferred style

[0319] The user's current sentiment

[0320] Example: User types, "I have a client meeting today. The weather is sunny, the temperature is 20 degrees, and I'm feeling a bit nervous today."

[0321] ---

[0322] 2. Sending a request to the server

[0323] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[0324] ---

[0325] 3. Obtaining user history

[0326] The server accesses the database to obtain the user's clothing history and the clothing they own, thereby obtaining information to prevent the use of duplicate clothing.

[0327] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[0328] ---

[0329] 4. Obtaining trend information

[0330] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and style guidelines.

[0331] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[0332] ---

[0333] 5. Obtaining Weather Information

[0334] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[0335] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[0336] ---

[0337] 6. Emotion Recognition by Emotion Engine

[0338] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[0339] ---

[0340] 7. Generating clothing suggestions using generative AI

[0341] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[0342] ---

[0343] 8. Sending the proposal to the device

[0344] The server transmits the generated clothing suggestions to the user's terminal, specifically, the generated suggestions are transmitted to the terminal via a network.

[0345] ---

[0346] 9. User notification of proposals

[0347] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[0348] Example: The user confirms the displayed suggestions.

[0349] ---

[0350] 10. Getting User Feedback

[0351] The user checks the suggested outfits and inputs their satisfaction and feedback via the terminal. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[0352] ---

[0353] 11. Recording and learning feedback and emotion data

[0354] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, the server records data such as "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[0355] ---

[0356] Specific examples

[0357] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it to improve the next suggestion.

[0358] In this way, the system makes optimal suggestions based on the user's information and emotions, and improves accuracy through feedback.

[0359] The processing flow will be explained below.

[0360] Step 1:

[0361] A user accesses a device such as a smartphone or tablet and launches the application. The user inputs their plans for the day, the weather, the temperature, their preferred style, and their current emotions. Example: A user inputs, "Today I'm meeting with a client. The weather is sunny, the temperature is 20 degrees, and I'm feeling a little nervous today."

[0362] Step 2:

[0363] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[0364] Step 3:

[0365] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[0366] Step 4:

[0367] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[0368] Step 5:

[0369] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[0370] Step 6:

[0371] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[0372] Step 7:

[0373] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[0374] Step 8:

[0375] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[0376] Step 9:

[0377] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user. The user confirms the displayed proposal.

[0378] Step 10:

[0379] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[0380] Step 11:

[0381] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, it records that "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[0382] Example 2

[0383] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0384] Conventional clothing suggestion systems generate suggestions based on the user's schedule, weather, temperature, and preferred style, but do not take the user's emotions into account and are unable to generate different suggestions based on individual emotional changes.In addition, the system lacks a mechanism for incorporating user feedback into the generated suggestions, limiting the improvement of suggestion accuracy.

[0385] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0386] In this invention, the server includes a means for the user to input information such as schedule, weather, temperature, preferred style, and current emotion; a means for retrieving data on past clothing history and clothing owned from a database; and a means for analyzing the user's emotion data using an emotion engine. This allows for more appropriate clothing suggestions that take the user's emotions into consideration. Furthermore, by recording user feedback on generated suggestions and reflecting this in the next suggestions, the accuracy of the suggestions can be improved.

[0387] "Schedule" refers to the schedule or plan that the user will carry out that day.

[0388] "Weather" refers to the current weather and meteorological conditions.

[0389] "Temperature" refers to the temperature and temperature information for that day.

[0390] "Preferred style" refers to the clothing or fashion style that the user prefers.

[0391] "Emotions" refers to the user's current state of mind and emotional ups and downs.

[0392] "Past clothing history" refers to a record of clothing previously worn by the user.

[0393] "Data on clothes owned" refers to information about clothes and accessories owned by the user.

[0394] "Fashion information" refers to information about the latest fashions and styles.

[0395] "Trend information" refers to currently popular fashions and style guidelines.

[0396] "Weather forecast information" refers to information predicting future weather and temperature.

[0397] An "emotion engine" refers to a system that analyzes the emotion data entered by the user and recognizes their emotional state.

[0398] "Generative AI" refers to an artificial intelligence model that generates optimal clothing suggestions based on multiple pieces of information.

[0399] "User's terminal" refers to a computer device such as a smartphone or tablet used by a user.

[0400] "Feedback" refers to ratings and comments provided by users on suggested outfits.

[0401] This invention is a system for assisting users in choosing clothing, suggesting appropriate clothing based on information such as the user's schedule, weather, temperature, preferred style, current emotion, etc. In particular, this system is characterized by using an emotion engine to recognize the user's emotions and a generative AI model to suggest optimal clothing.

[0402] System configuration

[0403] The system includes a terminal where users can input information, a server that receives the information, and multiple engines and APIs that process and analyze the information.The database also records the user's past clothing history, clothing data, and user feedback.

[0404] Hardware and software used

[0405] 1. Terminal: A device that a user uses to input information, such as a smartphone or tablet.

[0406] 2. Server: Receives requests and processes information by interacting with databases and APIs.

[0407] 3. Database: A storage device for storing user history data and feedback.

[0408] 4. Emotion engine: Software for analyzing user emotion data.

[0409] 5. Generative AI model: Artificial intelligence that generates clothing suggestions based on user information.

[0410] 6. API: External services for obtaining trending and weather information.

[0411] Data processing and calculation

[0412] 1. Enter your information:

[0413] Users use a smartphone or tablet to input their "today's schedule," "weather," "temperature," "preferred style," and "current feelings" into the system.

[0414] 2. Submit your request:

[0415] The entered information is sent from the terminal to the server in JSON format or similar.

[0416] 3. Retrieving from the database:

[0417] The server retrieves data on past clothing history and clothing owned from the database, which helps to avoid duplication of previously used clothing.

[0418] 4. Get trend and weather information:

[0419] The server obtains the latest fashion information and weather forecasts through external APIs. Trend information includes currently popular fashion styles, and weather information includes current weather conditions and forecasts.

[0420] 5. Emotion engine analysis:

[0421] The server uses an emotion engine to analyze the emotion data entered by the user and determine the user's emotional state.

[0422] 6. Generating outfit suggestions:

[0423] The server uses a generative AI model based on all the information it has acquired so far (user's schedule, weather, temperature, preferred style, emotions, past clothing history, data on clothing owned, and trend information) to generate optimal clothing suggestions.

[0424] 7. Submission and Notification of Proposals:

[0425] The generated clothing suggestions are sent from the server to the user's terminal via the network, and the terminal notifies the user of the suggestions.

[0426] Specific examples

[0427] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user reviews this suggestion and provides feedback that they are satisfied. This feedback is recorded on the server and reflected in future suggestions.

[0428] Prompt Sentence Examples

[0429] "What is the best business casual attire for when you have a client meeting, it's sunny, it's 20 degrees, and you're a little nervous?"

[0430] In this way, the system makes optimal clothing suggestions based on the user's information and emotions, and incorporates feedback to improve the accuracy of the suggestions.

[0431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0432] Step 1:

[0433] Users input information using devices such as smartphones and tablets. Specifically, they input information such as "today's schedule," "weather," "temperature," "preferred style," and "current emotion" into the application's input fields. This generates user input data.

[0434] Input: Today's schedule, weather, temperature, preferred style, current mood

[0435] Output: Input data (e.g., {Appointment: "Meet with a client", Weather: "Sunny", Temperature: 20, Style: "Business", Emotion: "Nervous"})

[0436] Step 2:

[0437] The terminal converts the information entered by the user into JSON format or similar and sends it to the server. Specifically, it sends the data to the server via an HTTP request, which allows the server to receive the data.

[0438] Input: Input data

[0439] Output: Request sent to server

[0440] Step 3:

[0441] The server analyzes the received data and accesses the database to retrieve the user's past clothing history and the clothes they own. It uses an SQL query to search the data and retrieves the results. This passes the user's past clothing history and a list of the clothes they currently own to the server.

[0442] Input: Received data

[0443] Output: User's past clothing history, clothing data

[0444] Step 4:

[0445] The server sends a request to an external API to get the latest fashion and trend information. For example, it sends a GET request to a fashion API to get trend information. This data is then processed on the server.

[0446] Input: API request

[0447] Output: Latest fashion and trend information

[0448] Step 5:

[0449] The server accesses the weather forecast API to obtain the latest weather information for the specified area. For example, it sends a GET request to the weather API and obtains weather and temperature information from the response. This data is stored on the server.

[0450] Input: API request

[0451] Output: Weather information, temperature information

[0452] Step 6:

[0453] The server uses an emotion engine to analyze the user's emotion data. Specifically, it analyzes the user's emotion input, such as "tension," to recognize the user's detailed emotional state. This information is used for further data processing.

[0454] Input: Emotion data

[0455] Output: Parsed emotional state

[0456] Step 7:

[0457] The server uses a generative AI model based on all the information it has acquired (user schedule, weather, temperature, preferred style, emotions, past clothing history, clothing data owned, and trend information) to generate optimal outfit suggestions. Text data combining this information is used as input prompts for the generative AI model.

[0458] Input: All acquired information

[0459] Output: Generated outfit suggestions

[0460] Step 8:

[0461] The server converts the generated clothing suggestions into JSON format and sends them to the user's device. The server then sends the suggestion data as an HTTP response over the network, allowing the user's device to receive the suggestions.

[0462] Input: Generated outfit suggestions

[0463] Output: Send the proposal to the device

[0464] Step 9:

[0465] The device notifies the user of the suggestions received from the server. Specifically, the suggestions are displayed as a pop-up or notification message within the application, allowing the user to confirm the suggestions.

[0466] Input: Receive proposal

[0467] Output: User notification of the proposal

[0468] Step 10:

[0469] The user reviews the proposed outfits and enters their satisfaction and feedback within the application, which generates evaluation comments.

[0470] Input: Confirm proposal

[0471] Output: Feedback data

[0472] Step 11:

[0473] The server records the feedback and emotion data provided by the user in a database and uses this data as training data to improve the accuracy of future suggestions.

[0474] Input: Feedback data, emotion data

[0475] Output: Update learning data, improve proposal accuracy

[0476] (Application example 2)

[0477] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0478] Conventional clothing suggestion systems do not take into account the user's emotions when making suggestions, making it difficult to suggest the best outfit for the user's mood and situation. Furthermore, they lacked connectivity with physical stores, making it impossible to purchase suggested outfits on the spot. Furthermore, they lacked the application of user feedback to improve the accuracy of suggestions.

[0479] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0480] In this invention, the server includes means for the user to input information such as schedule, weather, temperature, and preferred style, means for retrieving data on past clothing history and clothing owned from a database, means for retrieving fashion information and trend information, means for retrieving weather forecast information, means for recognizing the user's emotions in real time, generation AI means for generating outfit suggestions based on the retrieved information, means for displaying the suggested outfits as items available for purchase in a physical store, means for transmitting the generated suggestions to the user's terminal, and means for recording user feedback and reflecting it in the next suggestion. This makes it possible to take the user's emotions into consideration, facilitate purchases in a physical store, and improve the accuracy of suggestions based on feedback.

[0481] "Schedule" is information indicating the plans and activities that the user will undertake on that day.

[0482] "Weather" is information that indicates the weather conditions and meteorological conditions for that day.

[0483] "Temperature" is information indicating the temperature of the atmosphere at a specific time.

[0484] "Preferred style" is information that indicates the user's preferred fashion and clothing trends.

[0485] "Past clothing history" is a record of clothing worn by the user in the past.

[0486] "Data on clothes owned" is information on the types and quantities of clothes owned by the user.

[0487] A "database" is a collection of data and a system for efficiently managing information.

[0488] "Fashion information" is information about the latest clothing and accessories.

[0489] "Trend information" is information about currently popular fashions and styles.

[0490] "Weather forecast information" is information that indicates a prediction of future weather.

[0491] "Means for recognizing a user's emotions in real time" refers to technology for instantly analyzing a user's current emotions.

[0492] "Generative AI means" refers to artificial intelligence technology for generating clothing suggestions based on acquired information.

[0493] "In-store purchaseable items" are products that can be purchased immediately in a physical store.

[0494] The "means for transmitting the generated suggestions to the user's terminal" is a technique for sending the outfit suggestions to the device used by the user.

[0495] "User feedback" is information indicating evaluations and opinions from users.

[0496] "Means to reflect in the next proposal" refers to techniques for incorporating user feedback into the next proposal.

[0497] System Configuration

[0498] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and a server then suggests appropriate outfits based on that information. The system integrates the user's input information, a database on the server, a generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits. It also has a function to support purchasing in physical stores.

[0499] Hardware and Software

[0500] Hardware:

[0501] Smartphone / Tablet: A device for entering user information and using the emotion recognition camera.

[0502] Server: A system for processing data and hosting APIs.

[0503] software:

[0504] OpenCV: A library for face detection and image processing.

[0505] Keras: A deep learning framework used for loading and inferencing emotion recognition models.

[0506] Requests: A library for making requests to external APIs (weather API, fashion API).

[0507] System action

[0508] The server performs the following data processing and calculations: The user uses their device to input information such as their schedule, weather, temperature, and preferred style, and sends that information to the server. The server retrieves the user's past clothing history and clothing data from a database, and also obtains external fashion information, trend information, and weather forecast information via an API. Next, it analyzes the user's current emotions using an emotion engine that recognizes the user's emotions in real time. A generation AI generates outfit suggestions based on the acquired information and sends these suggestions to the user's device. It also displays items that can be purchased in physical stores based on the suggested outfits to assist with purchasing. User feedback is collected and reflected in future suggestions, improving the accuracy of the suggestions.

[0509] Specific examples

[0510] One day, a user enters a physical store and types "I'm in a fun party mood today" into their device. The system uses an emotion engine to analyze the user's emotions and retrieves past clothing history, clothing owned, weather information, and fashion information from the database. Based on this information, the generation AI suggests the most suitable outfit, and the suggestion is sent to the user's device. The suggested outfit is then displayed as an item available for purchase in the physical store. The user purchases the outfit based on the suggestion and provides feedback on their satisfaction. The server records this feedback and reflects it in the next suggestion.

[0511] A specific example of a prompt is as follows:

[0512] "Today's suggestion: I'm going to a party and I'd like to know what items I can buy at a nearby store. I'm in a fun mood."

[0513] The invention is a system that helps users easily choose the best outfit for their mood and situation, and also helps them make purchases in physical stores smoothly. This system is expected to increase user satisfaction and improve the shopping experience in stores.

[0514] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0515] Step 1:

[0516] The user uses the device to input information such as schedule, weather, temperature, preferred style, and emotions.

[0517] Input: User's schedule, weather, temperature, preferred style, emotions

[0518] Output: User information entered into the terminal

[0519] Specifically, the user inputs "I'm going to a party today and I'm in a good mood," which is then saved on the device.

[0520] Step 2:

[0521] The terminal transmits the input information to the server.

[0522] Input: User information stored on the device

[0523] Output: User information sent to the server

[0524] Specifically, the terminal transmits information such as "I'm going to a party today and I'm in a good mood" to the server via the network.

[0525] Step 3:

[0526] The server retrieves the user's past clothing history and data on the clothes they own from the database.

[0527] Input: User ID

[0528] Output: User's past clothing history and clothing data

[0529] Specifically, the server accesses the database and retrieves data such as "navy suit, white shirt, navy tie."

[0530] Step 4:

[0531] The server obtains fashion and trend information from the Internet via an API.

[0532] Input: API request

[0533] Output: Latest fashion and trend information

[0534] Specifically, the server sends a request to the fashion API and obtains information such as "business casual and monochrome are in fashion."

[0535] Step 5:

[0536] The server obtains weather information using the weather forecast API.

[0537] Input: Weather API request

[0538] Output: Weather information (sunny, temperature 20 degrees)

[0539] Specifically, the server accesses the weather forecast API and obtains information such as "The weather is sunny, with a maximum temperature of 20 degrees."

[0540] Step 6:

[0541] The server uses an emotion engine to recognize emotion information input by the user.

[0542] Input: User emotion data

[0543] Output: Perceived emotion (happy)

[0544] Specifically, the server inputs "feeling happy" into the emotion engine and analyzes it.

[0545] Step 7:

[0546] Based on the information acquired by the server, clothing suggestions are generated using a generation AI.

[0547] Input: Past clothing history, fashion information, weather information, schedule, emotions

[0548] Output: Generated outfit suggestions

[0549] Specifically, the generative AI generates suggestions such as "navy suit, white shirt, navy tie."

[0550] Step 8:

[0551] The server transmits the generated clothing suggestions to the terminal.

[0552] Input: Generated outfit suggestions

[0553] Output: Outfit suggestions sent to the device

[0554] Specifically, the server sends the suggestion to the terminal via the network, and "Today's suggestion: navy suit, white shirt, navy tie" is displayed.

[0555] Step 9:

[0556] The suggested outfits are displayed as items available for purchase in physical stores.

[0557] Input: Generated outfit suggestions

[0558] Output: A list of items available for purchase

[0559] Specifically, the terminal displays a list of products available for purchase at store A.

[0560] Step 10:

[0561] The user reviews the suggested outfits and enters feedback.

[0562] Input: User feedback

[0563] Output: Feedback data

[0564] Specifically, the user inputs feedback such as "I'm satisfied with this suggestion" and saves it on the device.

[0565] Step 11:

[0566] The server records the user's feedback and reflects it in future suggestions.

[0567] Input: Feedback data

[0568] Output: Updated user database

[0569] Specifically, the server records data such as "the combination of navy suit + white shirt + navy tie is popular" and uses it for the next proposal.

[0570] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0571] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0572] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0573] [Second embodiment]

[0574] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0575] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0576] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0577] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0578] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0579] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0580] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0581] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0582] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0583] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0584] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0585] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0586] The present invention is a system for assisting a user in selecting clothing, and is implemented through the following steps. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc. Specific embodiments of the system are described in detail below.

[0587] ---

[0588] System Overview

[0589] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. The system integrates the user's input information, the database on the server, generation AI, trend information, and weather information to suggest optimal outfits.

[0590] ---

[0591] Specific Embodiments of the System

[0592] 1. User input

[0593] The user enters the following information using a device such as a smartphone or tablet.

[0594] Today's Schedule

[0595] Weather information

[0596] temperature

[0597] Your preferred style

[0598] Example: User types, "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[0599] ---

[0600] 2. Sending a request to the server

[0601] The device sends the information the user has entered, including schedule information, weather, temperature, and style preferences, to a server.

[0602] ---

[0603] 3. Obtaining user history

[0604] The server accesses the database to obtain the user's clothing history and the clothing they own, which allows them to check which clothes they have worn at other events and to obtain information to prevent duplication.

[0605] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[0606] ---

[0607] 4. Obtaining trend information

[0608] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and fashionable styles.

[0609] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[0610] ---

[0611] 5. Obtaining Weather Information

[0612] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[0613] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[0614] ---

[0615] 6. Generating clothing suggestions using generative AI

[0616] The server uses a generation AI to generate optimal clothing suggestions based on the information it has acquired (the user's past clothing data, trend information, weather information, and schedule).

[0617] For example, suggest a navy suit, white shirt, and navy tie.

[0618] ---

[0619] 7. Sending the proposal to the device

[0620] The server transmits the generated clothing suggestions to the user's terminal.

[0621] ---

[0622] 8. User notification of proposals

[0623] The terminal notifies the user of the proposal received from the server.

[0624] Example: Show the user "Today's suggestion: navy suit with a white shirt and a navy tie."

[0625] ---

[0626] 9. Getting User Feedback

[0627] The user checks the proposed outfits and inputs their satisfaction and feedback via the terminal, including their rating of the suggestions and requests for additions.

[0628] Example: A user gives feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[0629] ---

[0630] 10. Record and learn from feedback

[0631] The server records feedback from users and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user.

[0632] Example: Record that "The combination of navy suit + white shirt + navy tie was well received" and reflect this in future proposals.

[0633] ---

[0634] Specific examples

[0635] One day, a user enters that they have a client meeting scheduled, the weather is sunny, and the temperature is 20 degrees. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it for the next suggestion.

[0636] In this way, the system makes optimal suggestions based on user information and improves accuracy through feedback.

[0637] The processing flow will be explained below.

[0638] Step 1:

[0639] A user accesses a device such as a smartphone or tablet and launches an application. The user enters information such as today's schedule, weather, temperature, and preferred style. Example: A user enters, "Today I'm meeting with a client. The weather is sunny and the temperature is 20 degrees."

[0640] Step 2:

[0641] The terminal sends the information entered by the user to the server. Specifically, it sends the data "Today's plan = meet with client," "Weather = sunny," and "Temperature = 20 degrees" to the server.

[0642] Step 3:

[0643] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[0644] Step 4:

[0645] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[0646] Step 5:

[0647] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[0648] Step 6:

[0649] The server uses the AI ​​to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule). Example: The server suggests "navy suit, white shirt, and navy tie."

[0650] Step 7:

[0651] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[0652] Step 8:

[0653] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[0654] Step 9:

[0655] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[0656] Step 10:

[0657] The server records user feedback and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user. Specifically, it records that "the combination of navy suit + white shirt + navy tie was well received" and reflects this in future suggestions.

[0658] Example 1

[0659] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0660] Conventional clothing selection support systems require users to take the time and effort to choose their outfits each time, and it is difficult to suggest outfits that take into account past clothing history and the latest trends. In addition, they lacked the functionality to prevent users from wearing the same outfit when meeting a specific person again, and the functionality to utilize user feedback to improve the accuracy of suggestions. Furthermore, it was difficult to incorporate changing weather information in real time.

[0661] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0662] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving data on past clothing history and clothing owned from a database; a means for retrieving external fashion and trend information; a means for retrieving weather forecast information; a means including a generative AI model that generates clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; and a means for recording user feedback and reflecting it in next suggestions. This allows the user to efficiently select optimal clothing and receive suggestions that take past history, the latest trends, and weather information into consideration. Furthermore, the server can avoid wearing the same clothes when meeting a specific person again, and can improve the accuracy of suggestions by utilizing feedback.

[0663] "User" refers to an individual who uses the system to receive clothing suggestions.

[0664] "Terminal" means a device through which a User inputs information and receives suggestions, including a smartphone, tablet, or PC.

[0665] "Server" refers to the central computer system that receives information from users, accesses various databases and APIs to generate outfit suggestions, and sends them to the device.

[0666] "Information input means" refers to an interface that allows a user to input information such as schedules, weather, temperature, and preferred style into the terminal.

[0667] "Clothing history acquisition means" refers to a function for acquiring the user's past clothing history and data on clothing owned by the user from a database.

[0668] "Means for obtaining fashion information" refers to the function for obtaining information from external fashion sites and trends via API.

[0669] "Means for obtaining weather information" refers to the function for obtaining the latest weather information using the weather forecast API.

[0670] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal clothing suggestions based on acquired information.

[0671] "Terminal transmission means" refers to a function that transmits the generated clothing suggestions to the user's terminal.

[0672] "Feedback recording means" refers to a function for recording feedback from users and reflecting that information in the next proposal.

[0673] The present invention is a system for assisting a user in selecting clothes, and has the following configuration.

[0674] System Overview

[0675] This system consists of a series of processes in which the user inputs information through a terminal, and the server then suggests appropriate clothing based on that information. Specific hardware used as the terminal is a smartphone or tablet for inputting user information. The server uses a virtual server on the cloud or a dedicated data center.

[0676] Hardware and software used

[0677] Devices: Smartphones, tablets, PCs

[0678] Server: Cloud server, database server

[0679] Software: API for HTTP requests, generative AI models (Python and machine learning frameworks such as TensorFlow)

[0680] Specific form of implementation

[0681] User input

[0682] A user uses a smartphone or tablet application to input information such as schedules, weather information, temperature, preferred style, etc. For example, a user inputs information such as "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[0683] Sending a request to the server

[0684] The device converts the user's input information into JSON format and sends it to the server via an HTTP request. Specifically, it sends a POST request to the API endpoint.

[0685] Retrieving user history

[0686] After receiving the request, the server accesses the database to retrieve data on the user's clothing history and the clothes they own. Specifically, it retrieves the data using SQL queries.

[0687] Obtaining trend information

[0688] The server obtains the latest information from external fashion sites and trend information services, using a RESTful API to understand the latest fashion trends and fashion trends.

[0689] Get weather information

[0690] The server retrieves the latest weather information from the weather forecast service, specifically, the current temperature and weather using the weather API.

[0691] Generative AI generates clothing suggestions

[0692] The server then sends prompts to the generative AI model based on the information it has acquired to generate optimal outfit suggestions, for example using prompts like the following:

[0693] To help users choose what to wear, please suggest the best outfit based on the following information:

[0694] Upcoming: Client Meeting

[0695] Weather: Sunny

[0696] Temperature: 20°C

[0697] User history: Navy suit, gray suit

[0698] Clothes I own: 4 suits, 8 shirts, 10 ties

[0699] Latest trends: business casual, monochrome

[0700] Once the generative AI model generates a proposal, the server receives it.

[0701] Sending the proposal to the device

[0702] The server sends the generated outfit suggestions to the user's device as an HTTP response. Specifically, the suggestion content is included in the POST request response in JSON format.

[0703] User notification of proposals

[0704] The terminal displays the suggestions received from the server on the application screen, for example, notifying the user of "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[0705] Get user feedback

[0706] The user inputs their satisfaction level and feedback about the suggested outfits. Specifically, they enter feedback such as "I'm satisfied with the suggestion" or "I would like to receive similar suggestions in the future" into the application form and click the submit button.

[0707] Record and learn from feedback

[0708] The server records the user feedback in a database and uses it as training data for the AI ​​model in future. The server saves the feedback in the database using an SQL query.

[0709] As a result, the present invention can provide efficient and personalized clothing suggestions, thereby increasing user satisfaction.

[0710] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0711] System program processing flow

[0712] Step 1: Enter your information

[0713] Users enter the following information using an application on their smartphone or tablet:

[0714] Input: Schedule, weather information, temperature, preferred style

[0715] Specific actions: Enter the information "Client meeting. Weather is sunny, temperature is 20 degrees" into the application's input form and press the send button.

[0716] Step 2: Submitting a request

[0717] The terminal converts the information entered by the user into JSON format and sends it to the server via an HTTP request.

[0718] Input: User input information

[0719] Output: JSON format data to send to the server

[0720] Specific behavior: Serializes the input information into JSON and sends a POST request to the API endpoint.

[0721] Step 3: Retrieving User History

[0722] After receiving the request, the server accesses the database to retrieve data on the user's past clothing history and the clothes they own.

[0723] Input: User ID

[0724] Output: Past clothing history and clothing owned data

[0725] Specific operation: Execute the SQL query "SELECT FROM user_clothing_history WHERE user_id = 'User ID'" and retrieve the results.

[0726] Step 4: Obtaining trend information

[0727] The server obtains the latest information from external fashion sites and trend information services.

[0728] Input: Fashion Information API endpoint

[0729] Output: Trend information

[0730] Specific behavior: Uses the RESTful API to send a "GET / fashion-trends" request and receives trend information in JSON format.

[0731] Step 5: Get Weather Information

[0732] The server retrieves the latest weather information from a weather forecast service.

[0733] Input: Weather API endpoint and specified location

[0734] Output: Weather information (current temperature and weather)

[0735] Specific operation: Sends a request "GET / weather?location=specified location" and receives weather information in JSON format.

[0736] Step 6: Generative AI generates outfit suggestions

[0737] The server sends prompts to the generative AI model based on the acquired information to generate optimal outfit suggestions.

[0738] Input: User's past clothing data, latest trend information, weather information, schedule information

[0739] Output: Generated outfit suggestions

[0740] How it works: Using a Python script, the generative AI model is fed the following prompt: "Client meeting scheduled. Weather: sunny, temperature: 20 degrees. Past outfits: navy suit, gray suit. Owned: four suits, eight shirts, ten ties. Current trends: business casual, monochrome," and the resulting suggestions are then analyzed.

[0741] Step 7: Send the proposal to the device

[0742] The server sends the generated clothing suggestions to the user's terminal as an HTTP response.

[0743] Input: Generated outfit suggestions

[0744] Output: Response to the user's device

[0745] Specific operation: The proposal content is converted into JSON format and returned in the HTTP response.

[0746] Step 8: Notify users of the proposal

[0747] The terminal displays the proposal received from the server on the application screen.

[0748] Input: Response from the server (generated outfit suggestions)

[0749] Output: Display on the application screen

[0750] Specific operation: Analyze the received suggestion and display "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[0751] Step 9: Get user feedback

[0752] The user inputs their satisfaction and feedback about the suggested outfit.

[0753] Input: Satisfaction and comments about the proposal

[0754] Output: Feedback sent from the device to the server

[0755] Specific actions: Enter "I'm satisfied with the proposal" and "I would like to receive similar proposals in the future" in the feedback form and click the submit button.

[0756] Step 10: Record and learn from feedback

[0757] The server records user feedback in a database and uses it as learning data for the AI ​​model in future runs.

[0758] Input: User feedback

[0759] Output: Save to database and training data for AI model

[0760] Specific behavior: Execute the SQL query "INSERT INTO feedback (user_id, feedback_text) VALUES ('user_id', 'Satisfied with the suggestion')" to save the feedback.

[0761] The above is the specific processing flow of the system program.

[0762] (Application example 1)

[0763] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0764] In conventional clothing suggestion systems, even if a user inputs information such as their schedule, weather, temperature, and preferred style, the suggested outfits may not necessarily suit the user's preferences or the situation of the day. Furthermore, suggesting outfits in physical stores places a heavy burden on store staff, making it difficult to make efficient suggestions. Furthermore, the system lacks the functionality to prevent users from wearing the same outfit when meeting a specific person from the past, and the feedback functionality to improve the accuracy of suggestions.

[0765] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0766] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving past clothing history and clothing data from a database; a means for retrieving fashion and trend information; a means for retrieving weather forecast information; a generation AI means for generating clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; a means for displaying the suggestions using a smart mirror; and a means for recording user feedback and reflecting it in future suggestions. This allows users to receive optimal clothing suggestions through a smart mirror installed in a physical store, thereby reducing the burden on store staff and enabling efficient clothing suggestions. Furthermore, by utilizing past history and feedback, the accuracy of suggestions can be improved, increasing user satisfaction.

[0767] "Means for users to input information such as schedules, weather, temperature, and preferred styles" refers to an interface that allows users to input information about the day's schedule, current weather, temperature, and preferred fashion style.

[0768] "Means for retrieving data on past clothing history and owned clothing from a database" is a function for retrieving data on the clothing history that a user has worn and the clothing in their closet from a database.

[0769] "Means for obtaining fashion information and trend information" refers to a function for obtaining the latest fashion information and trend information from sources on the Internet.

[0770] "Means for obtaining weather forecast information" refers to a function for obtaining current and future weather information from a weather forecast API, etc.

[0771] The "generative AI means for generating clothing suggestions based on acquired information" is an artificial intelligence model that comprehensively analyzes information entered by the user, past clothing history, trend information, weather information, etc., to generate optimal clothing suggestions.

[0772] "Means for sending generated suggestions to the user's device" refers to a function for notifying the user of clothing suggestions created by the generation AI on their device, such as a smartphone or tablet.

[0773] The "means for displaying suggestions using a smart mirror" is a function for displaying the generated clothing suggestions on a smart mirror installed in a physical store.

[0774] "Means for recording user feedback and reflecting it in the next proposal" is a function for collecting the evaluations and opinions given by users on the proposals and using them in the next clothing proposal.

[0775] This invention is a system that allows users to choose clothes more efficiently and effectively, and is implemented based on the following procedures and configuration: In this system, a server generates optimal outfit suggestions based on user input information and displays them to the user through a smart mirror.

[0776] System configuration

[0777] The system consists of a terminal where users input information, a server that processes the data, and a smart mirror that displays the output, allowing the system to suggest the most suitable outfit for the user.

[0778] Hardware and software used

[0779] Hardware

[0780] Smart Mirror

[0781] server

[0782] software

[0783] Weather Forecast API

[0784] Trend information acquisition API

[0785] Generative AI models (e.g., OpenAI GPT, fashion-specific generative models)

[0786] Implementation procedures and processing details

[0787] Users can stand in front of a smart mirror installed in a store and use voice or touch input to input information about their plans for the day, the weather, the temperature, and their preferred style. This information is collected by the smart mirror and sent to the server.

[0788] The server processes the data using the following means:

[0789] 1. User information input processing: Information such as schedule, weather, temperature, and style is acquired from the smart mirror. This information is sent to the server and analyzed.

[0790] Example: Say "I have a business meeting today" and input that the weather is sunny and the temperature is 20 degrees.

[0791] Example prompt: "What is the best outfit to wear for a business meeting on a sunny 20 degree day?"

[0792] 2. Database reference process: The server accesses the database to retrieve the user's clothing history and the clothing they own. This allows the server to make optimal recommendations while preventing duplication of past clothing.

[0793] 3. Trend information acquisition: The server acquires information from fashion sites and trends on the Internet through the API. This is an important element in suggesting the latest trends and stylish styles to users.

[0794] 4. Weather information acquisition process: The latest weather information is acquired using a weather forecast API. This information, along with the user's schedule for the day and preferred style, is input into the generation AI.

[0795] 5. Proposal generation process using generative AI: Based on the acquired data, a generative AI model generates optimal clothing suggestions. For example, OpenAI GPT or a fashion-specific generative model can be used to suggest specific outfits that the user should wear.

[0796] 6. Display of suggested outfits: The generated outfit suggestions are displayed on the smart mirror, allowing the user to see the specific outfit image.

[0797] 7. Feedback acquisition process: The user inputs their satisfaction with the clothing suggestions and any additional requests they may have. This feedback is sent to the server and will be used for future suggestions. For example, possible feedback is "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[0798] 8. Learning process: The server records user feedback as learning data to improve the accuracy of the generative AI model, allowing it to make suggestions that are more tailored to the user's preferences and tendencies.

[0799] Specific examples

[0800] When a user stands in front of a smart mirror and inputs that they have a business meeting and that the weather is sunny and 20 degrees, the server prompts the generation AI based on past clothing history, trend information, and weather information. The generation AI generates a suggestion of "navy suit, white shirt, navy tie" and displays it on the smart mirror. If the user is satisfied with this suggestion, their feedback will be reflected in the next suggestion. In this way, the system can make clothing suggestions that suit the user and continuously improve its accuracy through feedback.

[0801] This system provides support to users to effectively select clothing, improving the efficiency and accuracy of clothing suggestions in physical stores.

[0802] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0803] Step 1:

[0804] The user stands in front of the smart mirror and inputs information such as their schedule for the day, the weather, the temperature, and their preferred style. This information is input using voice or the touch panel. This information is entered into the smart mirror and sent to the terminal as a single data package.

[0805] Input: Schedule, weather, temperature, preferred style

[0806] Output: User input data package

[0807] Step 2:

[0808] The terminal transmits the information entered by the user to the server, which receives the information and prepares it for analysis.

[0809] Input: User input data package

[0810] Output: Data sent to the server

[0811] Step 3:

[0812] The server accesses the database to retrieve the user's clothing history and the clothing they own, allowing them to check which clothing items they have used in the past and which they currently have on hand.

[0813] Input: User ID

[0814] Output: User's past clothing history data and clothing data

[0815] Step 4:

[0816] The server obtains the latest fashion and trend information through an external API, and uses the information as reference data to suggest optimal clothing.

[0817] Input: Fashion information API request

[0818] Output: Latest fashion and trend information

[0819] Step 5:

[0820] The server uses a weather forecast API to obtain current weather and temperature information, which is necessary to suggest comfortable clothing for the user.

[0821] Input: Weather API request

[0822] Output: Weather forecast information

[0823] Step 6:

[0824] The server uses a generative AI model to generate outfit suggestions based on the user's past clothing history data, clothing data, the latest fashion and trend information, weather forecast information, and user input data. For example, a prompt such as "What is the best outfit to wear for a business meeting on a sunny 20-degree day?" is input to the generative AI model.

[0825] Input: User's past clothing history data, clothing data, fashion information, trend information, weather forecast information, user input data, prompt text

[0826] Output: Generated outfit suggestions

[0827] Step 7:

[0828] The generated outfit suggestions are sent from the server to the smart mirror, which displays the suggested outfits on its screen for the user to visually confirm.

[0829] Input: Generated outfit suggestions

[0830] Output: Outfit suggestions displayed on a smart mirror

[0831] Step 8:

[0832] Users can input their satisfaction and feedback about the suggested outfits through the smart mirror, which will then be sent to the server and reflected in the next outfit suggestions.

[0833] Input: User feedback

[0834] Output: Feedback data sent to the server

[0835] Step 9:

[0836] The server records the collected feedback in a database and uses it as training data for the generative AI model, thereby improving the accuracy of future suggestions.

[0837] Input: User feedback data

[0838] Output: An updated generative AI model

[0839] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0840] This invention is a system for assisting users in choosing clothing, and in particular, it recognizes the user's emotions and suggests appropriate clothing based on those emotions. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc., and also combining it with an emotion engine that recognizes the user's emotions.

[0841] ---

[0842] System Overview

[0843] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. This system integrates the user's input information, the database on the server, generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits.

[0844] ---

[0845] Specific Embodiments of the System

[0846] 1. User input

[0847] The user enters the following information using a device such as a smartphone or tablet.

[0848] Today's Schedule

[0849] Weather information

[0850] temperature

[0851] Your preferred style

[0852] The user's current sentiment

[0853] Example: User types, "I have a client meeting today. The weather is sunny, the temperature is 20 degrees, and I'm feeling a bit nervous today."

[0854] ---

[0855] 2. Sending a request to the server

[0856] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[0857] ---

[0858] 3. Obtaining user history

[0859] The server accesses the database to obtain the user's clothing history and the clothing they own, thereby obtaining information to prevent the use of duplicate clothing.

[0860] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[0861] ---

[0862] 4. Obtaining trend information

[0863] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and style guidelines.

[0864] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[0865] ---

[0866] 5. Obtaining Weather Information

[0867] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[0868] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[0869] ---

[0870] 6. Emotion Recognition by Emotion Engine

[0871] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[0872] ---

[0873] 7. Generating clothing suggestions using generative AI

[0874] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[0875] ---

[0876] 8. Sending the proposal to the device

[0877] The server transmits the generated clothing suggestions to the user's terminal, specifically, the generated suggestions are transmitted to the terminal via a network.

[0878] ---

[0879] 9. User notification of proposals

[0880] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[0881] Example: The user confirms the displayed suggestions.

[0882] ---

[0883] 10. Getting User Feedback

[0884] The user checks the suggested outfits and inputs their satisfaction and feedback via the terminal. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[0885] ---

[0886] 11. Recording and learning feedback and emotion data

[0887] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, the server records data such as "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[0888] ---

[0889] Specific examples

[0890] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it to improve the next suggestion.

[0891] In this way, the system makes optimal suggestions based on the user's information and emotions, and improves accuracy through feedback.

[0892] The processing flow will be explained below.

[0893] Step 1:

[0894] A user accesses a device such as a smartphone or tablet and launches the application. The user inputs their plans for the day, the weather, the temperature, their preferred style, and their current emotions. Example: A user inputs, "Today I'm meeting with a client. The weather is sunny, the temperature is 20 degrees, and I'm feeling a little nervous today."

[0895] Step 2:

[0896] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[0897] Step 3:

[0898] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[0899] Step 4:

[0900] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[0901] Step 5:

[0902] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[0903] Step 6:

[0904] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[0905] Step 7:

[0906] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[0907] Step 8:

[0908] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[0909] Step 9:

[0910] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user. The user confirms the displayed proposal.

[0911] Step 10:

[0912] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[0913] Step 11:

[0914] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, it records that "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[0915] Example 2

[0916] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0917] Conventional clothing suggestion systems generate suggestions based on the user's schedule, weather, temperature, and preferred style, but do not take the user's emotions into account and are unable to generate different suggestions based on individual emotional changes.In addition, the system lacks a mechanism for incorporating user feedback into the generated suggestions, limiting the improvement of suggestion accuracy.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0919] In this invention, the server includes a means for the user to input information such as schedule, weather, temperature, preferred style, and current emotion; a means for retrieving data on past clothing history and clothing owned from a database; and a means for analyzing the user's emotion data using an emotion engine. This allows for more appropriate clothing suggestions that take the user's emotions into consideration. Furthermore, by recording user feedback on generated suggestions and reflecting this in the next suggestions, the accuracy of the suggestions can be improved.

[0920] "Schedule" refers to the schedule or plan that the user will carry out that day.

[0921] "Weather" refers to the current weather and meteorological conditions.

[0922] "Temperature" refers to the temperature and temperature information for that day.

[0923] "Preferred style" refers to the clothing or fashion style that the user prefers.

[0924] "Emotions" refers to the user's current state of mind and emotional ups and downs.

[0925] "Past clothing history" refers to a record of clothing previously worn by the user.

[0926] "Data on clothes owned" refers to information about clothes and accessories owned by the user.

[0927] "Fashion information" refers to information about the latest fashions and styles.

[0928] "Trend information" refers to currently popular fashions and style guidelines.

[0929] "Weather forecast information" refers to information predicting future weather and temperature.

[0930] An "emotion engine" refers to a system that analyzes the emotion data entered by the user and recognizes their emotional state.

[0931] "Generative AI" refers to an artificial intelligence model that generates optimal clothing suggestions based on multiple pieces of information.

[0932] "User's terminal" refers to a computer device such as a smartphone or tablet used by a user.

[0933] "Feedback" refers to ratings and comments provided by users on suggested outfits.

[0934] This invention is a system for assisting users in choosing clothing, suggesting appropriate clothing based on information such as the user's schedule, weather, temperature, preferred style, current emotion, etc. In particular, this system is characterized by using an emotion engine to recognize the user's emotions and a generative AI model to suggest optimal clothing.

[0935] System configuration

[0936] The system includes a terminal where users can input information, a server that receives the information, and multiple engines and APIs that process and analyze the information.The database also records the user's past clothing history, clothing data, and user feedback.

[0937] Hardware and software used

[0938] 1. Terminal: A device that a user uses to input information, such as a smartphone or tablet.

[0939] 2. Server: Receives requests and processes information by interacting with databases and APIs.

[0940] 3. Database: A storage device for storing user history data and feedback.

[0941] 4. Emotion engine: Software for analyzing user emotion data.

[0942] 5. Generative AI model: Artificial intelligence that generates clothing suggestions based on user information.

[0943] 6. API: External services for obtaining trending and weather information.

[0944] Data processing and calculation

[0945] 1. Enter your information:

[0946] Users use a smartphone or tablet to input their "today's schedule," "weather," "temperature," "preferred style," and "current feelings" into the system.

[0947] 2. Submit your request:

[0948] The entered information is sent from the terminal to the server in JSON format or similar.

[0949] 3. Retrieving from the database:

[0950] The server retrieves data on past clothing history and clothing owned from the database, which helps to avoid duplication of previously used clothing.

[0951] 4. Get trend and weather information:

[0952] The server obtains the latest fashion information and weather forecasts through external APIs. Trend information includes currently popular fashion styles, and weather information includes current weather conditions and forecasts.

[0953] 5. Emotion engine analysis:

[0954] The server uses an emotion engine to analyze the emotion data entered by the user and determine the user's emotional state.

[0955] 6. Generating outfit suggestions:

[0956] The server uses a generative AI model based on all the information it has acquired so far (user's schedule, weather, temperature, preferred style, emotions, past clothing history, data on clothing owned, and trend information) to generate optimal clothing suggestions.

[0957] 7. Submission and Notification of Proposals:

[0958] The generated clothing suggestions are sent from the server to the user's terminal via the network, and the terminal notifies the user of the suggestions.

[0959] Specific examples

[0960] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user reviews this suggestion and provides feedback that they are satisfied. This feedback is recorded on the server and reflected in future suggestions.

[0961] Prompt Sentence Examples

[0962] "What is the best business casual attire for when you have a client meeting, it's sunny, it's 20 degrees, and you're a little nervous?"

[0963] In this way, the system makes optimal clothing suggestions based on the user's information and emotions, and incorporates feedback to improve the accuracy of the suggestions.

[0964] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0965] Step 1:

[0966] Users input information using devices such as smartphones and tablets. Specifically, they input information such as "today's schedule," "weather," "temperature," "preferred style," and "current emotion" into the application's input fields. This generates user input data.

[0967] Input: Today's schedule, weather, temperature, preferred style, current mood

[0968] Output: Input data (e.g., {Appointment: "Meet with a client", Weather: "Sunny", Temperature: 20, Style: "Business", Emotion: "Nervous"})

[0969] Step 2:

[0970] The terminal converts the information entered by the user into JSON format or similar and sends it to the server. Specifically, it sends the data to the server via an HTTP request, which allows the server to receive the data.

[0971] Input: Input data

[0972] Output: Request sent to server

[0973] Step 3:

[0974] The server analyzes the received data and accesses the database to retrieve the user's past clothing history and the clothes they own. It uses an SQL query to search the data and retrieves the results. This passes the user's past clothing history and a list of the clothes they currently own to the server.

[0975] Input: Received data

[0976] Output: User's past clothing history, clothing data

[0977] Step 4:

[0978] The server sends a request to an external API to get the latest fashion and trend information. For example, it sends a GET request to a fashion API to get trend information. This data is then processed on the server.

[0979] Input: API request

[0980] Output: Latest fashion and trend information

[0981] Step 5:

[0982] The server accesses the weather forecast API to obtain the latest weather information for the specified area. For example, it sends a GET request to the weather API and obtains weather and temperature information from the response. This data is stored on the server.

[0983] Input: API request

[0984] Output: Weather information, temperature information

[0985] Step 6:

[0986] The server uses an emotion engine to analyze the user's emotion data. Specifically, it analyzes the user's emotion input, such as "tension," to recognize the user's detailed emotional state. This information is used for further data processing.

[0987] Input: Emotion data

[0988] Output: Parsed emotional state

[0989] Step 7:

[0990] The server uses a generative AI model based on all the information it has acquired (user schedule, weather, temperature, preferred style, emotions, past clothing history, clothing data owned, and trend information) to generate optimal outfit suggestions. Text data combining this information is used as input prompts for the generative AI model.

[0991] Input: All acquired information

[0992] Output: Generated outfit suggestions

[0993] Step 8:

[0994] The server converts the generated clothing suggestions into JSON format and sends them to the user's device. The server then sends the suggestion data as an HTTP response over the network, allowing the user's device to receive the suggestions.

[0995] Input: Generated outfit suggestions

[0996] Output: Send the proposal to the device

[0997] Step 9:

[0998] The device notifies the user of the suggestions received from the server. Specifically, the suggestions are displayed as a pop-up or notification message within the application, allowing the user to confirm the suggestions.

[0999] Input: Receive proposal

[1000] Output: User notification of the proposal

[1001] Step 10:

[1002] The user reviews the proposed outfits and enters their satisfaction and feedback within the application, which generates evaluation comments.

[1003] Input: Confirm proposal

[1004] Output: Feedback data

[1005] Step 11:

[1006] The server records the feedback and emotion data provided by the user in a database and uses this data as training data to improve the accuracy of future suggestions.

[1007] Input: Feedback data, emotion data

[1008] Output: Update learning data, improve proposal accuracy

[1009] (Application example 2)

[1010] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1011] Conventional clothing suggestion systems do not take into account the user's emotions when making suggestions, making it difficult to suggest the best outfit for the user's mood and situation. Furthermore, they lacked connectivity with physical stores, making it impossible to purchase suggested outfits on the spot. Furthermore, they lacked the application of user feedback to improve the accuracy of suggestions.

[1012] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1013] In this invention, the server includes means for the user to input information such as schedule, weather, temperature, and preferred style, means for retrieving data on past clothing history and clothing owned from a database, means for retrieving fashion information and trend information, means for retrieving weather forecast information, means for recognizing the user's emotions in real time, generation AI means for generating outfit suggestions based on the retrieved information, means for displaying the suggested outfits as items available for purchase in a physical store, means for transmitting the generated suggestions to the user's terminal, and means for recording user feedback and reflecting it in the next suggestion. This makes it possible to take the user's emotions into consideration, facilitate purchases in a physical store, and improve the accuracy of suggestions based on feedback.

[1014] "Schedule" is information indicating the plans and activities that the user will undertake on that day.

[1015] "Weather" is information that indicates the weather conditions and meteorological conditions for that day.

[1016] "Temperature" is information indicating the temperature of the atmosphere at a specific time.

[1017] "Preferred style" is information that indicates the user's preferred fashion and clothing trends.

[1018] "Past clothing history" is a record of clothing worn by the user in the past.

[1019] "Data on clothes owned" is information on the types and quantities of clothes owned by the user.

[1020] A "database" is a collection of data and a system for efficiently managing information.

[1021] "Fashion information" is information about the latest clothing and accessories.

[1022] "Trend information" is information about currently popular fashions and styles.

[1023] "Weather forecast information" is information that indicates a prediction of future weather.

[1024] "Means for recognizing a user's emotions in real time" refers to technology for instantly analyzing a user's current emotions.

[1025] "Generative AI means" refers to artificial intelligence technology for generating clothing suggestions based on acquired information.

[1026] "In-store purchaseable items" are products that can be purchased immediately in a physical store.

[1027] The "means for transmitting the generated suggestions to the user's terminal" is a technique for sending the outfit suggestions to the device used by the user.

[1028] "User feedback" is information indicating evaluations and opinions from users.

[1029] "Means to reflect in the next proposal" refers to techniques for incorporating user feedback into the next proposal.

[1030] System Configuration

[1031] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and a server then suggests appropriate outfits based on that information. The system integrates the user's input information, a database on the server, a generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits. It also has a function to support purchasing in physical stores.

[1032] Hardware and Software

[1033] Hardware:

[1034] Smartphone / Tablet: A device for entering user information and using the emotion recognition camera.

[1035] Server: A system for processing data and hosting APIs.

[1036] software:

[1037] OpenCV: A library for face detection and image processing.

[1038] Keras: A deep learning framework used for loading and inferencing emotion recognition models.

[1039] Requests: A library for making requests to external APIs (weather API, fashion API).

[1040] System action

[1041] The server performs the following data processing and calculations: The user uses their device to input information such as their schedule, weather, temperature, and preferred style, and sends that information to the server. The server retrieves the user's past clothing history and clothing data from a database, and also obtains external fashion information, trend information, and weather forecast information via an API. Next, it analyzes the user's current emotions using an emotion engine that recognizes the user's emotions in real time. A generation AI generates outfit suggestions based on the acquired information and sends these suggestions to the user's device. It also displays items that can be purchased in physical stores based on the suggested outfits to assist with purchasing. User feedback is collected and reflected in future suggestions, improving the accuracy of the suggestions.

[1042] Specific examples

[1043] One day, a user enters a physical store and types "I'm in a fun party mood today" into their device. The system uses an emotion engine to analyze the user's emotions and retrieves past clothing history, clothing owned, weather information, and fashion information from the database. Based on this information, the generation AI suggests the most suitable outfit, and the suggestion is sent to the user's device. The suggested outfit is then displayed as an item available for purchase in the physical store. The user purchases the outfit based on the suggestion and provides feedback on their satisfaction. The server records this feedback and reflects it in the next suggestion.

[1044] A specific example of a prompt is as follows:

[1045] "Today's suggestion: I'm going to a party and I'd like to know what items I can buy at a nearby store. I'm in a fun mood."

[1046] The invention is a system that helps users easily choose the best outfit for their mood and situation, and also helps them make purchases in physical stores smoothly. This system is expected to increase user satisfaction and improve the shopping experience in stores.

[1047] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1048] Step 1:

[1049] The user uses the device to input information such as schedule, weather, temperature, preferred style, and emotions.

[1050] Input: User's schedule, weather, temperature, preferred style, emotions

[1051] Output: User information entered into the terminal

[1052] Specifically, the user inputs "I'm going to a party today and I'm in a good mood," which is then saved on the device.

[1053] Step 2:

[1054] The terminal transmits the input information to the server.

[1055] Input: User information stored on the device

[1056] Output: User information sent to the server

[1057] Specifically, the terminal transmits information such as "I'm going to a party today and I'm in a good mood" to the server via the network.

[1058] Step 3:

[1059] The server retrieves the user's past clothing history and data on the clothes they own from the database.

[1060] Input: User ID

[1061] Output: User's past clothing history and clothing data

[1062] Specifically, the server accesses the database and retrieves data such as "navy suit, white shirt, navy tie."

[1063] Step 4:

[1064] The server obtains fashion and trend information from the Internet via an API.

[1065] Input: API request

[1066] Output: Latest fashion and trend information

[1067] Specifically, the server sends a request to the fashion API and obtains information such as "business casual and monochrome are in fashion."

[1068] Step 5:

[1069] The server obtains weather information using the weather forecast API.

[1070] Input: Weather API request

[1071] Output: Weather information (sunny, temperature 20 degrees)

[1072] Specifically, the server accesses the weather forecast API and obtains information such as "The weather is sunny, with a maximum temperature of 20 degrees."

[1073] Step 6:

[1074] The server uses an emotion engine to recognize emotion information input by the user.

[1075] Input: User emotion data

[1076] Output: Perceived emotion (happy)

[1077] Specifically, the server inputs "feeling happy" into the emotion engine and analyzes it.

[1078] Step 7:

[1079] Based on the information acquired by the server, clothing suggestions are generated using a generation AI.

[1080] Input: Past clothing history, fashion information, weather information, schedule, emotions

[1081] Output: Generated outfit suggestions

[1082] Specifically, the generative AI generates suggestions such as "navy suit, white shirt, navy tie."

[1083] Step 8:

[1084] The server transmits the generated clothing suggestions to the terminal.

[1085] Input: Generated outfit suggestions

[1086] Output: Outfit suggestions sent to the device

[1087] Specifically, the server sends the suggestion to the terminal via the network, and "Today's suggestion: navy suit, white shirt, navy tie" is displayed.

[1088] Step 9:

[1089] The suggested outfits are displayed as items available for purchase in physical stores.

[1090] Input: Generated outfit suggestions

[1091] Output: A list of items available for purchase

[1092] Specifically, the terminal displays a list of products available for purchase at store A.

[1093] Step 10:

[1094] The user reviews the suggested outfits and enters feedback.

[1095] Input: User feedback

[1096] Output: Feedback data

[1097] Specifically, the user inputs feedback such as "I'm satisfied with this suggestion" and saves it on the device.

[1098] Step 11:

[1099] The server records the user's feedback and reflects it in future suggestions.

[1100] Input: Feedback data

[1101] Output: Updated user database

[1102] Specifically, the server records data such as "the combination of navy suit + white shirt + navy tie is popular" and uses it for the next proposal.

[1103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1105] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1106] [Third embodiment]

[1107] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1111] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1115] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1117] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1118] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1119] The present invention is a system for assisting a user in selecting clothing, and is implemented through the following steps. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc. Specific embodiments of the system are described in detail below.

[1120] ---

[1121] System Overview

[1122] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. The system integrates the user's input information, the database on the server, generation AI, trend information, and weather information to suggest optimal outfits.

[1123] ---

[1124] Specific Embodiments of the System

[1125] 1. User input

[1126] The user enters the following information using a device such as a smartphone or tablet.

[1127] Today's Schedule

[1128] Weather information

[1129] temperature

[1130] Your preferred style

[1131] Example: User types, "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[1132] ---

[1133] 2. Sending a request to the server

[1134] The device sends the information the user has entered, including schedule information, weather, temperature, and style preferences, to a server.

[1135] ---

[1136] 3. Obtaining user history

[1137] The server accesses the database to obtain the user's clothing history and the clothing they own, which allows them to check which clothes they have worn at other events and to obtain information to prevent duplication.

[1138] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[1139] ---

[1140] 4. Obtaining trend information

[1141] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and fashionable styles.

[1142] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[1143] ---

[1144] 5. Obtaining Weather Information

[1145] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[1146] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[1147] ---

[1148] 6. Generating clothing suggestions using generative AI

[1149] The server uses a generation AI to generate optimal clothing suggestions based on the information it has acquired (the user's past clothing data, trend information, weather information, and schedule).

[1150] For example, suggest a navy suit, white shirt, and navy tie.

[1151] ---

[1152] 7. Sending the proposal to the device

[1153] The server transmits the generated clothing suggestions to the user's terminal.

[1154] ---

[1155] 8. User notification of proposals

[1156] The terminal notifies the user of the proposal received from the server.

[1157] Example: Show the user "Today's suggestion: navy suit with a white shirt and a navy tie."

[1158] ---

[1159] 9. Getting User Feedback

[1160] The user checks the proposed outfits and inputs their satisfaction and feedback via the terminal, including their rating of the suggestions and requests for additions.

[1161] Example: A user gives feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[1162] ---

[1163] 10. Record and learn from feedback

[1164] The server records feedback from users and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user.

[1165] Example: Record that "The combination of navy suit + white shirt + navy tie was well received" and reflect this in future proposals.

[1166] ---

[1167] Specific examples

[1168] One day, a user enters that they have a client meeting scheduled, the weather is sunny, and the temperature is 20 degrees. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it for the next suggestion.

[1169] In this way, the system makes optimal suggestions based on user information and improves accuracy through feedback.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] A user accesses a device such as a smartphone or tablet and launches an application. The user enters information such as today's schedule, weather, temperature, and preferred style. Example: A user enters, "Today I'm meeting with a client. The weather is sunny and the temperature is 20 degrees."

[1173] Step 2:

[1174] The terminal sends the information entered by the user to the server. Specifically, it sends the data "Today's plan = meet with client," "Weather = sunny," and "Temperature = 20 degrees" to the server.

[1175] Step 3:

[1176] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[1177] Step 4:

[1178] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[1179] Step 5:

[1180] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[1181] Step 6:

[1182] The server uses the AI ​​to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule). Example: The server suggests "navy suit, white shirt, and navy tie."

[1183] Step 7:

[1184] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[1185] Step 8:

[1186] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[1187] Step 9:

[1188] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[1189] Step 10:

[1190] The server records user feedback and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user. Specifically, it records that "the combination of navy suit + white shirt + navy tie was well received" and reflects this in future suggestions.

[1191] Example 1

[1192] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1193] Conventional clothing selection support systems require users to take the time and effort to choose their outfits each time, and it is difficult to suggest outfits that take into account past clothing history and the latest trends. In addition, they lacked the functionality to prevent users from wearing the same outfit when meeting a specific person again, and the functionality to utilize user feedback to improve the accuracy of suggestions. Furthermore, it was difficult to incorporate changing weather information in real time.

[1194] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1195] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving data on past clothing history and clothing owned from a database; a means for retrieving external fashion and trend information; a means for retrieving weather forecast information; a means including a generative AI model that generates clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; and a means for recording user feedback and reflecting it in next suggestions. This allows the user to efficiently select optimal clothing and receive suggestions that take past history, the latest trends, and weather information into consideration. Furthermore, the server can avoid wearing the same clothes when meeting a specific person again, and can improve the accuracy of suggestions by utilizing feedback.

[1196] "User" refers to an individual who uses the system to receive clothing suggestions.

[1197] "Terminal" means a device through which a User inputs information and receives suggestions, including a smartphone, tablet, or PC.

[1198] "Server" refers to the central computer system that receives information from users, accesses various databases and APIs to generate outfit suggestions, and sends them to the device.

[1199] "Information input means" refers to an interface that allows a user to input information such as schedules, weather, temperature, and preferred style into the terminal.

[1200] "Clothing history acquisition means" refers to a function for acquiring the user's past clothing history and data on clothing owned by the user from a database.

[1201] "Means for obtaining fashion information" refers to the function for obtaining information from external fashion sites and trends via API.

[1202] "Means for obtaining weather information" refers to the function for obtaining the latest weather information using the weather forecast API.

[1203] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal clothing suggestions based on acquired information.

[1204] "Terminal transmission means" refers to a function that transmits the generated clothing suggestions to the user's terminal.

[1205] "Feedback recording means" refers to a function for recording feedback from users and reflecting that information in the next proposal.

[1206] The present invention is a system for assisting a user in selecting clothes, and has the following configuration.

[1207] System Overview

[1208] This system consists of a series of processes in which the user inputs information through a terminal, and the server then suggests appropriate clothing based on that information. Specific hardware used as the terminal is a smartphone or tablet for inputting user information. The server uses a virtual server on the cloud or a dedicated data center.

[1209] Hardware and software used

[1210] Devices: Smartphones, tablets, PCs

[1211] Server: Cloud server, database server

[1212] Software: API for HTTP requests, generative AI models (Python and machine learning frameworks such as TensorFlow)

[1213] Specific form of implementation

[1214] User input

[1215] A user uses a smartphone or tablet application to input information such as schedules, weather information, temperature, preferred style, etc. For example, a user inputs information such as "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[1216] Sending a request to the server

[1217] The device converts the user's input information into JSON format and sends it to the server via an HTTP request. Specifically, it sends a POST request to the API endpoint.

[1218] Retrieving user history

[1219] After receiving the request, the server accesses the database to retrieve data on the user's clothing history and the clothes they own. Specifically, it retrieves the data using SQL queries.

[1220] Obtaining trend information

[1221] The server obtains the latest information from external fashion sites and trend information services, using a RESTful API to understand the latest fashion trends and fashion trends.

[1222] Get weather information

[1223] The server retrieves the latest weather information from the weather forecast service, specifically, the current temperature and weather using the weather API.

[1224] Generative AI generates clothing suggestions

[1225] The server then sends prompts to the generative AI model based on the information it has acquired to generate optimal outfit suggestions, for example using prompts like the following:

[1226] To help users choose what to wear, please suggest the best outfit based on the following information:

[1227] Upcoming: Client Meeting

[1228] Weather: Sunny

[1229] Temperature: 20°C

[1230] User history: Navy suit, gray suit

[1231] Clothes I own: 4 suits, 8 shirts, 10 ties

[1232] Latest trends: business casual, monochrome

[1233] Once the generative AI model generates a proposal, the server receives it.

[1234] Sending the proposal to the device

[1235] The server sends the generated outfit suggestions to the user's device as an HTTP response. Specifically, the suggestion content is included in the POST request response in JSON format.

[1236] User notification of proposals

[1237] The terminal displays the suggestions received from the server on the application screen, for example, notifying the user of "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[1238] Get user feedback

[1239] The user inputs their satisfaction level and feedback about the suggested outfits. Specifically, they enter feedback such as "I'm satisfied with the suggestion" or "I would like to receive similar suggestions in the future" into the application form and click the submit button.

[1240] Record and learn from feedback

[1241] The server records the user feedback in a database and uses it as training data for the AI ​​model in future. The server saves the feedback in the database using an SQL query.

[1242] As a result, the present invention can provide efficient and personalized clothing suggestions, thereby increasing user satisfaction.

[1243] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1244] System program processing flow

[1245] Step 1: Enter your information

[1246] Users enter the following information using an application on their smartphone or tablet:

[1247] Input: Schedule, weather information, temperature, preferred style

[1248] Specific actions: Enter the information "Client meeting. Weather is sunny, temperature is 20 degrees" into the application's input form and press the send button.

[1249] Step 2: Submitting a request

[1250] The terminal converts the information entered by the user into JSON format and sends it to the server via an HTTP request.

[1251] Input: User input information

[1252] Output: JSON format data to send to the server

[1253] Specific behavior: Serializes the input information into JSON and sends a POST request to the API endpoint.

[1254] Step 3: Retrieving User History

[1255] After receiving the request, the server accesses the database to retrieve data on the user's past clothing history and the clothes they own.

[1256] Input: User ID

[1257] Output: Past clothing history and clothing owned data

[1258] Specific operation: Execute the SQL query "SELECT FROM user_clothing_history WHERE user_id = 'User ID'" and retrieve the results.

[1259] Step 4: Obtaining trend information

[1260] The server obtains the latest information from external fashion sites and trend information services.

[1261] Input: Fashion Information API endpoint

[1262] Output: Trend information

[1263] Specific behavior: Uses the RESTful API to send a "GET / fashion-trends" request and receives trend information in JSON format.

[1264] Step 5: Get Weather Information

[1265] The server retrieves the latest weather information from a weather forecast service.

[1266] Input: Weather API endpoint and specified location

[1267] Output: Weather information (current temperature and weather)

[1268] Specific operation: Sends a request "GET / weather?location=specified location" and receives weather information in JSON format.

[1269] Step 6: Generative AI generates outfit suggestions

[1270] The server sends prompts to the generative AI model based on the acquired information to generate optimal outfit suggestions.

[1271] Input: User's past clothing data, latest trend information, weather information, schedule information

[1272] Output: Generated outfit suggestions

[1273] How it works: Using a Python script, the generative AI model is fed the following prompt: "Client meeting scheduled. Weather: sunny, temperature: 20 degrees. Past outfits: navy suit, gray suit. Owned: four suits, eight shirts, ten ties. Current trends: business casual, monochrome," and the resulting suggestions are then analyzed.

[1274] Step 7: Send the proposal to the device

[1275] The server sends the generated clothing suggestions to the user's terminal as an HTTP response.

[1276] Input: Generated outfit suggestions

[1277] Output: Response to the user's device

[1278] Specific operation: The proposal content is converted into JSON format and returned in the HTTP response.

[1279] Step 8: Notify users of the proposal

[1280] The terminal displays the proposal received from the server on the application screen.

[1281] Input: Response from the server (generated outfit suggestions)

[1282] Output: Display on the application screen

[1283] Specific operation: Analyze the received suggestion and display "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[1284] Step 9: Get user feedback

[1285] The user inputs their satisfaction and feedback about the suggested outfit.

[1286] Input: Satisfaction and comments about the proposal

[1287] Output: Feedback sent from the device to the server

[1288] Specific actions: Enter "I'm satisfied with the proposal" and "I would like to receive similar proposals in the future" in the feedback form and click the submit button.

[1289] Step 10: Record and learn from feedback

[1290] The server records user feedback in a database and uses it as learning data for the AI ​​model in future runs.

[1291] Input: User feedback

[1292] Output: Save to database and training data for AI model

[1293] Specific behavior: Execute the SQL query "INSERT INTO feedback (user_id, feedback_text) VALUES ('user_id', 'Satisfied with the suggestion')" to save the feedback.

[1294] The above is the specific processing flow of the system program.

[1295] (Application example 1)

[1296] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1297] In conventional clothing suggestion systems, even if a user inputs information such as their schedule, weather, temperature, and preferred style, the suggested outfits may not necessarily suit the user's preferences or the situation of the day. Furthermore, suggesting outfits in physical stores places a heavy burden on store staff, making it difficult to make efficient suggestions. Furthermore, the system lacks the functionality to prevent users from wearing the same outfit when meeting a specific person from the past, and the feedback functionality to improve the accuracy of suggestions.

[1298] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1299] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving past clothing history and clothing data from a database; a means for retrieving fashion and trend information; a means for retrieving weather forecast information; a generation AI means for generating clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; a means for displaying the suggestions using a smart mirror; and a means for recording user feedback and reflecting it in future suggestions. This allows users to receive optimal clothing suggestions through a smart mirror installed in a physical store, thereby reducing the burden on store staff and enabling efficient clothing suggestions. Furthermore, by utilizing past history and feedback, the accuracy of suggestions can be improved, increasing user satisfaction.

[1300] "Means for users to input information such as schedules, weather, temperature, and preferred styles" refers to an interface that allows users to input information about the day's schedule, current weather, temperature, and preferred fashion style.

[1301] "Means for retrieving data on past clothing history and owned clothing from a database" is a function for retrieving data on the clothing history that a user has worn and the clothing in their closet from a database.

[1302] "Means for obtaining fashion information and trend information" refers to a function for obtaining the latest fashion information and trend information from sources on the Internet.

[1303] "Means for obtaining weather forecast information" refers to a function for obtaining current and future weather information from a weather forecast API, etc.

[1304] The "generative AI means for generating clothing suggestions based on acquired information" is an artificial intelligence model that comprehensively analyzes information entered by the user, past clothing history, trend information, weather information, etc., to generate optimal clothing suggestions.

[1305] "Means for sending generated suggestions to the user's device" refers to a function for notifying the user of clothing suggestions created by the generation AI on their device, such as a smartphone or tablet.

[1306] The "means for displaying suggestions using a smart mirror" is a function for displaying the generated clothing suggestions on a smart mirror installed in a physical store.

[1307] "Means for recording user feedback and reflecting it in the next proposal" is a function for collecting the evaluations and opinions given by users on the proposals and using them in the next clothing proposal.

[1308] This invention is a system that allows users to choose clothes more efficiently and effectively, and is implemented based on the following procedures and configuration: In this system, a server generates optimal outfit suggestions based on user input information and displays them to the user through a smart mirror.

[1309] System configuration

[1310] The system consists of a terminal where users input information, a server that processes the data, and a smart mirror that displays the output, allowing the system to suggest the most suitable outfit for the user.

[1311] Hardware and software used

[1312] Hardware

[1313] Smart Mirror

[1314] server

[1315] software

[1316] Weather Forecast API

[1317] Trend information acquisition API

[1318] Generative AI models (e.g., OpenAI GPT, fashion-specific generative models)

[1319] Implementation procedures and processing details

[1320] Users can stand in front of a smart mirror installed in a store and use voice or touch input to input information about their plans for the day, the weather, the temperature, and their preferred style. This information is collected by the smart mirror and sent to the server.

[1321] The server processes the data using the following means:

[1322] 1. User information input processing: Information such as schedule, weather, temperature, and style is acquired from the smart mirror. This information is sent to the server and analyzed.

[1323] Example: Say "I have a business meeting today" and input that the weather is sunny and the temperature is 20 degrees.

[1324] Example prompt: "What is the best outfit to wear for a business meeting on a sunny 20 degree day?"

[1325] 2. Database reference process: The server accesses the database to retrieve the user's clothing history and the clothing they own. This allows the server to make optimal recommendations while preventing duplication of past clothing.

[1326] 3. Trend information acquisition: The server acquires information from fashion sites and trends on the Internet through the API. This is an important element in suggesting the latest trends and stylish styles to users.

[1327] 4. Weather information acquisition process: The latest weather information is acquired using a weather forecast API. This information, along with the user's schedule for the day and preferred style, is input into the generation AI.

[1328] 5. Proposal generation process using generative AI: Based on the acquired data, a generative AI model generates optimal clothing suggestions. For example, OpenAI GPT or a fashion-specific generative model can be used to suggest specific outfits that the user should wear.

[1329] 6. Display of suggested outfits: The generated outfit suggestions are displayed on the smart mirror, allowing the user to see the specific outfit image.

[1330] 7. Feedback acquisition process: The user inputs their satisfaction with the clothing suggestions and any additional requests they may have. This feedback is sent to the server and will be used for future suggestions. For example, possible feedback is "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[1331] 8. Learning process: The server records user feedback as learning data to improve the accuracy of the generative AI model, allowing it to make suggestions that are more tailored to the user's preferences and tendencies.

[1332] Specific examples

[1333] When a user stands in front of a smart mirror and inputs that they have a business meeting and that the weather is sunny and 20 degrees, the server prompts the generation AI based on past clothing history, trend information, and weather information. The generation AI generates a suggestion of "navy suit, white shirt, navy tie" and displays it on the smart mirror. If the user is satisfied with this suggestion, their feedback will be reflected in the next suggestion. In this way, the system can make clothing suggestions that suit the user and continuously improve its accuracy through feedback.

[1334] This system provides support to users to effectively select clothing, improving the efficiency and accuracy of clothing suggestions in physical stores.

[1335] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1336] Step 1:

[1337] The user stands in front of the smart mirror and inputs information such as their schedule for the day, the weather, the temperature, and their preferred style. This information is input using voice or the touch panel. This information is entered into the smart mirror and sent to the terminal as a single data package.

[1338] Input: Schedule, weather, temperature, preferred style

[1339] Output: User input data package

[1340] Step 2:

[1341] The terminal transmits the information entered by the user to the server, which receives the information and prepares it for analysis.

[1342] Input: User input data package

[1343] Output: Data sent to the server

[1344] Step 3:

[1345] The server accesses the database to retrieve the user's clothing history and the clothing they own, allowing them to check which clothing items they have used in the past and which they currently have on hand.

[1346] Input: User ID

[1347] Output: User's past clothing history data and clothing data

[1348] Step 4:

[1349] The server obtains the latest fashion and trend information through an external API, and uses the information as reference data to suggest optimal clothing.

[1350] Input: Fashion information API request

[1351] Output: Latest fashion and trend information

[1352] Step 5:

[1353] The server uses a weather forecast API to obtain current weather and temperature information, which is necessary to suggest comfortable clothing for the user.

[1354] Input: Weather API request

[1355] Output: Weather forecast information

[1356] Step 6:

[1357] The server uses a generative AI model to generate outfit suggestions based on the user's past clothing history data, clothing data, the latest fashion and trend information, weather forecast information, and user input data. For example, a prompt such as "What is the best outfit to wear for a business meeting on a sunny 20-degree day?" is input to the generative AI model.

[1358] Input: User's past clothing history data, clothing data, fashion information, trend information, weather forecast information, user input data, prompt text

[1359] Output: Generated outfit suggestions

[1360] Step 7:

[1361] The generated outfit suggestions are sent from the server to the smart mirror, which displays the suggested outfits on its screen for the user to visually confirm.

[1362] Input: Generated outfit suggestions

[1363] Output: Outfit suggestions displayed on a smart mirror

[1364] Step 8:

[1365] Users can input their satisfaction and feedback about the suggested outfits through the smart mirror, which will then be sent to the server and reflected in the next outfit suggestions.

[1366] Input: User feedback

[1367] Output: Feedback data sent to the server

[1368] Step 9:

[1369] The server records the collected feedback in a database and uses it as training data for the generative AI model, thereby improving the accuracy of future suggestions.

[1370] Input: User feedback data

[1371] Output: An updated generative AI model

[1372] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1373] This invention is a system for assisting users in choosing clothing, and in particular, it recognizes the user's emotions and suggests appropriate clothing based on those emotions. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc., and also combining it with an emotion engine that recognizes the user's emotions.

[1374] ---

[1375] System Overview

[1376] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. This system integrates the user's input information, the database on the server, generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits.

[1377] ---

[1378] Specific Embodiments of the System

[1379] 1. User input

[1380] The user enters the following information using a device such as a smartphone or tablet.

[1381] Today's Schedule

[1382] Weather information

[1383] temperature

[1384] Your preferred style

[1385] The user's current sentiment

[1386] Example: User types, "I have a client meeting today. The weather is sunny, the temperature is 20 degrees, and I'm feeling a bit nervous today."

[1387] ---

[1388] 2. Sending a request to the server

[1389] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[1390] ---

[1391] 3. Obtaining user history

[1392] The server accesses the database to obtain the user's clothing history and the clothing they own, thereby obtaining information to prevent the use of duplicate clothing.

[1393] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[1394] ---

[1395] 4. Obtaining trend information

[1396] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and style guidelines.

[1397] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[1398] ---

[1399] 5. Obtaining Weather Information

[1400] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[1401] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[1402] ---

[1403] 6. Emotion Recognition by Emotion Engine

[1404] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[1405] ---

[1406] 7. Generating clothing suggestions using generative AI

[1407] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[1408] ---

[1409] 8. Sending the proposal to the device

[1410] The server transmits the generated clothing suggestions to the user's terminal, specifically, the generated suggestions are transmitted to the terminal via a network.

[1411] ---

[1412] 9. User notification of proposals

[1413] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[1414] Example: The user confirms the displayed suggestions.

[1415] ---

[1416] 10. Getting User Feedback

[1417] The user checks the suggested outfits and inputs their satisfaction and feedback via the terminal. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[1418] ---

[1419] 11. Recording and learning feedback and emotion data

[1420] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, the server records data such as "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[1421] ---

[1422] Specific examples

[1423] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it to improve the next suggestion.

[1424] In this way, the system makes optimal suggestions based on the user's information and emotions, and improves accuracy through feedback.

[1425] The processing flow will be explained below.

[1426] Step 1:

[1427] A user accesses a device such as a smartphone or tablet and launches the application. The user inputs their plans for the day, the weather, the temperature, their preferred style, and their current emotions. Example: A user inputs, "Today I'm meeting with a client. The weather is sunny, the temperature is 20 degrees, and I'm feeling a little nervous today."

[1428] Step 2:

[1429] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[1430] Step 3:

[1431] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[1432] Step 4:

[1433] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[1434] Step 5:

[1435] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[1436] Step 6:

[1437] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[1438] Step 7:

[1439] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[1440] Step 8:

[1441] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[1442] Step 9:

[1443] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user. The user confirms the displayed proposal.

[1444] Step 10:

[1445] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[1446] Step 11:

[1447] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, it records that "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[1448] Example 2

[1449] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1450] Conventional clothing suggestion systems generate suggestions based on the user's schedule, weather, temperature, and preferred style, but do not take the user's emotions into account and are unable to generate different suggestions based on individual emotional changes.In addition, the system lacks a mechanism for incorporating user feedback into the generated suggestions, limiting the improvement of suggestion accuracy.

[1451] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1452] In this invention, the server includes a means for the user to input information such as schedule, weather, temperature, preferred style, and current emotion; a means for retrieving data on past clothing history and clothing owned from a database; and a means for analyzing the user's emotion data using an emotion engine. This allows for more appropriate clothing suggestions that take the user's emotions into consideration. Furthermore, by recording user feedback on generated suggestions and reflecting this in the next suggestions, the accuracy of the suggestions can be improved.

[1453] "Schedule" refers to the schedule or plan that the user will carry out that day.

[1454] "Weather" refers to the current weather and meteorological conditions.

[1455] "Temperature" refers to the temperature and temperature information for that day.

[1456] "Preferred style" refers to the clothing or fashion style that the user prefers.

[1457] "Emotions" refers to the user's current state of mind and emotional ups and downs.

[1458] "Past clothing history" refers to a record of clothing previously worn by the user.

[1459] "Data on clothes owned" refers to information about clothes and accessories owned by the user.

[1460] "Fashion information" refers to information about the latest fashions and styles.

[1461] "Trend information" refers to currently popular fashions and style guidelines.

[1462] "Weather forecast information" refers to information predicting future weather and temperature.

[1463] An "emotion engine" refers to a system that analyzes the emotion data entered by the user and recognizes their emotional state.

[1464] "Generative AI" refers to an artificial intelligence model that generates optimal clothing suggestions based on multiple pieces of information.

[1465] "User's terminal" refers to a computer device such as a smartphone or tablet used by a user.

[1466] "Feedback" refers to ratings and comments provided by users on suggested outfits.

[1467] This invention is a system for assisting users in choosing clothing, suggesting appropriate clothing based on information such as the user's schedule, weather, temperature, preferred style, current emotion, etc. In particular, this system is characterized by using an emotion engine to recognize the user's emotions and a generative AI model to suggest optimal clothing.

[1468] System configuration

[1469] The system includes a terminal where users can input information, a server that receives the information, and multiple engines and APIs that process and analyze the information.The database also records the user's past clothing history, clothing data, and user feedback.

[1470] Hardware and software used

[1471] 1. Terminal: A device that a user uses to input information, such as a smartphone or tablet.

[1472] 2. Server: Receives requests and processes information by interacting with databases and APIs.

[1473] 3. Database: A storage device for storing user history data and feedback.

[1474] 4. Emotion engine: Software for analyzing user emotion data.

[1475] 5. Generative AI model: Artificial intelligence that generates clothing suggestions based on user information.

[1476] 6. API: External services for obtaining trending and weather information.

[1477] Data processing and calculation

[1478] 1. Enter your information:

[1479] Users use a smartphone or tablet to input their "today's schedule," "weather," "temperature," "preferred style," and "current feelings" into the system.

[1480] 2. Submit your request:

[1481] The entered information is sent from the terminal to the server in JSON format or similar.

[1482] 3. Retrieving from the database:

[1483] The server retrieves data on past clothing history and clothing owned from the database, which helps to avoid duplication of previously used clothing.

[1484] 4. Get trend and weather information:

[1485] The server obtains the latest fashion information and weather forecasts through external APIs. Trend information includes currently popular fashion styles, and weather information includes current weather conditions and forecasts.

[1486] 5. Emotion engine analysis:

[1487] The server uses an emotion engine to analyze the emotion data entered by the user and determine the user's emotional state.

[1488] 6. Generating outfit suggestions:

[1489] The server uses a generative AI model based on all the information it has acquired so far (user's schedule, weather, temperature, preferred style, emotions, past clothing history, data on clothing owned, and trend information) to generate optimal clothing suggestions.

[1490] 7. Submission and Notification of Proposals:

[1491] The generated clothing suggestions are sent from the server to the user's terminal via the network, and the terminal notifies the user of the suggestions.

[1492] Specific examples

[1493] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user reviews this suggestion and provides feedback that they are satisfied. This feedback is recorded on the server and reflected in future suggestions.

[1494] Prompt Sentence Examples

[1495] "What is the best business casual attire for when you have a client meeting, it's sunny, it's 20 degrees, and you're a little nervous?"

[1496] In this way, the system makes optimal clothing suggestions based on the user's information and emotions, and incorporates feedback to improve the accuracy of the suggestions.

[1497] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1498] Step 1:

[1499] Users input information using devices such as smartphones and tablets. Specifically, they input information such as "today's schedule," "weather," "temperature," "preferred style," and "current emotion" into the application's input fields. This generates user input data.

[1500] Input: Today's schedule, weather, temperature, preferred style, current mood

[1501] Output: Input data (e.g., {Appointment: "Meet with a client", Weather: "Sunny", Temperature: 20, Style: "Business", Emotion: "Nervous"})

[1502] Step 2:

[1503] The terminal converts the information entered by the user into JSON format or similar and sends it to the server. Specifically, it sends the data to the server via an HTTP request, which allows the server to receive the data.

[1504] Input: Input data

[1505] Output: Request sent to server

[1506] Step 3:

[1507] The server analyzes the received data and accesses the database to retrieve the user's past clothing history and the clothes they own. It uses an SQL query to search the data and retrieves the results. This passes the user's past clothing history and a list of the clothes they currently own to the server.

[1508] Input: Received data

[1509] Output: User's past clothing history, clothing data

[1510] Step 4:

[1511] The server sends a request to an external API to get the latest fashion and trend information. For example, it sends a GET request to a fashion API to get trend information. This data is then processed on the server.

[1512] Input: API request

[1513] Output: Latest fashion and trend information

[1514] Step 5:

[1515] The server accesses the weather forecast API to obtain the latest weather information for the specified area. For example, it sends a GET request to the weather API and obtains weather and temperature information from the response. This data is stored on the server.

[1516] Input: API request

[1517] Output: Weather information, temperature information

[1518] Step 6:

[1519] The server uses an emotion engine to analyze the user's emotion data. Specifically, it analyzes the user's emotion input, such as "tension," to recognize the user's detailed emotional state. This information is used for further data processing.

[1520] Input: Emotion data

[1521] Output: Parsed emotional state

[1522] Step 7:

[1523] The server uses a generative AI model based on all the information it has acquired (user schedule, weather, temperature, preferred style, emotions, past clothing history, clothing data owned, and trend information) to generate optimal outfit suggestions. Text data combining this information is used as input prompts for the generative AI model.

[1524] Input: All acquired information

[1525] Output: Generated outfit suggestions

[1526] Step 8:

[1527] The server converts the generated clothing suggestions into JSON format and sends them to the user's device. The server then sends the suggestion data as an HTTP response over the network, allowing the user's device to receive the suggestions.

[1528] Input: Generated outfit suggestions

[1529] Output: Send the proposal to the device

[1530] Step 9:

[1531] The device notifies the user of the suggestions received from the server. Specifically, the suggestions are displayed as a pop-up or notification message within the application, allowing the user to confirm the suggestions.

[1532] Input: Receive proposal

[1533] Output: User notification of the proposal

[1534] Step 10:

[1535] The user reviews the proposed outfits and enters their satisfaction and feedback within the application, which generates evaluation comments.

[1536] Input: Confirm proposal

[1537] Output: Feedback data

[1538] Step 11:

[1539] The server records the feedback and emotion data provided by the user in a database and uses this data as training data to improve the accuracy of future suggestions.

[1540] Input: Feedback data, emotion data

[1541] Output: Update learning data, improve proposal accuracy

[1542] (Application example 2)

[1543] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1544] Conventional clothing suggestion systems do not take into account the user's emotions when making suggestions, making it difficult to suggest the best outfit for the user's mood and situation. Furthermore, they lacked connectivity with physical stores, making it impossible to purchase suggested outfits on the spot. Furthermore, they lacked the application of user feedback to improve the accuracy of suggestions.

[1545] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1546] In this invention, the server includes means for the user to input information such as schedule, weather, temperature, and preferred style, means for retrieving data on past clothing history and clothing owned from a database, means for retrieving fashion information and trend information, means for retrieving weather forecast information, means for recognizing the user's emotions in real time, generation AI means for generating outfit suggestions based on the retrieved information, means for displaying the suggested outfits as items available for purchase in a physical store, means for transmitting the generated suggestions to the user's terminal, and means for recording user feedback and reflecting it in the next suggestion. This makes it possible to take the user's emotions into consideration, facilitate purchases in a physical store, and improve the accuracy of suggestions based on feedback.

[1547] "Schedule" is information indicating the plans and activities that the user will undertake on that day.

[1548] "Weather" is information that indicates the weather conditions and meteorological conditions for that day.

[1549] "Temperature" is information indicating the temperature of the atmosphere at a specific time.

[1550] "Preferred style" is information that indicates the user's preferred fashion and clothing trends.

[1551] "Past clothing history" is a record of clothing worn by the user in the past.

[1552] "Data on clothes owned" is information on the types and quantities of clothes owned by the user.

[1553] A "database" is a collection of data and a system for efficiently managing information.

[1554] "Fashion information" is information about the latest clothing and accessories.

[1555] "Trend information" is information about currently popular fashions and styles.

[1556] "Weather forecast information" is information that indicates a prediction of future weather.

[1557] "Means for recognizing a user's emotions in real time" refers to technology for instantly analyzing a user's current emotions.

[1558] "Generative AI means" refers to artificial intelligence technology for generating clothing suggestions based on acquired information.

[1559] "In-store purchaseable items" are products that can be purchased immediately in a physical store.

[1560] The "means for transmitting the generated suggestions to the user's terminal" is a technique for sending the outfit suggestions to the device used by the user.

[1561] "User feedback" is information indicating evaluations and opinions from users.

[1562] "Means to reflect in the next proposal" refers to techniques for incorporating user feedback into the next proposal.

[1563] System Configuration

[1564] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and a server then suggests appropriate outfits based on that information. The system integrates the user's input information, a database on the server, a generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits. It also has a function to support purchasing in physical stores.

[1565] Hardware and Software

[1566] Hardware:

[1567] Smartphone / Tablet: A device for entering user information and using the emotion recognition camera.

[1568] Server: A system for processing data and hosting APIs.

[1569] software:

[1570] OpenCV: A library for face detection and image processing.

[1571] Keras: A deep learning framework used for loading and inferencing emotion recognition models.

[1572] Requests: A library for making requests to external APIs (weather API, fashion API).

[1573] System action

[1574] The server performs the following data processing and calculations: The user uses their device to input information such as their schedule, weather, temperature, and preferred style, and sends that information to the server. The server retrieves the user's past clothing history and clothing data from a database, and also obtains external fashion information, trend information, and weather forecast information via an API. Next, it analyzes the user's current emotions using an emotion engine that recognizes the user's emotions in real time. A generation AI generates outfit suggestions based on the acquired information and sends these suggestions to the user's device. It also displays items that can be purchased in physical stores based on the suggested outfits to assist with purchasing. User feedback is collected and reflected in future suggestions, improving the accuracy of the suggestions.

[1575] Specific examples

[1576] One day, a user enters a physical store and types "I'm in a fun party mood today" into their device. The system uses an emotion engine to analyze the user's emotions and retrieves past clothing history, clothing owned, weather information, and fashion information from the database. Based on this information, the generation AI suggests the most suitable outfit, and the suggestion is sent to the user's device. The suggested outfit is then displayed as an item available for purchase in the physical store. The user purchases the outfit based on the suggestion and provides feedback on their satisfaction. The server records this feedback and reflects it in the next suggestion.

[1577] A specific example of a prompt is as follows:

[1578] "Today's suggestion: I'm going to a party and I'd like to know what items I can buy at a nearby store. I'm in a fun mood."

[1579] The invention is a system that helps users easily choose the best outfit for their mood and situation, and also helps them make purchases in physical stores smoothly. This system is expected to increase user satisfaction and improve the shopping experience in stores.

[1580] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1581] Step 1:

[1582] The user uses the device to input information such as schedule, weather, temperature, preferred style, and emotions.

[1583] Input: User's schedule, weather, temperature, preferred style, emotions

[1584] Output: User information entered into the terminal

[1585] Specifically, the user inputs "I'm going to a party today and I'm in a good mood," which is then saved on the device.

[1586] Step 2:

[1587] The terminal transmits the input information to the server.

[1588] Input: User information stored on the device

[1589] Output: User information sent to the server

[1590] Specifically, the terminal transmits information such as "I'm going to a party today and I'm in a good mood" to the server via the network.

[1591] Step 3:

[1592] The server retrieves the user's past clothing history and data on the clothes they own from the database.

[1593] Input: User ID

[1594] Output: User's past clothing history and clothing data

[1595] Specifically, the server accesses the database and retrieves data such as "navy suit, white shirt, navy tie."

[1596] Step 4:

[1597] The server obtains fashion and trend information from the Internet via an API.

[1598] Input: API request

[1599] Output: Latest fashion and trend information

[1600] Specifically, the server sends a request to the fashion API and obtains information such as "business casual and monochrome are in fashion."

[1601] Step 5:

[1602] The server obtains weather information using the weather forecast API.

[1603] Input: Weather API request

[1604] Output: Weather information (sunny, temperature 20 degrees)

[1605] Specifically, the server accesses the weather forecast API and obtains information such as "The weather is sunny, with a maximum temperature of 20 degrees."

[1606] Step 6:

[1607] The server uses an emotion engine to recognize emotion information input by the user.

[1608] Input: User emotion data

[1609] Output: Perceived emotion (happy)

[1610] Specifically, the server inputs "feeling happy" into the emotion engine and analyzes it.

[1611] Step 7:

[1612] Based on the information acquired by the server, clothing suggestions are generated using a generation AI.

[1613] Input: Past clothing history, fashion information, weather information, schedule, emotions

[1614] Output: Generated outfit suggestions

[1615] Specifically, the generative AI generates suggestions such as "navy suit, white shirt, navy tie."

[1616] Step 8:

[1617] The server transmits the generated clothing suggestions to the terminal.

[1618] Input: Generated outfit suggestions

[1619] Output: Outfit suggestions sent to the device

[1620] Specifically, the server sends the suggestion to the terminal via the network, and "Today's suggestion: navy suit, white shirt, navy tie" is displayed.

[1621] Step 9:

[1622] The suggested outfits are displayed as items available for purchase in physical stores.

[1623] Input: Generated outfit suggestions

[1624] Output: A list of items available for purchase

[1625] Specifically, the terminal displays a list of products available for purchase at store A.

[1626] Step 10:

[1627] The user reviews the suggested outfits and enters feedback.

[1628] Input: User feedback

[1629] Output: Feedback data

[1630] Specifically, the user inputs feedback such as "I'm satisfied with this suggestion" and saves it on the device.

[1631] Step 11:

[1632] The server records the user's feedback and reflects it in future suggestions.

[1633] Input: Feedback data

[1634] Output: Updated user database

[1635] Specifically, the server records data such as "the combination of navy suit + white shirt + navy tie is popular" and uses it for the next proposal.

[1636] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1637] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1638] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1639] [Fourth embodiment]

[1640] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1641] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1642] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1643] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1644] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1645] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1646] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1647] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1648] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1649] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1650] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1651] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1652] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1653] The present invention is a system for assisting a user in selecting clothing, and is implemented through the following steps. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc. Specific embodiments of the system are described in detail below.

[1654] ---

[1655] System Overview

[1656] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. The system integrates the user's input information, the database on the server, generation AI, trend information, and weather information to suggest optimal outfits.

[1657] ---

[1658] Specific Embodiments of the System

[1659] 1. User input

[1660] The user enters the following information using a device such as a smartphone or tablet.

[1661] Today's Schedule

[1662] Weather information

[1663] temperature

[1664] Your preferred style

[1665] Example: User types, "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[1666] ---

[1667] 2. Sending a request to the server

[1668] The device sends the information the user has entered, including schedule information, weather, temperature, and style preferences, to a server.

[1669] ---

[1670] 3. Obtaining user history

[1671] The server accesses the database to obtain the user's clothing history and the clothing they own, which allows them to check which clothes they have worn at other events and to obtain information to prevent duplication.

[1672] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[1673] ---

[1674] 4. Obtaining trend information

[1675] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and fashionable styles.

[1676] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[1677] ---

[1678] 5. Obtaining Weather Information

[1679] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[1680] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[1681] ---

[1682] 6. Generating clothing suggestions using generative AI

[1683] The server uses a generation AI to generate optimal clothing suggestions based on the information it has acquired (the user's past clothing data, trend information, weather information, and schedule).

[1684] For example, suggest a navy suit, white shirt, and navy tie.

[1685] ---

[1686] 7. Sending the proposal to the device

[1687] The server transmits the generated clothing suggestions to the user's terminal.

[1688] ---

[1689] 8. User notification of proposals

[1690] The terminal notifies the user of the proposal received from the server.

[1691] Example: Show the user "Today's suggestion: navy suit with a white shirt and a navy tie."

[1692] ---

[1693] 9. Getting User Feedback

[1694] The user checks the proposed outfits and inputs their satisfaction and feedback via the terminal, including their rating of the suggestions and requests for additions.

[1695] Example: A user gives feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[1696] ---

[1697] 10. Record and learn from feedback

[1698] The server records feedback from users and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user.

[1699] Example: Record that "The combination of navy suit + white shirt + navy tie was well received" and reflect this in future proposals.

[1700] ---

[1701] Specific examples

[1702] One day, a user enters that they have a client meeting scheduled, the weather is sunny, and the temperature is 20 degrees. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it for the next suggestion.

[1703] In this way, the system makes optimal suggestions based on user information and improves accuracy through feedback.

[1704] The processing flow will be explained below.

[1705] Step 1:

[1706] A user accesses a device such as a smartphone or tablet and launches an application. The user enters information such as today's schedule, weather, temperature, and preferred style. Example: A user enters, "Today I'm meeting with a client. The weather is sunny and the temperature is 20 degrees."

[1707] Step 2:

[1708] The terminal sends the information entered by the user to the server. Specifically, it sends the data "Today's plan = meet with client," "Weather = sunny," and "Temperature = 20 degrees" to the server.

[1709] Step 3:

[1710] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[1711] Step 4:

[1712] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[1713] Step 5:

[1714] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[1715] Step 6:

[1716] The server uses the AI ​​to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule). Example: The server suggests "navy suit, white shirt, and navy tie."

[1717] Step 7:

[1718] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[1719] Step 8:

[1720] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[1721] Step 9:

[1722] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[1723] Step 10:

[1724] The server records user feedback and uses it as learning data for the AI ​​so that future suggestions will be more suitable for the user. Specifically, it records that "the combination of navy suit + white shirt + navy tie was well received" and reflects this in future suggestions.

[1725] Example 1

[1726] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1727] Conventional clothing selection support systems require users to take the time and effort to choose their outfits each time, and it is difficult to suggest outfits that take into account past clothing history and the latest trends. In addition, they lacked the functionality to prevent users from wearing the same outfit when meeting a specific person again, and the functionality to utilize user feedback to improve the accuracy of suggestions. Furthermore, it was difficult to incorporate changing weather information in real time.

[1728] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1729] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving data on past clothing history and clothing owned from a database; a means for retrieving external fashion and trend information; a means for retrieving weather forecast information; a means including a generative AI model that generates clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; and a means for recording user feedback and reflecting it in next suggestions. This allows the user to efficiently select optimal clothing and receive suggestions that take past history, the latest trends, and weather information into consideration. Furthermore, the server can avoid wearing the same clothes when meeting a specific person again, and can improve the accuracy of suggestions by utilizing feedback.

[1730] "User" refers to an individual who uses the system to receive clothing suggestions.

[1731] "Terminal" means a device through which a User inputs information and receives suggestions, including a smartphone, tablet, or PC.

[1732] "Server" refers to the central computer system that receives information from users, accesses various databases and APIs to generate outfit suggestions, and sends them to the device.

[1733] "Information input means" refers to an interface that allows a user to input information such as schedules, weather, temperature, and preferred style into the terminal.

[1734] "Clothing history acquisition means" refers to a function for acquiring the user's past clothing history and data on clothing owned by the user from a database.

[1735] "Means for obtaining fashion information" refers to the function for obtaining information from external fashion sites and trends via API.

[1736] "Means for obtaining weather information" refers to the function for obtaining the latest weather information using the weather forecast API.

[1737] "Generative AI model" refers to an artificial intelligence algorithm that generates optimal clothing suggestions based on acquired information.

[1738] "Terminal transmission means" refers to a function that transmits the generated clothing suggestions to the user's terminal.

[1739] "Feedback recording means" refers to a function for recording feedback from users and reflecting that information in the next proposal.

[1740] The present invention is a system for assisting a user in selecting clothes, and has the following configuration.

[1741] System Overview

[1742] This system consists of a series of processes in which the user inputs information through a terminal, and the server then suggests appropriate clothing based on that information. Specific hardware used as the terminal is a smartphone or tablet for inputting user information. The server uses a virtual server on the cloud or a dedicated data center.

[1743] Hardware and software used

[1744] Devices: Smartphones, tablets, PCs

[1745] Server: Cloud server, database server

[1746] Software: API for HTTP requests, generative AI models (Python and machine learning frameworks such as TensorFlow)

[1747] Specific form of implementation

[1748] User input

[1749] A user uses a smartphone or tablet application to input information such as schedules, weather information, temperature, preferred style, etc. For example, a user inputs information such as "I'm meeting with a client today. The weather is sunny and the temperature is 20 degrees."

[1750] Sending a request to the server

[1751] The device converts the user's input information into JSON format and sends it to the server via an HTTP request. Specifically, it sends a POST request to the API endpoint.

[1752] Retrieving user history

[1753] After receiving the request, the server accesses the database to retrieve data on the user's clothing history and the clothes they own. Specifically, it retrieves the data using SQL queries.

[1754] Obtaining trend information

[1755] The server obtains the latest information from external fashion sites and trend information services, using a RESTful API to understand the latest fashion trends and fashion trends.

[1756] Get weather information

[1757] The server retrieves the latest weather information from the weather forecast service, specifically, the current temperature and weather using the weather API.

[1758] Generative AI generates clothing suggestions

[1759] The server then sends prompts to the generative AI model based on the information it has acquired to generate optimal outfit suggestions, for example using prompts like the following:

[1760] To help users choose what to wear, please suggest the best outfit based on the following information:

[1761] Upcoming: Client Meeting

[1762] Weather: Sunny

[1763] Temperature: 20°C

[1764] User history: Navy suit, gray suit

[1765] Clothes I own: 4 suits, 8 shirts, 10 ties

[1766] Latest trends: business casual, monochrome

[1767] Once the generative AI model generates a proposal, the server receives it.

[1768] Sending the proposal to the device

[1769] The server sends the generated outfit suggestions to the user's device as an HTTP response. Specifically, the suggestion content is included in the POST request response in JSON format.

[1770] User notification of proposals

[1771] The terminal displays the suggestions received from the server on the application screen, for example, notifying the user of "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[1772] Get user feedback

[1773] The user inputs their satisfaction level and feedback about the suggested outfits. Specifically, they enter feedback such as "I'm satisfied with the suggestion" or "I would like to receive similar suggestions in the future" into the application form and click the submit button.

[1774] Record and learn from feedback

[1775] The server records the user feedback in a database and uses it as training data for the AI ​​model in future. The server saves the feedback in the database using an SQL query.

[1776] As a result, the present invention can provide efficient and personalized clothing suggestions, thereby increasing user satisfaction.

[1777] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1778] System program processing flow

[1779] Step 1: Enter your information

[1780] Users enter the following information using an application on their smartphone or tablet:

[1781] Input: Schedule, weather information, temperature, preferred style

[1782] Specific actions: Enter the information "Client meeting. Weather is sunny, temperature is 20 degrees" into the application's input form and press the send button.

[1783] Step 2: Submitting a request

[1784] The terminal converts the information entered by the user into JSON format and sends it to the server via an HTTP request.

[1785] Input: User input information

[1786] Output: JSON format data to send to the server

[1787] Specific behavior: Serializes the input information into JSON and sends a POST request to the API endpoint.

[1788] Step 3: Retrieving User History

[1789] After receiving the request, the server accesses the database to retrieve data on the user's past clothing history and the clothes they own.

[1790] Input: User ID

[1791] Output: Past clothing history and clothing owned data

[1792] Specific operation: Execute the SQL query "SELECT FROM user_clothing_history WHERE user_id = 'User ID'" and retrieve the results.

[1793] Step 4: Obtaining trend information

[1794] The server obtains the latest information from external fashion sites and trend information services.

[1795] Input: Fashion Information API endpoint

[1796] Output: Trend information

[1797] Specific behavior: Uses the RESTful API to send a "GET / fashion-trends" request and receives trend information in JSON format.

[1798] Step 5: Get Weather Information

[1799] The server retrieves the latest weather information from a weather forecast service.

[1800] Input: Weather API endpoint and specified location

[1801] Output: Weather information (current temperature and weather)

[1802] Specific operation: Sends a request "GET / weather?location=specified location" and receives weather information in JSON format.

[1803] Step 6: Generative AI generates outfit suggestions

[1804] The server sends prompts to the generative AI model based on the acquired information to generate optimal outfit suggestions.

[1805] Input: User's past clothing data, latest trend information, weather information, schedule information

[1806] Output: Generated outfit suggestions

[1807] How it works: Using a Python script, the generative AI model is fed the following prompt: "Client meeting scheduled. Weather: sunny, temperature: 20 degrees. Past outfits: navy suit, gray suit. Owned: four suits, eight shirts, ten ties. Current trends: business casual, monochrome," and the resulting suggestions are then analyzed.

[1808] Step 7: Send the proposal to the device

[1809] The server sends the generated clothing suggestions to the user's terminal as an HTTP response.

[1810] Input: Generated outfit suggestions

[1811] Output: Response to the user's device

[1812] Specific operation: The proposal content is converted into JSON format and returned in the HTTP response.

[1813] Step 8: Notify users of the proposal

[1814] The terminal displays the proposal received from the server on the application screen.

[1815] Input: Response from the server (generated outfit suggestions)

[1816] Output: Display on the application screen

[1817] Specific operation: Analyze the received suggestion and display "Today's outfit suggestion: navy suit, white shirt, and navy tie."

[1818] Step 9: Get user feedback

[1819] The user inputs their satisfaction and feedback about the suggested outfit.

[1820] Input: Satisfaction and comments about the proposal

[1821] Output: Feedback sent from the device to the server

[1822] Specific actions: Enter "I'm satisfied with the proposal" and "I would like to receive similar proposals in the future" in the feedback form and click the submit button.

[1823] Step 10: Record and learn from feedback

[1824] The server records user feedback in a database and uses it as learning data for the AI ​​model in future runs.

[1825] Input: User feedback

[1826] Output: Save to database and training data for AI model

[1827] Specific behavior: Execute the SQL query "INSERT INTO feedback (user_id, feedback_text) VALUES ('user_id', 'Satisfied with the suggestion')" to save the feedback.

[1828] The above is the specific processing flow of the system program.

[1829] (Application example 1)

[1830] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1831] In conventional clothing suggestion systems, even if a user inputs information such as their schedule, weather, temperature, and preferred style, the suggested outfits may not necessarily suit the user's preferences or the situation of the day. Furthermore, suggesting outfits in physical stores places a heavy burden on store staff, making it difficult to make efficient suggestions. Furthermore, the system lacks the functionality to prevent users from wearing the same outfit when meeting a specific person from the past, and the feedback functionality to improve the accuracy of suggestions.

[1832] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1833] In this invention, the server includes: a means for a user to input information such as schedule, weather, temperature, and preferred style; a means for retrieving past clothing history and clothing data from a database; a means for retrieving fashion and trend information; a means for retrieving weather forecast information; a generation AI means for generating clothing suggestions based on the retrieved information; a means for transmitting the generated suggestions to the user's device; a means for displaying the suggestions using a smart mirror; and a means for recording user feedback and reflecting it in future suggestions. This allows users to receive optimal clothing suggestions through a smart mirror installed in a physical store, thereby reducing the burden on store staff and enabling efficient clothing suggestions. Furthermore, by utilizing past history and feedback, the accuracy of suggestions can be improved, increasing user satisfaction.

[1834] "Means for users to input information such as schedules, weather, temperature, and preferred styles" refers to an interface that allows users to input information about the day's schedule, current weather, temperature, and preferred fashion style.

[1835] "Means for retrieving data on past clothing history and owned clothing from a database" is a function for retrieving data on the clothing history that a user has worn and the clothing in their closet from a database.

[1836] "Means for obtaining fashion information and trend information" refers to a function for obtaining the latest fashion information and trend information from sources on the Internet.

[1837] "Means for obtaining weather forecast information" refers to a function for obtaining current and future weather information from a weather forecast API, etc.

[1838] The "generative AI means for generating clothing suggestions based on acquired information" is an artificial intelligence model that comprehensively analyzes information entered by the user, past clothing history, trend information, weather information, etc., to generate optimal clothing suggestions.

[1839] "Means for sending generated suggestions to the user's device" refers to a function for notifying the user of clothing suggestions created by the generation AI on their device, such as a smartphone or tablet.

[1840] The "means for displaying suggestions using a smart mirror" is a function for displaying the generated clothing suggestions on a smart mirror installed in a physical store.

[1841] "Means for recording user feedback and reflecting it in the next proposal" is a function for collecting the evaluations and opinions given by users on the proposals and using them in the next clothing proposal.

[1842] This invention is a system that allows users to choose clothes more efficiently and effectively, and is implemented based on the following procedures and configuration: In this system, a server generates optimal outfit suggestions based on user input information and displays them to the user through a smart mirror.

[1843] System configuration

[1844] The system consists of a terminal where users input information, a server that processes the data, and a smart mirror that displays the output, allowing the system to suggest the most suitable outfit for the user.

[1845] Hardware and software used

[1846] Hardware

[1847] Smart Mirror

[1848] server

[1849] software

[1850] Weather Forecast API

[1851] Trend information acquisition API

[1852] Generative AI models (e.g., OpenAI GPT, fashion-specific generative models)

[1853] Implementation procedures and processing details

[1854] Users can stand in front of a smart mirror installed in a store and use voice or touch input to input information about their plans for the day, the weather, the temperature, and their preferred style. This information is collected by the smart mirror and sent to the server.

[1855] The server processes the data using the following means:

[1856] 1. User information input processing: Information such as schedule, weather, temperature, and style is acquired from the smart mirror. This information is sent to the server and analyzed.

[1857] Example: Say "I have a business meeting today" and input that the weather is sunny and the temperature is 20 degrees.

[1858] Example prompt: "What is the best outfit to wear for a business meeting on a sunny 20 degree day?"

[1859] 2. Database reference process: The server accesses the database to retrieve the user's clothing history and the clothing they own. This allows the server to make optimal recommendations while preventing duplication of past clothing.

[1860] 3. Trend information acquisition: The server acquires information from fashion sites and trends on the Internet through the API. This is an important element in suggesting the latest trends and stylish styles to users.

[1861] 4. Weather information acquisition process: The latest weather information is acquired using a weather forecast API. This information, along with the user's schedule for the day and preferred style, is input into the generation AI.

[1862] 5. Proposal generation process using generative AI: Based on the acquired data, a generative AI model generates optimal clothing suggestions. For example, OpenAI GPT or a fashion-specific generative model can be used to suggest specific outfits that the user should wear.

[1863] 6. Display of suggested outfits: The generated outfit suggestions are displayed on the smart mirror, allowing the user to see the specific outfit image.

[1864] 7. Feedback acquisition process: The user inputs their satisfaction with the clothing suggestions and any additional requests they may have. This feedback is sent to the server and will be used for future suggestions. For example, possible feedback is "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[1865] 8. Learning process: The server records user feedback as learning data to improve the accuracy of the generative AI model, allowing it to make suggestions that are more tailored to the user's preferences and tendencies.

[1866] Specific examples

[1867] When a user stands in front of a smart mirror and inputs that they have a business meeting and that the weather is sunny and 20 degrees, the server prompts the generation AI based on past clothing history, trend information, and weather information. The generation AI generates a suggestion of "navy suit, white shirt, navy tie" and displays it on the smart mirror. If the user is satisfied with this suggestion, their feedback will be reflected in the next suggestion. In this way, the system can make clothing suggestions that suit the user and continuously improve its accuracy through feedback.

[1868] This system provides support to users to effectively select clothing, improving the efficiency and accuracy of clothing suggestions in physical stores.

[1869] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1870] Step 1:

[1871] The user stands in front of the smart mirror and inputs information such as their schedule for the day, the weather, the temperature, and their preferred style. This information is input using voice or the touch panel. This information is entered into the smart mirror and sent to the terminal as a single data package.

[1872] Input: Schedule, weather, temperature, preferred style

[1873] Output: User input data package

[1874] Step 2:

[1875] The terminal transmits the information entered by the user to the server, which receives the information and prepares it for analysis.

[1876] Input: User input data package

[1877] Output: Data sent to the server

[1878] Step 3:

[1879] The server accesses the database to retrieve the user's clothing history and the clothing they own, allowing them to check which clothing items they have used in the past and which they currently have on hand.

[1880] Input: User ID

[1881] Output: User's past clothing history data and clothing data

[1882] Step 4:

[1883] The server obtains the latest fashion and trend information through an external API, and uses the information as reference data to suggest optimal clothing.

[1884] Input: Fashion information API request

[1885] Output: Latest fashion and trend information

[1886] Step 5:

[1887] The server uses a weather forecast API to obtain current weather and temperature information, which is necessary to suggest comfortable clothing for the user.

[1888] Input: Weather API request

[1889] Output: Weather forecast information

[1890] Step 6:

[1891] The server uses a generative AI model to generate outfit suggestions based on the user's past clothing history data, clothing data, the latest fashion and trend information, weather forecast information, and user input data. For example, a prompt such as "What is the best outfit to wear for a business meeting on a sunny 20-degree day?" is input to the generative AI model.

[1892] Input: User's past clothing history data, clothing data, fashion information, trend information, weather forecast information, user input data, prompt text

[1893] Output: Generated outfit suggestions

[1894] Step 7:

[1895] The generated outfit suggestions are sent from the server to the smart mirror, which displays the suggested outfits on its screen for the user to visually confirm.

[1896] Input: Generated outfit suggestions

[1897] Output: Outfit suggestions displayed on a smart mirror

[1898] Step 8:

[1899] Users can input their satisfaction and feedback about the suggested outfits through the smart mirror, which will then be sent to the server and reflected in the next outfit suggestions.

[1900] Input: User feedback

[1901] Output: Feedback data sent to the server

[1902] Step 9:

[1903] The server records the collected feedback in a database and uses it as training data for the generative AI model, thereby improving the accuracy of future suggestions.

[1904] Input: User feedback data

[1905] Output: An updated generative AI model

[1906] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1907] This invention is a system for assisting users in choosing clothing, and in particular, it recognizes the user's emotions and suggests appropriate clothing based on those emotions. This system suggests optimal clothing based on information input by the user, taking into consideration past clothing history, trend information, weather information, etc., and also combining it with an emotion engine that recognizes the user's emotions.

[1908] ---

[1909] System Overview

[1910] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and the server then suggests appropriate outfits based on that information. This system integrates the user's input information, the database on the server, generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits.

[1911] ---

[1912] Specific Embodiments of the System

[1913] 1. User input

[1914] The user enters the following information using a device such as a smartphone or tablet.

[1915] Today's Schedule

[1916] Weather information

[1917] temperature

[1918] Your preferred style

[1919] The user's current sentiment

[1920] Example: User types, "I have a client meeting today. The weather is sunny, the temperature is 20 degrees, and I'm feeling a bit nervous today."

[1921] ---

[1922] 2. Sending a request to the server

[1923] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[1924] ---

[1925] 3. Obtaining user history

[1926] The server accesses the database to obtain the user's clothing history and the clothing they own, thereby obtaining information to prevent the use of duplicate clothing.

[1927] Example: Obtain information such as "Past clothing history: navy suit, gray suit" and "Clothes owned: 4 suits, 8 shirts, 10 ties."

[1928] ---

[1929] 4. Obtaining trend information

[1930] The server accesses online fashion sites and trend information via API, allowing it to understand the latest trends and style guidelines.

[1931] Example: Obtain information that "Current trends: business casual, monochrome" are in fashion.

[1932] ---

[1933] 5. Obtaining Weather Information

[1934] The server uses a weather API to retrieve the latest weather information, including the current temperature, forecast, wind speed, etc.

[1935] Example: Get "Weather information: sunny, maximum temperature 20 degrees."

[1936] ---

[1937] 6. Emotion Recognition by Emotion Engine

[1938] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[1939] ---

[1940] 7. Generating clothing suggestions using generative AI

[1941] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[1942] ---

[1943] 8. Sending the proposal to the device

[1944] The server transmits the generated clothing suggestions to the user's terminal, specifically, the generated suggestions are transmitted to the terminal via a network.

[1945] ---

[1946] 9. User notification of proposals

[1947] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user.

[1948] Example: The user confirms the displayed suggestions.

[1949] ---

[1950] 10. Getting User Feedback

[1951] The user checks the suggested outfits and inputs their satisfaction and feedback via the terminal. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I hope to receive similar suggestions in the future."

[1952] ---

[1953] 11. Recording and learning feedback and emotion data

[1954] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, the server records data such as "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[1955] ---

[1956] Specific examples

[1957] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user provides feedback that they are satisfied with this suggestion, and the server records this feedback and uses it to improve the next suggestion.

[1958] In this way, the system makes optimal suggestions based on the user's information and emotions, and improves accuracy through feedback.

[1959] The processing flow will be explained below.

[1960] Step 1:

[1961] A user accesses a device such as a smartphone or tablet and launches the application. The user inputs their plans for the day, the weather, the temperature, their preferred style, and their current emotions. Example: A user inputs, "Today I'm meeting with a client. The weather is sunny, the temperature is 20 degrees, and I'm feeling a little nervous today."

[1962] Step 2:

[1963] The device sends the information entered by the user to the server. Specifically, it sends the following data to the server: "Today's plan = meet with a client," "Weather = sunny," "Temperature = 20 degrees," and "Emotion = nervous."

[1964] Step 3:

[1965] The server accesses the database and obtains the user's past clothing history and data on the clothes they own. This provides information to prevent them from using the same clothes multiple times. Example: The server obtains information such as "Past clothing history: navy suit, gray suit" and "Owned clothes: 4 suits, 8 shirts, 10 ties."

[1966] Step 4:

[1967] The server obtains information from fashion sites and trend information on the Internet through the API. This allows it to understand the latest trends and style guidelines. Example: The server obtains information that "Current trends: business casual, monochrome" are in fashion.

[1968] Step 5:

[1969] The server uses the weather forecast API to get the latest weather information, including the current temperature, forecast, wind speed, etc. Example: The server gets "Weather information: sunny, maximum temperature 20 degrees."

[1970] Step 6:

[1971] The server uses an emotion engine to recognize the emotional information entered by the user. For example, if the emotion "tension" is entered, this will influence the clothing suggestions made by the generation AI.

[1972] Step 7:

[1973] The server uses generative AI to generate optimal outfit suggestions based on the acquired information (user's past clothing data, trend information, weather information, schedule, and emotions). Example: The server suggests "navy suit, white shirt, and navy tie" to ease tension.

[1974] Step 8:

[1975] The server transmits the generated clothing suggestions to the user's terminal. Specifically, the generated suggestions are transmitted to the terminal via a network.

[1976] Step 9:

[1977] The terminal notifies the user of the proposal received from the server. Specifically, it displays "Today's proposal: navy suit, white shirt, and navy tie" to the user. The user confirms the displayed proposal.

[1978] Step 10:

[1979] The user checks the suggested outfits and inputs their satisfaction and feedback via the device. For example, the user may give feedback such as "I'm satisfied with this suggestion" or "I would like to receive similar suggestions in the future."

[1980] Step 11:

[1981] The server records user feedback and emotional data and uses it as learning data for the AI ​​to make future suggestions more suitable for the user. Specifically, it records that "the combination of a navy suit, white shirt, and navy tie was well received" and reflects this in future suggestions.

[1982] Example 2

[1983] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1984] Conventional clothing suggestion systems generate suggestions based on the user's schedule, weather, temperature, and preferred style, but do not take the user's emotions into account and are unable to generate different suggestions based on individual emotional changes.In addition, the system lacks a mechanism for incorporating user feedback into the generated suggestions, limiting the improvement of suggestion accuracy.

[1985] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1986] In this invention, the server includes a means for the user to input information such as schedule, weather, temperature, preferred style, and current emotion; a means for retrieving data on past clothing history and clothing owned from a database; and a means for analyzing the user's emotion data using an emotion engine. This allows for more appropriate clothing suggestions that take the user's emotions into consideration. Furthermore, by recording user feedback on generated suggestions and reflecting this in the next suggestions, the accuracy of the suggestions can be improved.

[1987] "Schedule" refers to the schedule or plan that the user will carry out that day.

[1988] "Weather" refers to the current weather and meteorological conditions.

[1989] "Temperature" refers to the temperature and temperature information for that day.

[1990] "Preferred style" refers to the clothing or fashion style that the user prefers.

[1991] "Emotions" refers to the user's current state of mind and emotional ups and downs.

[1992] "Past clothing history" refers to a record of clothing previously worn by the user.

[1993] "Data on clothes owned" refers to information about clothes and accessories owned by the user.

[1994] "Fashion information" refers to information about the latest fashions and styles.

[1995] "Trend information" refers to currently popular fashions and style guidelines.

[1996] "Weather forecast information" refers to information predicting future weather and temperature.

[1997] An "emotion engine" refers to a system that analyzes the emotion data entered by the user and recognizes their emotional state.

[1998] "Generative AI" refers to an artificial intelligence model that generates optimal clothing suggestions based on multiple pieces of information.

[1999] "User's terminal" refers to a computer device such as a smartphone or tablet used by a user.

[2000] "Feedback" refers to ratings and comments provided by users on suggested outfits.

[2001] This invention is a system for assisting users in choosing clothing, suggesting appropriate clothing based on information such as the user's schedule, weather, temperature, preferred style, current emotion, etc. In particular, this system is characterized by using an emotion engine to recognize the user's emotions and a generative AI model to suggest optimal clothing.

[2002] System configuration

[2003] The system includes a terminal where users can input information, a server that receives the information, and multiple engines and APIs that process and analyze the information.The database also records the user's past clothing history, clothing data, and user feedback.

[2004] Hardware and software used

[2005] 1. Terminal: A device that a user uses to input information, such as a smartphone or tablet.

[2006] 2. Server: Receives requests and processes information by interacting with databases and APIs.

[2007] 3. Database: A storage device for storing user history data and feedback.

[2008] 4. Emotion engine: Software for analyzing user emotion data.

[2009] 5. Generative AI model: Artificial intelligence that generates clothing suggestions based on user information.

[2010] 6. API: External services for obtaining trending and weather information.

[2011] Data processing and calculation

[2012] 1. Enter your information:

[2013] Users use a smartphone or tablet to input their "today's schedule," "weather," "temperature," "preferred style," and "current feelings" into the system.

[2014] 2. Submit your request:

[2015] The entered information is sent from the terminal to the server in JSON format or similar.

[2016] 3. Retrieving from the database:

[2017] The server retrieves data on past clothing history and clothing owned from the database, which helps to avoid duplication of previously used clothing.

[2018] 4. Get trend and weather information:

[2019] The server obtains the latest fashion information and weather forecasts through external APIs. Trend information includes currently popular fashion styles, and weather information includes current weather conditions and forecasts.

[2020] 5. Emotion engine analysis:

[2021] The server uses an emotion engine to analyze the emotion data entered by the user and determine the user's emotional state.

[2022] 6. Generating outfit suggestions:

[2023] The server uses a generative AI model based on all the information it has acquired so far (user's schedule, weather, temperature, preferred style, emotions, past clothing history, data on clothing owned, and trend information) to generate optimal clothing suggestions.

[2024] 7. Submission and Notification of Proposals:

[2025] The generated clothing suggestions are sent from the server to the user's terminal via the network, and the terminal notifies the user of the suggestions.

[2026] Specific examples

[2027] One day, a user inputs that they have a client meeting scheduled, the weather is sunny, the temperature is 20 degrees, and they are feeling a little nervous. The server retrieves the user's past clothing history from the database, combines the latest trend information with weather information and emotional information, and generates a suggestion of "navy suit, white shirt, navy tie." The user reviews this suggestion and provides feedback that they are satisfied. This feedback is recorded on the server and reflected in future suggestions.

[2028] Prompt Sentence Examples

[2029] "What is the best business casual attire for when you have a client meeting, it's sunny, it's 20 degrees, and you're a little nervous?"

[2030] In this way, the system makes optimal clothing suggestions based on the user's information and emotions, and incorporates feedback to improve the accuracy of the suggestions.

[2031] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2032] Step 1:

[2033] Users input information using devices such as smartphones and tablets. Specifically, they input information such as "today's schedule," "weather," "temperature," "preferred style," and "current emotion" into the application's input fields. This generates user input data.

[2034] Input: Today's schedule, weather, temperature, preferred style, current mood

[2035] Output: Input data (e.g., {Appointment: "Meet with a client", Weather: "Sunny", Temperature: 20, Style: "Business", Emotion: "Nervous"})

[2036] Step 2:

[2037] The terminal converts the information entered by the user into JSON format or similar and sends it to the server. Specifically, it sends the data to the server via an HTTP request, which allows the server to receive the data.

[2038] Input: Input data

[2039] Output: Request sent to server

[2040] Step 3:

[2041] The server analyzes the received data and accesses the database to retrieve the user's past clothing history and the clothes they own. It uses an SQL query to search the data and retrieves the results. This passes the user's past clothing history and a list of the clothes they currently own to the server.

[2042] Input: Received data

[2043] Output: User's past clothing history, clothing data

[2044] Step 4:

[2045] The server sends a request to an external API to get the latest fashion and trend information. For example, it sends a GET request to a fashion API to get trend information. This data is then processed on the server.

[2046] Input: API request

[2047] Output: Latest fashion and trend information

[2048] Step 5:

[2049] The server accesses the weather forecast API to obtain the latest weather information for the specified area. For example, it sends a GET request to the weather API and obtains weather and temperature information from the response. This data is stored on the server.

[2050] Input: API request

[2051] Output: Weather information, temperature information

[2052] Step 6:

[2053] The server uses an emotion engine to analyze the user's emotion data. Specifically, it analyzes the user's emotion input, such as "tension," to recognize the user's detailed emotional state. This information is used for further data processing.

[2054] Input: Emotion data

[2055] Output: Parsed emotional state

[2056] Step 7:

[2057] The server uses a generative AI model based on all the information it has acquired (user schedule, weather, temperature, preferred style, emotions, past clothing history, clothing data owned, and trend information) to generate optimal outfit suggestions. Text data combining this information is used as input prompts for the generative AI model.

[2058] Input: All acquired information

[2059] Output: Generated outfit suggestions

[2060] Step 8:

[2061] The server converts the generated clothing suggestions into JSON format and sends them to the user's device. The server then sends the suggestion data as an HTTP response over the network, allowing the user's device to receive the suggestions.

[2062] Input: Generated outfit suggestions

[2063] Output: Send the proposal to the device

[2064] Step 9:

[2065] The device notifies the user of the suggestions received from the server. Specifically, the suggestions are displayed as a pop-up or notification message within the application, allowing the user to confirm the suggestions.

[2066] Input: Receive proposal

[2067] Output: User notification of the proposal

[2068] Step 10:

[2069] The user reviews the proposed outfits and enters their satisfaction and feedback within the application, which generates evaluation comments.

[2070] Input: Confirm proposal

[2071] Output: Feedback data

[2072] Step 11:

[2073] The server records the feedback and emotion data provided by the user in a database and uses this data as training data to improve the accuracy of future suggestions.

[2074] Input: Feedback data, emotion data

[2075] Output: Update learning data, improve proposal accuracy

[2076] (Application example 2)

[2077] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2078] Conventional clothing suggestion systems do not take into account the user's emotions when making suggestions, making it difficult to suggest the best outfit for the user's mood and situation. Furthermore, they lacked connectivity with physical stores, making it impossible to purchase suggested outfits on the spot. Furthermore, they lacked the application of user feedback to improve the accuracy of suggestions.

[2079] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2080] In this invention, the server includes means for the user to input information such as schedule, weather, temperature, and preferred style, means for retrieving data on past clothing history and clothing owned from a database, means for retrieving fashion information and trend information, means for retrieving weather forecast information, means for recognizing the user's emotions in real time, generation AI means for generating outfit suggestions based on the retrieved information, means for displaying the suggested outfits as items available for purchase in a physical store, means for transmitting the generated suggestions to the user's terminal, and means for recording user feedback and reflecting it in the next suggestion. This makes it possible to take the user's emotions into consideration, facilitate purchases in a physical store, and improve the accuracy of suggestions based on feedback.

[2081] "Schedule" is information indicating the plans and activities that the user will undertake on that day.

[2082] "Weather" is information that indicates the weather conditions and meteorological conditions for that day.

[2083] "Temperature" is information indicating the temperature of the atmosphere at a specific time.

[2084] "Preferred style" is information that indicates the user's preferred fashion and clothing trends.

[2085] "Past clothing history" is a record of clothing worn by the user in the past.

[2086] "Data on clothes owned" is information on the types and quantities of clothes owned by the user.

[2087] A "database" is a collection of data and a system for efficiently managing information.

[2088] "Fashion information" is information about the latest clothing and accessories.

[2089] "Trend information" is information about currently popular fashions and styles.

[2090] "Weather forecast information" is information that indicates a prediction of future weather.

[2091] "Means for recognizing a user's emotions in real time" refers to technology for instantly analyzing a user's current emotions.

[2092] "Generative AI means" refers to artificial intelligence technology for generating clothing suggestions based on acquired information.

[2093] "In-store purchaseable items" are products that can be purchased immediately in a physical store.

[2094] The "means for transmitting the generated suggestions to the user's terminal" is a technique for sending the outfit suggestions to the device used by the user.

[2095] "User feedback" is information indicating evaluations and opinions from users.

[2096] "Means to reflect in the next proposal" refers to techniques for incorporating user feedback into the next proposal.

[2097] System Configuration

[2098] The system of the present invention consists of a series of processes in which a user inputs information through a terminal, and a server then suggests appropriate outfits based on that information. The system integrates the user's input information, a database on the server, a generation AI, trend information, weather information, and an emotion engine to suggest optimal outfits. It also has a function to support purchasing in physical stores.

[2099] Hardware and Software

[2100] Hardware:

[2101] Smartphone / Tablet: A device for entering user information and using the emotion recognition camera.

[2102] Server: A system for processing data and hosting APIs.

[2103] software:

[2104] OpenCV: A library for face detection and image processing.

[2105] Keras: A deep learning framework used for loading and inferencing emotion recognition models.

[2106] Requests: A library for making requests to external APIs (weather API, fashion API).

[2107] System action

[2108] The server performs the following data processing and calculations: The user uses their device to input information such as their schedule, weather, temperature, and preferred style, and sends that information to the server. The server retrieves the user's past clothing history and clothing data from a database, and also obtains external fashion information, trend information, and weather forecast information via an API. Next, it analyzes the user's current emotions using an emotion engine that recognizes the user's emotions in real time. A generation AI generates outfit suggestions based on the acquired information and sends these suggestions to the user's device. It also displays items that can be purchased in physical stores based on the suggested outfits to assist with purchasing. User feedback is collected and reflected in future suggestions, improving the accuracy of the suggestions.

[2109] Specific examples

[2110] One day, a user enters a physical store and types "I'm in a fun party mood today" into their device. The system uses an emotion engine to analyze the user's emotions and retrieves past clothing history, clothing owned, weather information, and fashion information from the database. Based on this information, the generation AI suggests the most suitable outfit, and the suggestion is sent to the user's device. The suggested outfit is then displayed as an item available for purchase in the physical store. The user purchases the outfit based on the suggestion and provides feedback on their satisfaction. The server records this feedback and reflects it in the next suggestion.

[2111] A specific example of a prompt is as follows:

[2112] "Today's suggestion: I'm going to a party and I'd like to know what items I can buy at a nearby store. I'm in a fun mood."

[2113] The invention is a system that helps users easily choose the best outfit for their mood and situation, and also helps them make purchases in physical stores smoothly. This system is expected to increase user satisfaction and improve the shopping experience in stores.

[2114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2115] Step 1:

[2116] The user uses the device to input information such as schedule, weather, temperature, preferred style, and emotions.

[2117] Input: User's schedule, weather, temperature, preferred style, emotions

[2118] Output: User information entered into the terminal

[2119] Specifically, the user inputs "I'm going to a party today and I'm in a good mood," which is then saved on the device.

[2120] Step 2:

[2121] The terminal transmits the input information to the server.

[2122] Input: User information stored on the device

[2123] Output: User information sent to the server

[2124] Specifically, the terminal transmits information such as "I'm going to a party today and I'm in a good mood" to the server via the network.

[2125] Step 3:

[2126] The server retrieves the user's past clothing history and data on the clothes they own from the database.

[2127] Input: User ID

[2128] Output: User's past clothing history and clothing data

[2129] Specifically, the server accesses the database and retrieves data such as "navy suit, white shirt, navy tie."

[2130] Step 4:

[2131] The server obtains fashion and trend information from the Internet via an API.

[2132] Input: API request

[2133] Output: Latest fashion and trend information

[2134] Specifically, the server sends a request to the fashion API and obtains information such as "business casual and monochrome are in fashion."

[2135] Step 5:

[2136] The server obtains weather information using the weather forecast API.

[2137] Input: Weather API request

[2138] Output: Weather information (sunny, temperature 20 degrees)

[2139] Specifically, the server accesses the weather forecast API and obtains information such as "The weather is sunny, with a maximum temperature of 20 degrees."

[2140] Step 6:

[2141] The server uses an emotion engine to recognize emotion information input by the user.

[2142] Input: User emotion data

[2143] Output: Perceived emotion (happy)

[2144] Specifically, the server inputs "feeling happy" into the emotion engine and analyzes it.

[2145] Step 7:

[2146] Based on the information acquired by the server, clothing suggestions are generated using a generation AI.

[2147] Input: Past clothing history, fashion information, weather information, schedule, emotions

[2148] Output: Generated outfit suggestions

[2149] Specifically, the generative AI generates suggestions such as "navy suit, white shirt, navy tie."

[2150] Step 8:

[2151] The server transmits the generated clothing suggestions to the terminal.

[2152] Input: Generated outfit suggestions

[2153] Output: Outfit suggestions sent to the device

[2154] Specifically, the server sends the suggestion to the terminal via the network, and "Today's suggestion: navy suit, white shirt, navy tie" is displayed.

[2155] Step 9:

[2156] The suggested outfits are displayed as items available for purchase in physical stores.

[2157] Input: Generated outfit suggestions

[2158] Output: A list of items available for purchase

[2159] Specifically, the terminal displays a list of products available for purchase at store A.

[2160] Step 10:

[2161] The user reviews the suggested outfits and enters feedback.

[2162] Input: User feedback

[2163] Output: Feedback data

[2164] Specifically, the user inputs feedback such as "I'm satisfied with this suggestion" and saves it on the device.

[2165] Step 11:

[2166] The server records the user's feedback and reflects it in future suggestions.

[2167] Input: Feedback data

[2168] Output: Updated user database

[2169] Specifically, the server records data such as "the combination of navy suit + white shirt + navy tie is popular" and uses it for the next proposal.

[2170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2171] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2172] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2180] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2181] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2183] 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.

[2184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2185] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2186] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2191] The following is further disclosed regarding the above embodiment.

[2192] (Claim 1)

[2193] A means for users to input information such as schedules, weather, temperature, and preferred style;

[2194] A means to retrieve data on past clothing history and clothing owned from a database,

[2195] A means of obtaining fashion and trend information,

[2196] A means for obtaining weather forecast information;

[2197] A generating AI means for generating clothing suggestions based on the acquired information;

[2198] means for transmitting the generated proposal to a user terminal;

[2199] A way to record user feedback and incorporate it into future proposals,

[2200] A system including:

[2201] (Claim 2)

[2202] The system according to claim 1, further comprising a function for preventing a person from re-encountering a specific person wearing the same clothes based on the acquired information.

[2203] (Claim 3)

[2204] 10. The system of claim 1, further comprising a function for learning from a user's historical data and feedback to improve the accuracy of the suggestions.

[2205] "Example 1"

[2206] (Claim 1)

[2207] A means for users to input information such as schedules, weather, temperature, and preferred style;

[2208] A means to retrieve data on past clothing history and clothing owned from a database,

[2209] A means of obtaining external fashion and trend information;

[2210] A means for obtaining weather forecast information;

[2211] a means including a generative AI model for generating clothing suggestions based on the acquired information;

[2212] means for transmitting the generated proposal to a user terminal;

[2213] A way to record user feedback and incorporate it into future proposals,

[2214] A system including:

[2215] (Claim 2)

[2216] The system according to claim 1, further comprising a function for preventing a person from re-encountering a specific person wearing the same clothes based on the acquired information.

[2217] (Claim 3)

[2218] 10. The system of claim 1, further comprising a function for learning from a user's historical data and feedback to improve the accuracy of the suggestions.

[2219] "Application Example 1"

[2220] (Claim 1)

[2221] A means for users to input information such as schedules, weather, temperature, and preferred style;

[2222] A means to retrieve data on past clothing history and clothing owned from a database,

[2223] A means of obtaining fashion and trend information,

[2224] A means for obtaining weather forecast information;

[2225] A generating AI means for generating clothing suggestions based on the acquired information;

[2226] means for transmitting the generated proposal to a user terminal;

[2227] a means for displaying the suggestions using a smart mirror;

[2228] A way to record user feedback and incorporate it into future proposals,

[2229] A system including:

[2230] (Claim 2)

[2231] The system according to claim 1, further comprising a function for preventing a person from re-encountering a specific person wearing the same clothes based on the acquired information.

[2232] (Claim 3)

[2233] 10. The system of claim 1, further comprising a function for learning from a user's historical data and feedback to improve the accuracy of the suggestions.

[2234] "Example 2: Combining Emotion Engines"

[2235] (Claim 1)

[2236] A means for users to input information such as their schedule, weather, temperature, preferred style, and current emotions;

[2237] A means to retrieve data on past clothing history and clothing owned from a database,

[2238] A means of obtaining fashion and trend information,

[2239] A means for obtaining weather forecast information;

[2240] means for analyzing user emotion data by an emotion engine;

[2241] A generating AI means for generating clothing suggestions based on the acquired information;

[2242] means for transmitting the generated prop...

Claims

1. A means for users to input information such as schedules, weather, temperature, and preferred style; A means to retrieve data on past clothing history and clothing owned from a database, A means of obtaining fashion and trend information, A means for obtaining weather forecast information; A generating AI means for generating clothing suggestions based on the acquired information; means for transmitting the generated proposal to a user terminal; A way to record user feedback and incorporate it into future proposals, A system including:

2. The system according to claim 1, further comprising a function for preventing a person from re-encountering a specific person wearing the same clothes based on the acquired information.

3. The system of claim 1 , further comprising a function for learning from a user's historical data and feedback to improve the accuracy of the suggestions.

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