System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting user information; means for filling in user preferences, styles, and needs; means for transmitting the user information and preferences, styles, and needs to a server; means for executing a AI algorithm to select optimal items based on the user information; means for displaying the selected items to the user; means for periodically delivering items based on a subscription plan; and means for providing feedback on items received by the user and updating the AI algorithm accordingly.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] In today's busy society, many people lack the time to choose and shop for fashion. Furthermore, many people find it difficult to select appropriate items due to a lack of fashion knowledge or confidence. Furthermore, selecting fashion items for seasonal coordination or special events requires additional effort. These challenges are a major source of stress in modern lifestyles and detract from the enjoyment of fashion. The present invention solves these challenges by providing a system that allows users to achieve their individual style while saving time and effort. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting user information and entering information regarding preferences, style, and needs, a means for transmitting the user information and information regarding preferences, style, and needs to a server, a means for executing an AI algorithm to select optimal items based on the user information, a means for displaying the selected items to the user, a means for periodically delivering items based on a subscription plan, and a means for providing feedback to the user about the items received and updating the AI ​​algorithm based on the feedback. The system also includes a means for the user to try on the received items and select whether to keep or return them. The selection information is transmitted to the server and the database is updated, thereby further optimizing the user's next item selection. This allows users to easily and efficiently enjoy the latest fashion.

[0006] A "user" is a person who uses the system and provides personal information such as registration information, preferences, and style to the system.

[0007] A "means for inputting information" is a method or device that provides an interface for a user to input required information into the system. Examples include web forms and mobile applications.

[0008] "Information about preferences, style, and needs" refers to information such as the user's own fashion preferences and the types, colors, and uses of items they desire.

[0009] A "server" is a computer system that receives, stores, and further processes information from users.

[0010] "Database" means a storage device or system for recording and storing user information, preferences, styles, requests, etc.

[0011] "AI algorithm" is an artificial intelligence technology that analyzes collected data and selects the most suitable fashion items.

[0012] An "item" refers to a fashion-related product provided to a user, and includes, as specific examples, clothing, accessories, shoes, etc.

[0013] "Subscription plan" refers to a form of continuous service provision that a user enters into, in which items are received on a regular basis.

[0014] "Feedback" refers to the user's ratings and opinions about the items they receive, and is information provided to the system.

[0015] "Trying on" refers to the process in which a user actually wears and evaluates the item they received.

[0016] "Keep" refers to the user keeping the items they like from the items they received.

[0017] "Return" refers to the process by which a user returns to the system any items they have received that they no longer need.

[0018] "Delivery means" refers to the process of using delivery methods and related vendors to deliver items from the system to the user. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention is a subscription-based system that provides optimal fashion coordination tailored to a user's preferences, style, and needs. This system uses an AI algorithm to select fashion items based on information entered by the user and delivers them periodically, allowing the user to easily try on the items at home. Furthermore, the AI ​​continues to learn based on user feedback, making its next suggestions more personalized.

[0041] Registering users and setting preferences

[0042] Users first access the service using their device and register. On the registration page, they enter basic information and verify their email address. They then enter detailed information about their tastes, style, and needs, including their favorite colors, preferred types of items, and special events.

[0043] Sending and storing user information

[0044] The device sends the entered information to the server, which then stores it in a database and manages it as a user profile.

[0045] AI stylist selection

[0046] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This information is then sent to the user's device and displayed.

[0047] Regular deliveries

[0048] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, and the items are packed and delivered as instructed.

[0049] Trying on and holding items

[0050] The user tries on the received item and decides whether to keep it or return it, and then uses the terminal to notify the server of the result, which stores the information in a database.

[0051] Inputting feedback and training the AI

[0052] Users can use their devices to input feedback about the items they receive and send it to the server, which then updates the AI ​​algorithm based on that feedback and reflects it in the next item selection.

[0053] Specific examples

[0054] For example, a user might say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects a casual blue shirt and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user uses their device to notify the server of the results, and the AI ​​learns to make the next selection more in line with the user's preferences.

[0055] As described above, the system of the present invention proposes optimal fashion items that meet the user's preferences and needs, allowing the user to enjoy the latest fashions while reducing the burden on the user.

[0056] The processing flow will be explained below.

[0057] Registration and Preferences

[0058] Process flow:

[0059] Step 1:

[0060] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[0061] Step 2:

[0062] The user enters basic information and clicks the "Register" button.

[0063] Step 3:

[0064] The terminal transmits the input information to the server.

[0065] Step 4:

[0066] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[0067] Step 5:

[0068] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[0069] Step 6:

[0070] The user responds to the questionnaire form and clicks the "Submit" button.

[0071] Step 7:

[0072] The device sends information about preferences, style and needs to the server.

[0073] Step 8:

[0074] The server stores this information in a database and updates the user's profile.

[0075] AI stylist selection

[0076] Process flow:

[0077] Step 1:

[0078] The server periodically retrieves user information from the database.

[0079] Step 2:

[0080] The server uses the information it receives to run AI algorithms to select the best items, taking into account the user's preferences, style, season, temperature, and special events.

[0081] Step 3:

[0082] The server sends information about the selected item to the terminal.

[0083] Step 4:

[0084] The terminal displays the received item information to the user.

[0085] Regular deliveries

[0086] Process flow:

[0087] Step 1:

[0088] The server schedules the next delivery date based on the user's subscription plan.

[0089] Step 2:

[0090] When the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[0091] Step 3:

[0092] The server sends the generated delivery instructions to the warehouse system.

[0093] Step 4:

[0094] The warehouse system receives the instructions and picks and packs the specified items.

[0095] Step 5:

[0096] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[0097] Trying on and holding items

[0098] Process flow:

[0099] Step 1:

[0100] The user receives the delivered item.

[0101] Step 2:

[0102] The user checks the received item list on the terminal.

[0103] Step 3:

[0104] The user tries on the item and decides whether to keep it or return it.

[0105] Step 4:

[0106] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[0107] Step 5:

[0108] The terminal transmits the user's selection information to the server.

[0109] Step 6:

[0110] The server receives the selections, updates the database, and generates return labels for any returned items.

[0111] Inputting feedback and training the AI

[0112] Process flow:

[0113] Step 1:

[0114] The user enters feedback about the item received at the terminal.

[0115] Step 2:

[0116] The user completes their feedback and clicks the "Submit" button.

[0117] Step 3:

[0118] The terminal transmits the feedback information to the server.

[0119] Step 4:

[0120] The server receives the feedback information and stores it in a database.

[0121] Step 5:

[0122] The server updates the AI ​​algorithm based on the new feedback and reflects it in the next item selection.

[0123] The above is a detailed and specific explanation of each processing step, which allows the user to efficiently and accurately receive fashion items that suit their preferences.

[0124] Example 1

[0125] 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."

[0126] Conventional fashion coordination systems did not adequately select items that matched the user's preferences and style, making it difficult to personalize the system to meet the user's needs. Furthermore, there were issues with insufficient regular delivery and improvements based on feedback, resulting in a poor user experience. Furthermore, the AI ​​model did not efficiently suggest or learn appropriate items to select.

[0127] 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.

[0128] In this invention, the server includes means for executing an artificial intelligence model to select optimal items based on user information, means for displaying the selected items to the user, and means for periodically delivering the items based on a subscription plan, thereby enabling provision of personalized fashion items tailored to the user's preferences and style.

[0129] "User information" refers to detailed information entered by a user, such as personal information, preferences, style, and requests.

[0130] "Server" is a computer system that has the ability to receive and store user information and run artificial intelligence models to select the most suitable items for the user.

[0131] An "artificial intelligence model" is a machine learning algorithm that selects the most suitable fashion items based on user information.

[0132] "Means for displaying selected items to the user" refers to a technique or method for transmitting information about the fashion items selected by the server to the user's terminal and displaying it.

[0133] A "subscription plan" is a service contract under which a user receives fashion items delivered to them on a regular basis.

[0134] "Means for periodic delivery" refers to a technique or method by which the server issues delivery instructions to the warehouse system and sends items to the user based on the user's subscription plan.

[0135] "Feedback" is information that provides a user's impressions and evaluations of the items they have received.

[0136] A "prompt sentence" is a specific instruction to the AI ​​model to select fashion items based on the user's preferences and style.

[0137] The present invention is a subscription-based system that provides optimal fashion coordination based on a user's preferences, style, and needs. To build this system, the following hardware and software are used.

[0138] Hardware and software used

[0139] Hardware: User devices (smartphones, tablets, PCs), servers, warehouse systems

[0140] Software: Database management systems (MySQL, PostgreSQL, etc.), AI algorithms (TensorFlow, PyTorch, etc.), web application frameworks (React, Angular, etc.)

[0141] Specific operation of the system

[0142] 1. User Registration and Preferences

[0143] First, a user uses a terminal to access the system's registration page and enter basic information. Then, they answer detailed questions about their preferences and style. For example, by answering questions such as "What is your favorite color?" and "What style do you like?", the user's individual preferences and requests are provided to the system.

[0144] 2. Transmission and storage of user information

[0145] The terminal sends the input information to the server, which stores it in a database and manages it as a user profile.

[0146] 3. AI stylist selection

[0147] The server periodically retrieves user information from the database and uses AI algorithms to select fashion items that best suit the user's preferences and style. For example, it may select items for a particular season or event. The results of the selection are sent to the device and displayed to the user.

[0148] 4. Regular deliveries

[0149] The server schedules the next delivery date based on the user's subscription plan, and when this date approaches, the server issues a delivery instruction to the warehouse system, which then packs and delivers the item to the user.

[0150] 5. Trying on and holding items

[0151] The user tries on the received item and decides whether to keep it or return it. The result is notified to the server from the terminal, and the server stores the information in a database.

[0152] 6. Inputting feedback and training the AI

[0153] Users use their devices to provide feedback about the items they receive, and the server receives this feedback and updates the AI ​​algorithm, which helps ensure that the next item selection is more tailored to the user's preferences.

[0154] Examples and prompts

[0155] For example, based on a user's information such as "I like casual style, I like blue items, and I travel a lot in the summer," the AI ​​will select blue casual shirts and shorts and deliver them to the user periodically. Below is a specific example of such a prompt.

[0156] Prompt Sentence Examples

[0157] User Information:

[0158] 1. Style: Casual

[0159] 2. Favorite color: Blue

[0160] 3. Season: Summer

[0161] 4. Special requirements: I travel a lot

[0162] Prompt the AI ​​model:

[0163] Please suggest the best fashion items for a user who likes casual style, prefers blue items, and travels a lot in the summer.

[0164] The AI ​​model uses this prompt to select fashion items, and the server then processes the delivery based on the results. User feedback can be used to further personalize future recommendations, ensuring that the latest trends are always available.

[0165] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0166] Step 1:

[0167] A user accesses the system using a terminal and enters basic information and information about preferences, style, and needs on a registration page.

[0168] Input: Details such as the user's name, email address, address, preferred color, style, and any special requests

[0169] Specific operation: The user enters information into a form on the device screen and presses the "Submit" button.

[0170] Output: The input information becomes data to be sent from the terminal.

[0171] Step 2:

[0172] The terminal transmits the input user information to the server.

[0173] Input: User information entered in step 1

[0174] Specific operation: The device sends data to the server using the HTTPS protocol.

[0175] Output: The server receives the user information data.

[0176] Step 3:

[0177] The server stores the received user information in a database.

[0178] Input: User information data received in step 2

[0179] What happens: The server executes an SQL query to store the user information in a database (e.g., MySQL or PostgreSQL).

[0180] Output: User information is saved in the database.

[0181] Step 4:

[0182] The server periodically retrieves user information from the database and uses a generative AI model to select the most suitable items.

[0183] Input: User information, season, temperature, special event information retrieved from the database

[0184] Specific operation: The server executes an SQL query to obtain user information, inputs that information into the AI ​​model as a prompt, and the AI ​​model selects an item.

[0185] Output: Selected fashion item information is generated.

[0186] Step 5:

[0187] The server sends the selected item to the user's terminal and displays it.

[0188] Input: Fashion item information generated in step 4

[0189] Specific operation: The server sends the selected item information to the user's device. The device displays the received information.

[0190] Output: The selected item information is displayed on the user's device.

[0191] Step 6:

[0192] The server schedules the next delivery date based on the user's subscription plan and issues a delivery instruction to the warehouse system.

[0193] Input: User's subscription plan information and selected item information

[0194] Specific operation: The server uses the scheduler to set the next delivery date and sends packing and delivery instructions to the warehouse system via API.

[0195] Output: The item is packaged and delivered to the user.

[0196] Step 7:

[0197] The user tries on the received item and uses the terminal to decide whether to keep it or return it.

[0198] Input: User feedback about the item received

[0199] Specific operation: The user selects the option to keep or return using the terminal application and sends the result.

[0200] Output: The selection is sent to the server.

[0201] Step 8:

[0202] The server stores the received feedback and selection information in a database and updates the AI ​​model to reflect this in the next item selection.

[0203] Input: Feedback and selections received from users

[0204] How it works: The server stores the feedback information in a database and uses that data to retrain the AI ​​model.

[0205] Output: The next selection made by the updated AI model will be more in line with the user's preferences.

[0206] (Application example 1)

[0207] 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."

[0208] Conventional fashion coordination suggestion systems lack sufficient personalization based on the user's preferences, style, and needs, making it difficult to provide appropriate suggestions. Furthermore, there is a lack of technology to efficiently utilize user feedback, limiting the improvement of user satisfaction. Furthermore, there is a need for a method that allows users to easily try on suggested items and choose whether to keep them or return them.

[0209] 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.

[0210] In this invention, the server includes a means for allowing a user to input preferences and feedback using a smartphone, and for AI to select and suggest personalized fashion items based on the input, a means for transmitting information about the user and information about preferences, style, and needs to the server, and a means for executing an AI algorithm to select optimal items based on the user information, thereby enabling efficient input of user information and provision of personalized fashion suggestions.

[0211] The "means for inputting user information" refers to an interface that allows a user to input information such as their personal information, preferences, style, and requests into the system.

[0212] The "means for entering information about the user's preferences, style, and requests" is a function that allows the user to enter detailed information about specific fashion preferences, style, requests regarding events, and the like.

[0213] The "means for transmitting the user's information and information regarding preferences, style, and needs to the server" refers to a communication means for transmitting the information input by the user to the central server.

[0214] "Means for executing an AI algorithm to select optimal items based on user information" refers to a system that runs on a server and executes an algorithm to select fashion items that match the user's preferences, style, and requests.

[0215] The "means for displaying selected items to the user" refers to an interface for displaying the fashion items selected by the AI ​​algorithm on the user's device.

[0216] "Means for periodically delivering items based on a subscription plan" refers to the procedures and means for periodically delivering fashion items to a user's address based on a plan selected by the user.

[0217] "A means for users to provide feedback on the items they receive and update the AI ​​algorithm based on that feedback" refers to a system in which users input their impressions and ratings of the items they try on, and that information is used to improve and update the AI ​​algorithm.

[0218] "A means for AI to select and suggest personalized fashion items based on the input of user preferences and feedback using a smartphone" refers to a system in which users input preferences and feedback through a smartphone application, and AI selects and suggests personalized fashion items based on that information.

[0219] This invention relates to a subscription system that proposes and periodically delivers optimal fashion items based on a user's preferences, style, and needs. The system consists of multiple components, including a smartphone application, a server, and an AI algorithm.

[0220] User registration and information entry

[0221] Users first access the service using their smartphone and register. On the registration page, they enter basic information and verify their email address. Next, users enter detailed information about their preferences, style, and needs, including favorite colors, preferred types of items, and special events.

[0222] Sending and storing information

[0223] The information entered by the user is sent from the smartphone to a central server, which then stores it in a database. The stored information is managed as a user profile and used for subsequent processing.

[0224] Item selection by AI algorithm

[0225] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This selection information is then sent to the user's smartphone and displayed.

[0226] Regular deliveries

[0227] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, which then packs the items and delivers them to the user's address.

[0228] Trying on items and giving feedback

[0229] The user tries on the received item and decides whether to keep it or return it. They can then use their smartphone to notify the server of their decision. The server stores this information in a database and uses it to select items for the next purchase. The user can also enter feedback about the received item and send it back to the server. The server uses this feedback to retrain the AI ​​algorithm and make the next recommendation even more personalized.

[0230] Hardware and software used

[0231] Hardware: Smartphones, servers, database systems (e.g., PostgreSQL)

[0232] Software: Smartphone application, Python code for server-side processing (e.g., Flask), AI algorithms (e.g., TensorFlow or PyTorch)

[0233] Specific examples

[0234] For example, a user may say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects blue casual shirts and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user then notifies the server of the results using their smartphone, allowing the AI ​​to learn so that the next selection will better suit the user's preferences.

[0235] Prompt Sentence Examples

[0236] "Suggest fashion items for a summer trip for a user who likes casual style and is looking for something blue."

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] Users access the service using their smartphones and enter basic information as well as detailed information about their preferences, style and desires.

[0240] Input: User information (name, email address, favorite colors, style, special events, etc.)

[0241] Output: The entered user information is saved in the smartphone application.

[0242] Step 2:

[0243] The entered user information is sent from the smartphone to the server.

[0244] Input: User information on smartphone

[0245] Output: The server receives the user information and stores it in the database.

[0246] Step 3:

[0247] The server periodically retrieves user information from the database and runs an AI algorithm to select fashion items that best suit the user's preferences and style.

[0248] Input: User information stored in the database

[0249] Output: A list of fashion items selected by an AI algorithm

[0250] Step 4:

[0251] The selected fashion items are sent to a smartphone and displayed to the user.

[0252] Input: A list of items selected by an AI algorithm

[0253] Output: Item list displayed on smartphone

[0254] Step 5:

[0255] The server schedules the next delivery based on the user's subscription plan and generates delivery instructions for the warehouse system.

[0256] Input: Subscription plan information, selected item list

[0257] Output: Delivery schedule information, delivery instructions

[0258] Step 6:

[0259] The user tries on the received item, chooses on their smartphone whether to keep it or return it, and notifies the server of the result.

[0260] Input: Choice of keeping or returning the item you tried on

[0261] Output: Selections sent to the server

[0262] Step 7:

[0263] The server stores the selection information in a database and uses it as feedback to reflect in the next item selection. User feedback information is also sent to the server and used as data for retraining the AI ​​algorithm.

[0264] Input: Selection information, feedback information

[0265] Output: Database update, AI algorithm retraining

[0266] Through the above processing steps, the system will continually suggest optimal fashion items based on the user's preferences and feedback.

[0267] 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.

[0268] This invention is a fashion coordination provision system that combines an emotion engine that recognizes the user's emotions, achieving a high level of personalization based not only on the user's preferences and requests but also on their emotions. By utilizing the information entered by the user as well as the emotional data analyzed by the emotion engine, the AI ​​algorithm selects the most suitable fashion items for the user and delivers them periodically. This allows users to easily try on items at home and enjoy highly personalized coordination.

[0269] Registering users and setting preferences

[0270] Using their device, users access the service's registration page. They create an account by entering basic information such as their name, email address, and password. They also enter detailed information about their preferences, style, and needs, including favorite colors, types of items they like, and special events. This information is sent via the device to the server and stored in a database.

[0271] AI stylist selection and user emotion recognition

[0272] The server periodically retrieves the user's basic information and preference information from the database. In addition, it uses an emotion engine to analyze the user's emotion data. The emotion engine recognizes and analyzes emotions from images, videos, text, etc. input or provided by the user. The analysis results are sent to the server.

[0273] Optimal item selection

[0274] The AI ​​algorithm on the server selects the most suitable fashion items based on the user's basic information, preferences, and emotional data. The AI ​​algorithm also takes into consideration the season, temperature, special events, and other factors when selecting items. The results of this selection are sent to the device and displayed to the user.

[0275] Regular deliveries

[0276] The server schedules the next delivery date based on the user's subscription plan. When the delivery date approaches, the server generates a delivery instruction for the warehouse system, and the specified items are picked and packed. The items are then handed over to a delivery company for delivery to the user.

[0277] Trying on and holding items

[0278] The user receives the delivered items and tries them on. The user checks the item list on the terminal and decides whether to keep or return the items after trying them on. The result is notified to the server via the terminal, and the server updates the database.

[0279] Feedback and sentiment data input

[0280] The user enters feedback about the item they received on the device, including their opinion on size, color, and style. The user's emotional data is also collected. This information is then sent to the server and stored in a database.

[0281] AI learning and item selection optimization

[0282] The server updates the AI ​​algorithm based on the newly received feedback and emotional data, taking into account the user's emotional tendencies and optimizing the next item selection. For example, if a user expresses strong emotions about a particular item, that information will be reflected in the next selection.

[0283] Specific examples

[0284] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[0285] As described above, the system of the present invention realizes a high level of personalization that takes into consideration the user's emotions, and provides efficient and appropriate fashion coordination.

[0286] The processing flow will be explained below.

[0287] Registering users and setting preferences

[0288] Process flow:

[0289] Step 1:

[0290] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[0291] Step 2:

[0292] The user enters basic information and clicks the "Register" button.

[0293] Step 3:

[0294] The terminal transmits the input information to the server.

[0295] Step 4:

[0296] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[0297] Step 5:

[0298] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[0299] Step 6:

[0300] The user responds to the questionnaire form and clicks the "Submit" button.

[0301] Step 7:

[0302] The device sends information about preferences, style and needs to the server.

[0303] Step 8:

[0304] The server stores this information in a database and updates the user's profile.

[0305] AI stylist selection and user emotion recognition

[0306] Process flow:

[0307] Step 1:

[0308] The server periodically retrieves the user's basic information and preferences from the database.

[0309] Step 2:

[0310] The server activates an emotion engine and analyzes data such as images, videos, and text provided by the user to recognize emotions.

[0311] Step 3:

[0312] The emotion engine analyzes the user's emotions and sends the results to the server.

[0313] Step 4:

[0314] The AI ​​algorithm on the server selects the most suitable items based on the user's basic information, preferences, and emotional data.

[0315] Step 5:

[0316] The server sends information about the selected item to the terminal.

[0317] Step 6:

[0318] The terminal displays the received item information to the user.

[0319] Regular deliveries

[0320] Process flow:

[0321] Step 1:

[0322] The server schedules the next delivery date based on the user's subscription plan.

[0323] Step 2:

[0324] As the delivery date approaches, the server generates delivery instructions for the warehouse system.

[0325] Step 3:

[0326] The server sends the generated delivery instructions to the warehouse system.

[0327] Step 4:

[0328] The warehouse system receives the instructions and picks and packs the specified items.

[0329] Step 5:

[0330] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[0331] Trying on and holding items

[0332] Process flow:

[0333] Step 1:

[0334] The user receives the delivered item.

[0335] Step 2:

[0336] The user checks the received item list on the terminal.

[0337] Step 3:

[0338] The user tries on the item and decides whether to keep it or return it.

[0339] Step 4:

[0340] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[0341] Step 5:

[0342] The terminal transmits the user's selection information to the server.

[0343] Step 6:

[0344] The server receives the selections, updates the database, and generates return labels for any returned items.

[0345] Feedback and sentiment data input

[0346] Process flow:

[0347] Step 1:

[0348] The user enters feedback about the received item at the terminal.

[0349] Step 2:

[0350] The user completes their feedback and clicks the "Submit" button.

[0351] Step 3:

[0352] The terminal transmits the feedback information to the server.

[0353] Step 4:

[0354] The server receives the feedback information and stores it in a database.

[0355] Step 5:

[0356] The server restarts the emotion engine and collects and analyzes the user's emotion data.

[0357] Step 6:

[0358] The emotion engine sends the analysis results to the server.

[0359] AI learning and item selection optimization

[0360] Process flow:

[0361] Step 1:

[0362] The server updates its AI algorithms based on newly received feedback and emotional data.

[0363] Step 2:

[0364] The AI ​​algorithm takes into account the user's emotional tendencies to further optimize the next item selection.

[0365] Step 3:

[0366] The server will perform new item selection based on the updated AI algorithm.

[0367] Step 4:

[0368] Information about the selected item is sent to the terminal and displayed to the user.

[0369] Specific examples

[0370] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on the subscription plan. The user can try them on at home, keep them, and return them, and the feedback and emotional data will be reflected in the next item selection.

[0371] The above is a detailed processing flow of the system that includes an emotion engine that recognizes the user's emotions. This enables advanced personalization that takes the user's emotions into consideration, providing efficient and appropriate fashion coordination.

[0372] Example 2

[0373] 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."

[0374] Conventional fashion coordination systems only select items based on the user's preferences, style, and needs, but do not provide personalization that takes into account the user's emotions. Furthermore, because the selected items do not necessarily match the user's emotions or mood, it is difficult to improve user satisfaction. Furthermore, updating the AI ​​algorithm based on feedback is inefficient, resulting in insufficient optimization of the next item selection.

[0375] 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.

[0376] In this invention, the server includes means for using an emotion recognition engine to analyze user input data and emotion data, means for executing an AI algorithm to select optimal items based on the analysis results, and means for learning and updating the AI ​​algorithm based on user feedback and emotion data. This enables advanced personalization tailored to the user's emotions, improving the accuracy of item selection and user satisfaction.

[0377] "User information" refers to basic personal information such as the user's name, email address, and password.

[0378] "Information about preferences, styles, and needs" is detailed information such as the user's favorite colors, types of items, and needs for special events.

[0379] The "server" is a computer system that receives user information and preference information, stores it in a database, and then selects items using AI algorithms and emotion recognition engines.

[0380] An "emotion recognition engine" is a program that analyzes emotions from images, videos, text, etc. provided by the user and generates emotional data.

[0381] "AI algorithm" is an artificial intelligence technology that selects the most suitable items based on the user's basic information, preferences, and emotional data.

[0382] "Feedback" refers to information about opinions and ratings regarding the size, color, and style of the items the user has tried on.

[0383] A "subscription plan" is a service agreement under which a user receives items on a regular basis, including the frequency and terms of delivery.

[0384] MODE FOR CARRYING OUT THE INVENTION

[0385] The present invention is a fashion coordination system that combines an emotion recognition engine that recognizes the user's emotions. This system realizes advanced personalization based on emotions as well as the user's preferences and requests.

[0386] First, a user uses their device to access the service's registration page and create an account by entering basic information such as their name, email address, and password. Next, the user enters detailed information about their preferences, style, and needs. This information is sent via the device to a server and stored in a database.

[0387] The server uses an emotion recognition engine (e.g., Google Cloud Vision API or IBM Watson) to analyze emotions from images, videos, and text provided by the user and generate emotion data. The analysis results are sent to the server and stored in a database.

[0388] The AI ​​algorithm selects the most suitable fashion items based on the user's basic information, preferences, and emotional data stored on the server. This selection is performed using a generative AI model. For example, open-source machine learning libraries such as TensorFlow and PyTorch are used as generative AI models. The selected items are then sent to the device and displayed to the user.

[0389] The server schedules the next delivery date based on the user's subscription plan. As the delivery date approaches, the server generates a delivery instruction for the warehouse system. The warehouse system picks, packs, and delivers the specified items to a delivery company. The items are then delivered to the user.

[0390] The user receives the delivered item and tries it on. After trying it on, the user uses a terminal to select whether to keep it or return it. This selection information is sent to the server, which updates the database.

[0391] Users can use the device to provide feedback on the items they receive, including opinions on size, color, and style. In addition, emotional data is collected and sent to the server where it is stored in a database.

[0392] The server updates the AI ​​algorithm based on new feedback and emotional data. The generative AI model further refines personalization based on the user's emotional tendencies, resulting in a more optimized selection of items for the next round.

[0393] For example, a user might input, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion recognition engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). An example of a prompt sentence might be, "The user likes a casual style and would like me to select blue items. However, I'm currently feeling stressed, so I'd like you to recommend some relaxing fashion." This is input to the generative AI model.

[0394] As described above, the system of the present invention realizes a high level of personalization that takes into account the user's emotions, and provides efficient and accurate fashion coordination. This system aims to improve user satisfaction and is capable of always providing optimal coordination.

[0395] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0396] Step 1: User Registration

[0397] The user accesses the service's registration page using a device and enters basic information such as name, email address, and password. The device then sends this information to the server, which then stores it in a database.

[0398] Input: User's name, email address, and password

[0399] Output: Basic information of the user stored on the server

[0400] Specific operation: The user enters information into the input form and clicks the "Register" button. The device sends the input data to the server via a POST request. The server saves the information in the database and returns a message to the device indicating registration is complete.

[0401] Step 2: Set your preferences

[0402] Users enter details about their preferences, style and needs, and the device sends this information to the server, which stores it in a database.

[0403] Input: User preferences (color, item type, special events, etc.)

[0404] Output: User details stored on the server

[0405] How it works: The user enters their preferred information by manipulating multiple checkboxes and text fields on the device screen. The device then sends this data to the server, which stores the information in a database.

[0406] Step 3: Emotion Recognition

[0407] The server uses an emotion recognition engine to analyze emotions from images, videos, and text provided by the user, and the analysis results are stored on the server as emotion data.

[0408] Input: User-uploaded images, videos, and text

[0409] Output: Parsed emotion data

[0410] How it works: A user uploads images, videos, and text using their device. The device then sends this data to the server, which then uses an emotion recognition engine to analyze the emotions and records the results in a database.

[0411] Step 4: Selecting items

[0412] The AI ​​algorithm runs on the server and combines the user's basic information, preferences, and emotional data to select the most suitable fashion items. The results are then sent to the device and displayed to the user.

[0413] Input: User's basic information, preference information, emotional data

[0414] Output: A list of selected fashion items

[0415] Specific operation: The server retrieves registered user information from the database and selects items using a generative AI model. The selection results are sent to the device and displayed on the device.

[0416] Step 5: Arrange shipping

[0417] The server schedules the next delivery date based on the subscription plan, and when the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[0418] Input: User's subscription plan

[0419] Output: Delivery instructions, picking list

[0420] Specific operation: The server checks the delivery schedule, sends an API request to the warehouse management system, and creates a picking list. Warehouse workers pick items based on the list, pack them, and hand them over to the delivery company.

[0421] Step 6: Try on the item and decide whether to keep it

[0422] The user receives the delivered item, tries it on, and selects whether to keep it or return it. This selection information is then sent to the server, which updates the database.

[0423] Input: User's retention or return decision

[0424] Output: Updated retention information on the server

[0425] Specific operation: The user clicks the confirmation button on the device and selects the items to keep and the items to return. The device sends this information to the server, which updates the database.

[0426] Step 7: Collect feedback and sentiment data

[0427] Users input their feedback about the items they receive on the device, and emotional data is collected. This information is then sent to the server and stored in a database.

[0428] Input: User feedback, emotional data

[0429] Output: Feedback and emotion data stored in a database

[0430] Specific operation: The user enters their opinion and evaluation score in the feedback form on the device and uploads emotional data (e.g., photos and text). This data is sent to the server, which then stores it in a database.

[0431] Step 8: Training the AI ​​algorithm

[0432] The server updates the AI ​​algorithm based on new feedback and emotion data, which further optimizes the next item selection.

[0433] Input: User feedback, emotional data

[0434] Output: Updated AI algorithm

[0435] How it works: The server inputs the received data into the generative AI model and uses it as new training data. The AI ​​algorithm learns and improves the accuracy of the next item selection.

[0436] (Application example 2)

[0437] 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."

[0438] Conventional fashion coordination systems provide personalization based on the user's preferences, style, and needs, but do not provide advanced personalization based on the user's emotions. This makes it difficult for users to select the optimal fashion items that match their emotions at any given time, and this has led to the issue of not being able to sufficiently increase user satisfaction.

[0439] 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.

[0440] In this invention, the server includes means for executing an artificial intelligence algorithm for selecting optimal items based on the user's personal information and emotion data obtained by an emotion analysis engine, means for displaying the selected items to the user, and means for delivering the items to the user based on a regular delivery schedule. This enables advanced personalization based on the user's emotions and makes it possible to provide the user with the most optimal fashion items.

[0441] Definition of each word

[0442] "Personal information" refers to information that identifies an individual, such as the user's name, address, email address, and telephone number.

[0443] "Preferences" is information about the user's preferences such as colors, styles, and fashion items.

[0444] "Style" is information about the type and design of fashion that the user normally wears.

[0445] "Request" is information about items or services that a user desires for a specific event or purpose.

[0446] "Data" refers to any information processed by the system, including users' personal information, preferences, style, needs, and emotional data.

[0447] A "server" is a computer system for storing and processing data sent by users.

[0448] An "emotion analysis engine" is software that analyzes a user's emotions from input data such as images and text provided by the user and generates data.

[0449] "Emotion data" is information about the user's emotional state obtained by the emotion analysis engine.

[0450] An "artificial intelligence algorithm" is a computer program that selects items suitable for a user through a process of data analysis and learning.

[0451] The "display means" is a function for displaying information about the selected item on the user's terminal screen.

[0452] A "delivery plan" is a schedule for periodically sending selected items to a user.

[0453] "Feedback" refers to the thoughts and opinions of users about the items they receive, which helps improve the system.

[0454] "Mode for Carrying Out the Invention"

[0455] This invention is a system that analyzes a user's emotions and suggests fashion coordination. The system collects and analyzes the user's emotional data, selects optimal fashion items based on that data, and delivers them to the user on a regular basis. The configuration and operation of this system are described below.

[0456] Hardware and software used

[0457] Hardware:

[0458] Smartphone: A device that allows users to input information and interact with systems.

[0459] Server: A system for storing data and running AI algorithms and sentiment analysis engines.

[0460] software:

[0461] Emotion analysis engine (EmotionEngine): Software that analyzes images and text provided by users and generates emotional data.

[0462] Artificial intelligence algorithm (FashionAI): An algorithm that selects fashion items based on a user's personal information, preferences, style, and emotional data.

[0463] Database: Stores and manages users' personal information, preferences, style, feedback, and emotional data.

[0464] Delivery System: Software that executes a schedule for the periodic delivery of selected items to users.

[0465] System Overview

[0466] The server receives personal information and preference data entered by the user and stores the data in a database. The user's emotional data is extracted from the images and text provided by the user using an emotional analysis engine. The server then runs an artificial intelligence algorithm based on the user's personal information, preferences, style, and emotional data to select the most suitable fashion items.

[0467] The selected fashion items are displayed on the user's smartphone, allowing the user to check the items. The selected items are then delivered to the user based on a regular delivery schedule. The user can try on the received items and choose whether to keep them or return them. This selection information and feedback are then sent back to the server and stored in a database.

[0468] The server updates its AI algorithm based on this feedback and emotional data, which will further optimize item selection for the user from the next time onwards.

[0469] Specific examples

[0470] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional analysis engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[0471] Example prompts for generative AI models

[0472] Write pseudocode for an app that analyzes user-provided image and text data and recommends personalized fashion items based on sentiment data. Use a sentiment analysis engine, artificial intelligence algorithms, and database classes to select items and schedule deliveries based on user preference information and sentiment data. Include user feedback processing.

[0473] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0474] Program processing flow and specific explanation

[0475] Processing Steps

[0476] Step 1:

[0477] Using their smartphones, users enter personal information and data about their preferences, style, and needs, including their name, address, favorite colors and styles, desired item types, etc. This input data is then sent from the device to a server.

[0478] Input: Personal information, preferences, style, requests

[0479] Output: Data sent to the server

[0480] Specific operation: The user fills in the data in the input form on their smartphone and presses the send button, which sends the data to the server.

[0481] Step 2:

[0482] The server stores the received user personal information and preference related data in a database.

[0483] Input: Received data (personal information, preferences, style, requests)

[0484] Output: Data stored in the database

[0485] Specific behavior: The server validates the received data and stores it in the corresponding fields in the database.

[0486] Step 3:

[0487] For emotion analysis, users input image and text data into their devices and send it to the server. The emotion analysis engine receives this data and performs the analysis.

[0488] Input: image, text data

[0489] Output: Parsed emotion data

[0490] How it works: When a user takes a picture with their smartphone camera or enters text and presses the send button, the data is sent to the server. The emotion analysis engine analyzes this data and generates emotion data.

[0491] Step 4:

[0492] The server stores the emotional data obtained from the emotion analysis engine in a database, combines it with the user's personal information and preference data, and runs an artificial intelligence algorithm to select the most suitable fashion items.

[0493] Input: Emotional data, personal information, preference data

[0494] Output: Selected fashion items

[0495] Specific operation: The server stores the emotional data in a database, and inputs the emotional data, personal information, and preference data into an artificial intelligence algorithm to select the most suitable item.

[0496] Step 5:

[0497] The server sends information about the selected fashion items to the user's smartphone and displays it to the user.

[0498] Input: Selected fashion item

[0499] Output: Item information displayed on the user's smartphone

[0500] Specific operation: The server sends information about the selected item to the smartphone, and the item information is displayed on the smartphone screen.

[0501] Step 6:

[0502] The server delivers the selected items to the user based on a regular delivery plan.

[0503] Input: Selected fashion items, delivery plan

[0504] Output: Items delivered to the user

[0505] Specific operation: The server sends the selected item and the user's address information to the delivery system, and delivery is arranged.

[0506] Step 7:

[0507] The user tries on the received item and chooses whether to keep it or return it, and the selection information is sent to the server.

[0508] Input: Information on whether to keep or return

[0509] Output: Selections sent to the server

[0510] Specific operation: The user selects an option on the smartphone screen and presses the send button, which sends the information to the server.

[0511] Step 8:

[0512] The server stores user selections and feedback in a database and uses it to update the artificial intelligence algorithms.

[0513] Input: Selection information, feedback

[0514] Output: Updated artificial intelligence algorithm

[0515] Specific operation: The server stores the selection information and feedback in a database and adjusts and updates the parameters of the artificial intelligence algorithm based on that information.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] [Second embodiment]

[0520] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0521] 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.

[0522] 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).

[0523] 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.

[0524] 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.

[0525] 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).

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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."

[0532] This invention is a subscription-based system that provides optimal fashion coordination tailored to a user's preferences, style, and needs. This system uses an AI algorithm to select fashion items based on information entered by the user and delivers them periodically, allowing the user to easily try on the items at home. Furthermore, the AI ​​continues to learn based on user feedback, making its next suggestions more personalized.

[0533] Registering users and setting preferences

[0534] Users first access the service using their device and register. On the registration page, they enter basic information and verify their email address. They then enter detailed information about their tastes, style, and needs, including their favorite colors, preferred types of items, and special events.

[0535] Sending and storing user information

[0536] The device sends the entered information to the server, which then stores it in a database and manages it as a user profile.

[0537] AI stylist selection

[0538] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This information is then sent to the user's device and displayed.

[0539] Regular deliveries

[0540] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, and the items are packed and delivered as instructed.

[0541] Trying on and holding items

[0542] The user tries on the received item and decides whether to keep it or return it, and then uses the terminal to notify the server of the result, which stores the information in a database.

[0543] Inputting feedback and training the AI

[0544] Users can use their devices to input feedback about the items they receive and send it to the server, which then updates the AI ​​algorithm based on that feedback and reflects it in the next item selection.

[0545] Specific examples

[0546] For example, a user might say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects a casual blue shirt and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user uses their device to notify the server of the results, and the AI ​​learns to make the next selection more in line with the user's preferences.

[0547] As described above, the system of the present invention proposes optimal fashion items that meet the user's preferences and needs, allowing the user to enjoy the latest fashions while reducing the burden on the user.

[0548] The processing flow will be explained below.

[0549] Registration and Preferences

[0550] Process flow:

[0551] Step 1:

[0552] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[0553] Step 2:

[0554] The user enters basic information and clicks the "Register" button.

[0555] Step 3:

[0556] The terminal transmits the input information to the server.

[0557] Step 4:

[0558] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[0559] Step 5:

[0560] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[0561] Step 6:

[0562] The user responds to the questionnaire form and clicks the "Submit" button.

[0563] Step 7:

[0564] The device sends information about preferences, style and needs to the server.

[0565] Step 8:

[0566] The server stores this information in a database and updates the user's profile.

[0567] AI stylist selection

[0568] Process flow:

[0569] Step 1:

[0570] The server periodically retrieves user information from the database.

[0571] Step 2:

[0572] The server uses the information it receives to run AI algorithms to select the best items, taking into account the user's preferences, style, season, temperature, and special events.

[0573] Step 3:

[0574] The server sends information about the selected item to the terminal.

[0575] Step 4:

[0576] The terminal displays the received item information to the user.

[0577] Regular deliveries

[0578] Process flow:

[0579] Step 1:

[0580] The server schedules the next delivery date based on the user's subscription plan.

[0581] Step 2:

[0582] When the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[0583] Step 3:

[0584] The server sends the generated delivery instructions to the warehouse system.

[0585] Step 4:

[0586] The warehouse system receives the instructions and picks and packs the specified items.

[0587] Step 5:

[0588] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[0589] Trying on and holding items

[0590] Process flow:

[0591] Step 1:

[0592] The user receives the delivered item.

[0593] Step 2:

[0594] The user checks the received item list on the terminal.

[0595] Step 3:

[0596] The user tries on the item and decides whether to keep it or return it.

[0597] Step 4:

[0598] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[0599] Step 5:

[0600] The terminal transmits the user's selection information to the server.

[0601] Step 6:

[0602] The server receives the selections, updates the database, and generates return labels for any returned items.

[0603] Inputting feedback and training the AI

[0604] Process flow:

[0605] Step 1:

[0606] The user enters feedback about the item received at the terminal.

[0607] Step 2:

[0608] The user completes their feedback and clicks the "Submit" button.

[0609] Step 3:

[0610] The terminal transmits the feedback information to the server.

[0611] Step 4:

[0612] The server receives the feedback information and stores it in a database.

[0613] Step 5:

[0614] The server updates the AI ​​algorithm based on the new feedback and reflects it in the next item selection.

[0615] The above is a detailed and specific explanation of each processing step, which allows the user to efficiently and accurately receive fashion items that suit their preferences.

[0616] Example 1

[0617] 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."

[0618] Conventional fashion coordination systems did not adequately select items that matched the user's preferences and style, making it difficult to personalize the system to meet the user's needs. Furthermore, there were issues with insufficient regular delivery and improvements based on feedback, resulting in a poor user experience. Furthermore, the AI ​​model did not efficiently suggest or learn appropriate items to select.

[0619] 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.

[0620] In this invention, the server includes means for executing an artificial intelligence model to select optimal items based on user information, means for displaying the selected items to the user, and means for periodically delivering the items based on a subscription plan, thereby enabling provision of personalized fashion items tailored to the user's preferences and style.

[0621] "User information" refers to detailed information entered by a user, such as personal information, preferences, style, and requests.

[0622] "Server" is a computer system that has the ability to receive and store user information and run artificial intelligence models to select the most suitable items for the user.

[0623] An "artificial intelligence model" is a machine learning algorithm that selects the most suitable fashion items based on user information.

[0624] "Means for displaying selected items to the user" refers to a technique or method for transmitting information about the fashion items selected by the server to the user's terminal and displaying it.

[0625] A "subscription plan" is a service contract under which a user receives fashion items delivered to them on a regular basis.

[0626] "Means for periodic delivery" refers to a technique or method by which the server issues delivery instructions to the warehouse system and sends items to the user based on the user's subscription plan.

[0627] "Feedback" is information that provides a user's impressions and evaluations of the items they have received.

[0628] A "prompt sentence" is a specific instruction to the AI ​​model to select fashion items based on the user's preferences and style.

[0629] The present invention is a subscription-based system that provides optimal fashion coordination based on a user's preferences, style, and needs. To build this system, the following hardware and software are used.

[0630] Hardware and software used

[0631] Hardware: User devices (smartphones, tablets, PCs), servers, warehouse systems

[0632] Software: Database management systems (MySQL, PostgreSQL, etc.), AI algorithms (TensorFlow, PyTorch, etc.), web application frameworks (React, Angular, etc.)

[0633] Specific operation of the system

[0634] 1. User Registration and Preferences

[0635] First, a user uses a terminal to access the system's registration page and enter basic information. Then, they answer detailed questions about their preferences and style. For example, by answering questions such as "What is your favorite color?" and "What style do you like?", the user's individual preferences and requests are provided to the system.

[0636] 2. Transmission and storage of user information

[0637] The terminal sends the input information to the server, which stores it in a database and manages it as a user profile.

[0638] 3. AI stylist selection

[0639] The server periodically retrieves user information from the database and uses AI algorithms to select fashion items that best suit the user's preferences and style. For example, it may select items for a particular season or event. The results of the selection are sent to the device and displayed to the user.

[0640] 4. Regular deliveries

[0641] The server schedules the next delivery date based on the user's subscription plan, and when this date approaches, the server issues a delivery instruction to the warehouse system, which then packs and delivers the item to the user.

[0642] 5. Trying on and holding items

[0643] The user tries on the received item and decides whether to keep it or return it. The result is notified to the server from the terminal, and the server stores the information in a database.

[0644] 6. Inputting feedback and training the AI

[0645] Users use their devices to provide feedback about the items they receive, and the server receives this feedback and updates the AI ​​algorithm, which helps ensure that the next item selection is more tailored to the user's preferences.

[0646] Examples and prompts

[0647] For example, based on a user's information such as "I like casual style, I like blue items, and I travel a lot in the summer," the AI ​​will select blue casual shirts and shorts and deliver them to the user periodically. Below is a specific example of such a prompt.

[0648] Prompt Sentence Examples

[0649] User Information:

[0650] 1. Style: Casual

[0651] 2. Favorite color: Blue

[0652] 3. Season: Summer

[0653] 4. Special requirements: I travel a lot

[0654] Prompt the AI ​​model:

[0655] Please suggest the best fashion items for a user who likes casual style, prefers blue items, and travels a lot in the summer.

[0656] The AI ​​model uses this prompt to select fashion items, and the server then processes the delivery based on the results. User feedback can be used to further personalize future recommendations, ensuring that the latest trends are always available.

[0657] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0658] Step 1:

[0659] A user accesses the system using a terminal and enters basic information and information about preferences, style, and needs on a registration page.

[0660] Input: Details such as the user's name, email address, address, preferred color, style, and any special requests

[0661] Specific operation: The user enters information into a form on the device screen and presses the "Submit" button.

[0662] Output: The input information becomes data to be sent from the terminal.

[0663] Step 2:

[0664] The terminal transmits the input user information to the server.

[0665] Input: User information entered in step 1

[0666] Specific operation: The device sends data to the server using the HTTPS protocol.

[0667] Output: The server receives the user information data.

[0668] Step 3:

[0669] The server stores the received user information in a database.

[0670] Input: User information data received in step 2

[0671] What happens: The server executes an SQL query to store the user information in a database (e.g., MySQL or PostgreSQL).

[0672] Output: User information is saved in the database.

[0673] Step 4:

[0674] The server periodically retrieves user information from the database and uses a generative AI model to select the most suitable items.

[0675] Input: User information, season, temperature, special event information retrieved from the database

[0676] Specific operation: The server executes an SQL query to obtain user information, inputs that information into the AI ​​model as a prompt, and the AI ​​model selects an item.

[0677] Output: Selected fashion item information is generated.

[0678] Step 5:

[0679] The server sends the selected item to the user's terminal and displays it.

[0680] Input: Fashion item information generated in step 4

[0681] Specific operation: The server sends the selected item information to the user's device. The device displays the received information.

[0682] Output: The selected item information is displayed on the user's device.

[0683] Step 6:

[0684] The server schedules the next delivery date based on the user's subscription plan and issues a delivery instruction to the warehouse system.

[0685] Input: User's subscription plan information and selected item information

[0686] Specific operation: The server uses the scheduler to set the next delivery date and sends packing and delivery instructions to the warehouse system via API.

[0687] Output: The item is packaged and delivered to the user.

[0688] Step 7:

[0689] The user tries on the received item and uses the terminal to decide whether to keep it or return it.

[0690] Input: User feedback about the item received

[0691] Specific operation: The user selects the option to keep or return using the terminal application and sends the result.

[0692] Output: The selection is sent to the server.

[0693] Step 8:

[0694] The server stores the received feedback and selection information in a database and updates the AI ​​model to reflect this in the next item selection.

[0695] Input: Feedback and selections received from users

[0696] How it works: The server stores the feedback information in a database and uses that data to retrain the AI ​​model.

[0697] Output: The next selection made by the updated AI model will be more in line with the user's preferences.

[0698] (Application example 1)

[0699] 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."

[0700] Conventional fashion coordination suggestion systems lack sufficient personalization based on the user's preferences, style, and needs, making it difficult to provide appropriate suggestions. Furthermore, there is a lack of technology to efficiently utilize user feedback, limiting the improvement of user satisfaction. Furthermore, there is a need for a method that allows users to easily try on suggested items and choose whether to keep them or return them.

[0701] 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.

[0702] In this invention, the server includes a means for allowing a user to input preferences and feedback using a smartphone, and for AI to select and suggest personalized fashion items based on the input, a means for transmitting information about the user and information about preferences, style, and needs to the server, and a means for executing an AI algorithm to select optimal items based on the user information, thereby enabling efficient input of user information and provision of personalized fashion suggestions.

[0703] The "means for inputting user information" refers to an interface that allows a user to input information such as their personal information, preferences, style, and requests into the system.

[0704] The "means for entering information about the user's preferences, style, and requests" is a function that allows the user to enter detailed information about specific fashion preferences, style, requests regarding events, and the like.

[0705] The "means for transmitting the user's information and information regarding preferences, style, and needs to the server" refers to a communication means for transmitting the information input by the user to the central server.

[0706] "Means for executing an AI algorithm to select optimal items based on user information" refers to a system that runs on a server and executes an algorithm to select fashion items that match the user's preferences, style, and requests.

[0707] The "means for displaying selected items to the user" refers to an interface for displaying the fashion items selected by the AI ​​algorithm on the user's device.

[0708] "Means for periodically delivering items based on a subscription plan" refers to the procedures and means for periodically delivering fashion items to a user's address based on a plan selected by the user.

[0709] "A means for users to provide feedback on the items they receive and update the AI ​​algorithm based on that feedback" refers to a system in which users input their impressions and ratings of the items they try on, and that information is used to improve and update the AI ​​algorithm.

[0710] "A means for AI to select and suggest personalized fashion items based on the input of user preferences and feedback using a smartphone" refers to a system in which users input preferences and feedback through a smartphone application, and AI selects and suggests personalized fashion items based on that information.

[0711] This invention relates to a subscription system that proposes and periodically delivers optimal fashion items based on a user's preferences, style, and needs. The system consists of multiple components, including a smartphone application, a server, and an AI algorithm.

[0712] User registration and information entry

[0713] Users first access the service using their smartphone and register. On the registration page, they enter basic information and verify their email address. Next, users enter detailed information about their preferences, style, and needs, including favorite colors, preferred types of items, and special events.

[0714] Sending and storing information

[0715] The information entered by the user is sent from the smartphone to a central server, which then stores it in a database. The stored information is managed as a user profile and used for subsequent processing.

[0716] Item selection by AI algorithm

[0717] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This selection information is then sent to the user's smartphone and displayed.

[0718] Regular deliveries

[0719] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, which then packs the items and delivers them to the user's address.

[0720] Trying on items and giving feedback

[0721] The user tries on the received item and decides whether to keep it or return it. They can then use their smartphone to notify the server of their decision. The server stores this information in a database and uses it to select items for the next purchase. The user can also enter feedback about the received item and send it back to the server. The server uses this feedback to retrain the AI ​​algorithm and make the next recommendation even more personalized.

[0722] Hardware and software used

[0723] Hardware: Smartphones, servers, database systems (e.g., PostgreSQL)

[0724] Software: Smartphone application, Python code for server-side processing (e.g., Flask), AI algorithms (e.g., TensorFlow or PyTorch)

[0725] Specific examples

[0726] For example, a user may say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects blue casual shirts and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user then notifies the server of the results using their smartphone, allowing the AI ​​to learn so that the next selection will better suit the user's preferences.

[0727] Prompt Sentence Examples

[0728] "Suggest fashion items for a summer trip for a user who likes casual style and is looking for something blue."

[0729] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0730] Step 1:

[0731] Users access the service using their smartphones and enter basic information as well as detailed information about their preferences, style and desires.

[0732] Input: User information (name, email address, favorite colors, style, special events, etc.)

[0733] Output: The entered user information is saved in the smartphone application.

[0734] Step 2:

[0735] The entered user information is sent from the smartphone to the server.

[0736] Input: User information on smartphone

[0737] Output: The server receives the user information and stores it in the database.

[0738] Step 3:

[0739] The server periodically retrieves user information from the database and runs an AI algorithm to select fashion items that best suit the user's preferences and style.

[0740] Input: User information stored in the database

[0741] Output: A list of fashion items selected by an AI algorithm

[0742] Step 4:

[0743] The selected fashion items are sent to a smartphone and displayed to the user.

[0744] Input: A list of items selected by an AI algorithm

[0745] Output: Item list displayed on smartphone

[0746] Step 5:

[0747] The server schedules the next delivery based on the user's subscription plan and generates delivery instructions for the warehouse system.

[0748] Input: Subscription plan information, selected item list

[0749] Output: Delivery schedule information, delivery instructions

[0750] Step 6:

[0751] The user tries on the received item, chooses on their smartphone whether to keep it or return it, and notifies the server of the result.

[0752] Input: Choice of keeping or returning the item you tried on

[0753] Output: Selections sent to the server

[0754] Step 7:

[0755] The server stores the selection information in a database and uses it as feedback to reflect in the next item selection. User feedback information is also sent to the server and used as data for retraining the AI ​​algorithm.

[0756] Input: Selection information, feedback information

[0757] Output: Database update, AI algorithm retraining

[0758] Through the above processing steps, the system will continually suggest optimal fashion items based on the user's preferences and feedback.

[0759] 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.

[0760] This invention is a fashion coordination provision system that combines an emotion engine that recognizes the user's emotions, achieving a high level of personalization based not only on the user's preferences and requests but also on their emotions. By utilizing the information entered by the user as well as the emotional data analyzed by the emotion engine, the AI ​​algorithm selects the most suitable fashion items for the user and delivers them periodically. This allows users to easily try on items at home and enjoy highly personalized coordination.

[0761] Registering users and setting preferences

[0762] Using their device, users access the service's registration page. They create an account by entering basic information such as their name, email address, and password. They also enter detailed information about their preferences, style, and needs, including favorite colors, types of items they like, and special events. This information is sent via the device to the server and stored in a database.

[0763] AI stylist selection and user emotion recognition

[0764] The server periodically retrieves the user's basic information and preference information from the database. In addition, it uses an emotion engine to analyze the user's emotion data. The emotion engine recognizes and analyzes emotions from images, videos, text, etc. input or provided by the user. The analysis results are sent to the server.

[0765] Optimal item selection

[0766] The AI ​​algorithm on the server selects the most suitable fashion items based on the user's basic information, preferences, and emotional data. The AI ​​algorithm also takes into consideration the season, temperature, special events, and other factors when selecting items. The results of this selection are sent to the device and displayed to the user.

[0767] Regular deliveries

[0768] The server schedules the next delivery date based on the user's subscription plan. When the delivery date approaches, the server generates a delivery instruction for the warehouse system, and the specified items are picked and packed. The items are then handed over to a delivery company for delivery to the user.

[0769] Trying on and holding items

[0770] The user receives the delivered items and tries them on. The user checks the item list on the terminal and decides whether to keep or return the items after trying them on. The result is notified to the server via the terminal, and the server updates the database.

[0771] Feedback and sentiment data input

[0772] The user enters feedback about the item they received on the device, including their opinion on size, color, and style. The user's emotional data is also collected. This information is then sent to the server and stored in a database.

[0773] AI learning and item selection optimization

[0774] The server updates the AI ​​algorithm based on the newly received feedback and emotional data, taking into account the user's emotional tendencies and optimizing the next item selection. For example, if a user expresses strong emotions about a particular item, that information will be reflected in the next selection.

[0775] Specific examples

[0776] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[0777] As described above, the system of the present invention realizes a high level of personalization that takes into consideration the user's emotions, and provides efficient and appropriate fashion coordination.

[0778] The processing flow will be explained below.

[0779] Registering users and setting preferences

[0780] Process flow:

[0781] Step 1:

[0782] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[0783] Step 2:

[0784] The user enters basic information and clicks the "Register" button.

[0785] Step 3:

[0786] The terminal transmits the input information to the server.

[0787] Step 4:

[0788] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[0789] Step 5:

[0790] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[0791] Step 6:

[0792] The user responds to the questionnaire form and clicks the "Submit" button.

[0793] Step 7:

[0794] The device sends information about preferences, style and needs to the server.

[0795] Step 8:

[0796] The server stores this information in a database and updates the user's profile.

[0797] AI stylist selection and user emotion recognition

[0798] Process flow:

[0799] Step 1:

[0800] The server periodically retrieves the user's basic information and preferences from the database.

[0801] Step 2:

[0802] The server activates an emotion engine and analyzes data such as images, videos, and text provided by the user to recognize emotions.

[0803] Step 3:

[0804] The emotion engine analyzes the user's emotions and sends the results to the server.

[0805] Step 4:

[0806] The AI ​​algorithm on the server selects the most suitable items based on the user's basic information, preferences, and emotional data.

[0807] Step 5:

[0808] The server sends information about the selected item to the terminal.

[0809] Step 6:

[0810] The terminal displays the received item information to the user.

[0811] Regular deliveries

[0812] Process flow:

[0813] Step 1:

[0814] The server schedules the next delivery date based on the user's subscription plan.

[0815] Step 2:

[0816] As the delivery date approaches, the server generates delivery instructions for the warehouse system.

[0817] Step 3:

[0818] The server sends the generated delivery instructions to the warehouse system.

[0819] Step 4:

[0820] The warehouse system receives the instructions and picks and packs the specified items.

[0821] Step 5:

[0822] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[0823] Trying on and holding items

[0824] Process flow:

[0825] Step 1:

[0826] The user receives the delivered item.

[0827] Step 2:

[0828] The user checks the received item list on the terminal.

[0829] Step 3:

[0830] The user tries on the item and decides whether to keep it or return it.

[0831] Step 4:

[0832] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[0833] Step 5:

[0834] The terminal transmits the user's selection information to the server.

[0835] Step 6:

[0836] The server receives the selections, updates the database, and generates return labels for any returned items.

[0837] Feedback and sentiment data input

[0838] Process flow:

[0839] Step 1:

[0840] The user enters feedback about the received item at the terminal.

[0841] Step 2:

[0842] The user completes their feedback and clicks the "Submit" button.

[0843] Step 3:

[0844] The terminal transmits the feedback information to the server.

[0845] Step 4:

[0846] The server receives the feedback information and stores it in a database.

[0847] Step 5:

[0848] The server restarts the emotion engine and collects and analyzes the user's emotion data.

[0849] Step 6:

[0850] The emotion engine sends the analysis results to the server.

[0851] AI learning and item selection optimization

[0852] Process flow:

[0853] Step 1:

[0854] The server updates its AI algorithms based on newly received feedback and emotional data.

[0855] Step 2:

[0856] The AI ​​algorithm takes into account the user's emotional tendencies to further optimize the next item selection.

[0857] Step 3:

[0858] The server will perform new item selection based on the updated AI algorithm.

[0859] Step 4:

[0860] Information about the selected item is sent to the terminal and displayed to the user.

[0861] Specific examples

[0862] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on the subscription plan. The user can try them on at home, keep them, and return them, and the feedback and emotional data will be reflected in the next item selection.

[0863] The above is a detailed processing flow of the system that includes an emotion engine that recognizes the user's emotions. This enables advanced personalization that takes the user's emotions into consideration, providing efficient and appropriate fashion coordination.

[0864] Example 2

[0865] 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."

[0866] Conventional fashion coordination systems only select items based on the user's preferences, style, and needs, but do not provide personalization that takes into account the user's emotions. Furthermore, because the selected items do not necessarily match the user's emotions or mood, it is difficult to improve user satisfaction. Furthermore, updating the AI ​​algorithm based on feedback is inefficient, resulting in insufficient optimization of the next item selection.

[0867] 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.

[0868] In this invention, the server includes means for using an emotion recognition engine to analyze user input data and emotion data, means for executing an AI algorithm to select optimal items based on the analysis results, and means for learning and updating the AI ​​algorithm based on user feedback and emotion data. This enables advanced personalization tailored to the user's emotions, improving the accuracy of item selection and user satisfaction.

[0869] "User information" refers to basic personal information such as the user's name, email address, and password.

[0870] "Information about preferences, styles, and needs" is detailed information such as the user's favorite colors, types of items, and needs for special events.

[0871] The "server" is a computer system that receives user information and preference information, stores it in a database, and then selects items using AI algorithms and emotion recognition engines.

[0872] An "emotion recognition engine" is a program that analyzes emotions from images, videos, text, etc. provided by the user and generates emotional data.

[0873] "AI algorithm" is an artificial intelligence technology that selects the most suitable items based on the user's basic information, preferences, and emotional data.

[0874] "Feedback" refers to information about opinions and ratings regarding the size, color, and style of the items the user has tried on.

[0875] A "subscription plan" is a service agreement under which a user receives items on a regular basis, including the frequency and terms of delivery.

[0876] MODE FOR CARRYING OUT THE INVENTION

[0877] The present invention is a fashion coordination system that combines an emotion recognition engine that recognizes the user's emotions. This system realizes advanced personalization based on emotions as well as the user's preferences and requests.

[0878] First, a user uses their device to access the service's registration page and create an account by entering basic information such as their name, email address, and password. Next, the user enters detailed information about their preferences, style, and needs. This information is sent via the device to a server and stored in a database.

[0879] The server uses an emotion recognition engine (e.g., Google Cloud Vision API or IBM Watson) to analyze emotions from images, videos, and text provided by the user and generate emotion data. The analysis results are sent to the server and stored in a database.

[0880] The AI ​​algorithm selects the most suitable fashion items based on the user's basic information, preferences, and emotional data stored on the server. This selection is performed using a generative AI model. For example, open-source machine learning libraries such as TensorFlow and PyTorch are used as generative AI models. The selected items are then sent to the device and displayed to the user.

[0881] The server schedules the next delivery date based on the user's subscription plan. As the delivery date approaches, the server generates a delivery instruction for the warehouse system. The warehouse system picks, packs, and delivers the specified items to a delivery company. The items are then delivered to the user.

[0882] The user receives the delivered item and tries it on. After trying it on, the user uses a terminal to select whether to keep it or return it. This selection information is sent to the server, which updates the database.

[0883] Users can use the device to provide feedback on the items they receive, including opinions on size, color, and style. In addition, emotional data is collected and sent to the server where it is stored in a database.

[0884] The server updates the AI ​​algorithm based on new feedback and emotional data. The generative AI model further refines personalization based on the user's emotional tendencies, resulting in a more optimized selection of items for the next round.

[0885] For example, a user might input, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion recognition engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). An example of a prompt sentence might be, "The user likes a casual style and would like me to select blue items. However, I'm currently feeling stressed, so I'd like you to recommend some relaxing fashion." This is input to the generative AI model.

[0886] As described above, the system of the present invention realizes a high level of personalization that takes into account the user's emotions, and provides efficient and accurate fashion coordination. This system aims to improve user satisfaction and is capable of always providing optimal coordination.

[0887] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0888] Step 1: User Registration

[0889] The user accesses the service's registration page using a device and enters basic information such as name, email address, and password. The device then sends this information to the server, which then stores it in a database.

[0890] Input: User's name, email address, and password

[0891] Output: Basic information of the user stored on the server

[0892] Specific operation: The user enters information into the input form and clicks the "Register" button. The device sends the input data to the server via a POST request. The server saves the information in the database and returns a message to the device indicating registration is complete.

[0893] Step 2: Set your preferences

[0894] Users enter details about their preferences, style and needs, and the device sends this information to the server, which stores it in a database.

[0895] Input: User preferences (color, item type, special events, etc.)

[0896] Output: User details stored on the server

[0897] How it works: The user enters their preferred information by manipulating multiple checkboxes and text fields on the device screen. The device then sends this data to the server, which stores the information in a database.

[0898] Step 3: Emotion Recognition

[0899] The server uses an emotion recognition engine to analyze emotions from images, videos, and text provided by the user, and the analysis results are stored on the server as emotion data.

[0900] Input: User-uploaded images, videos, and text

[0901] Output: Parsed emotion data

[0902] How it works: A user uploads images, videos, and text using their device. The device then sends this data to the server, which then uses an emotion recognition engine to analyze the emotions and records the results in a database.

[0903] Step 4: Selecting items

[0904] The AI ​​algorithm runs on the server and combines the user's basic information, preferences, and emotional data to select the most suitable fashion items. The results are then sent to the device and displayed to the user.

[0905] Input: User's basic information, preference information, emotional data

[0906] Output: A list of selected fashion items

[0907] Specific operation: The server retrieves registered user information from the database and selects items using a generative AI model. The selection results are sent to the device and displayed on the device.

[0908] Step 5: Arrange shipping

[0909] The server schedules the next delivery date based on the subscription plan, and when the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[0910] Input: User's subscription plan

[0911] Output: Delivery instructions, picking list

[0912] Specific operation: The server checks the delivery schedule, sends an API request to the warehouse management system, and creates a picking list. Warehouse workers pick items based on the list, pack them, and hand them over to the delivery company.

[0913] Step 6: Try on the item and decide whether to keep it

[0914] The user receives the delivered item, tries it on, and selects whether to keep it or return it. This selection information is then sent to the server, which updates the database.

[0915] Input: User's retention or return decision

[0916] Output: Updated retention information on the server

[0917] Specific operation: The user clicks the confirmation button on the device and selects the items to keep and the items to return. The device sends this information to the server, which updates the database.

[0918] Step 7: Collect feedback and sentiment data

[0919] Users input their feedback about the items they receive on the device, and emotional data is collected. This information is then sent to the server and stored in a database.

[0920] Input: User feedback, emotional data

[0921] Output: Feedback and emotion data stored in a database

[0922] Specific operation: The user enters their opinion and evaluation score in the feedback form on the device and uploads emotional data (e.g., photos and text). This data is sent to the server, which then stores it in a database.

[0923] Step 8: Training the AI ​​algorithm

[0924] The server updates the AI ​​algorithm based on new feedback and emotion data, which further optimizes the next item selection.

[0925] Input: User feedback, emotional data

[0926] Output: Updated AI algorithm

[0927] How it works: The server inputs the received data into the generative AI model and uses it as new training data. The AI ​​algorithm learns and improves the accuracy of the next item selection.

[0928] (Application example 2)

[0929] 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."

[0930] Conventional fashion coordination systems provide personalization based on the user's preferences, style, and needs, but do not provide advanced personalization based on the user's emotions. This makes it difficult for users to select the optimal fashion items that match their emotions at any given time, and this has led to the issue of not being able to sufficiently increase user satisfaction.

[0931] 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.

[0932] In this invention, the server includes means for executing an artificial intelligence algorithm for selecting optimal items based on the user's personal information and emotion data obtained by an emotion analysis engine, means for displaying the selected items to the user, and means for delivering the items to the user based on a regular delivery schedule. This enables advanced personalization based on the user's emotions and makes it possible to provide the user with the most optimal fashion items.

[0933] Definition of each word

[0934] "Personal information" refers to information that identifies an individual, such as the user's name, address, email address, and telephone number.

[0935] "Preferences" is information about the user's preferences such as colors, styles, and fashion items.

[0936] "Style" is information about the type and design of fashion that the user normally wears.

[0937] "Request" is information about items or services that a user desires for a specific event or purpose.

[0938] "Data" refers to any information processed by the system, including users' personal information, preferences, style, needs, and emotional data.

[0939] A "server" is a computer system for storing and processing data sent by users.

[0940] An "emotion analysis engine" is software that analyzes a user's emotions from input data such as images and text provided by the user and generates data.

[0941] "Emotion data" is information about the user's emotional state obtained by the emotion analysis engine.

[0942] An "artificial intelligence algorithm" is a computer program that selects items suitable for a user through a process of data analysis and learning.

[0943] The "display means" is a function for displaying information about the selected item on the user's terminal screen.

[0944] A "delivery plan" is a schedule for periodically sending selected items to a user.

[0945] "Feedback" refers to the thoughts and opinions of users about the items they receive, which helps improve the system.

[0946] "Mode for Carrying Out the Invention"

[0947] This invention is a system that analyzes a user's emotions and suggests fashion coordination. The system collects and analyzes the user's emotional data, selects optimal fashion items based on that data, and delivers them to the user on a regular basis. The configuration and operation of this system are described below.

[0948] Hardware and software used

[0949] Hardware:

[0950] Smartphone: A device that allows users to input information and interact with systems.

[0951] Server: A system for storing data and running AI algorithms and sentiment analysis engines.

[0952] software:

[0953] Emotion analysis engine (EmotionEngine): Software that analyzes images and text provided by users and generates emotional data.

[0954] Artificial intelligence algorithm (FashionAI): An algorithm that selects fashion items based on a user's personal information, preferences, style, and emotional data.

[0955] Database: Stores and manages users' personal information, preferences, style, feedback, and emotional data.

[0956] Delivery System: Software that executes a schedule for the periodic delivery of selected items to users.

[0957] System Overview

[0958] The server receives personal information and preference data entered by the user and stores the data in a database. The user's emotional data is extracted from the images and text provided by the user using an emotional analysis engine. The server then runs an artificial intelligence algorithm based on the user's personal information, preferences, style, and emotional data to select the most suitable fashion items.

[0959] The selected fashion items are displayed on the user's smartphone, allowing the user to check the items. The selected items are then delivered to the user based on a regular delivery schedule. The user can try on the received items and choose whether to keep them or return them. This selection information and feedback are then sent back to the server and stored in a database.

[0960] The server updates its AI algorithm based on this feedback and emotional data, which will further optimize item selection for the user from the next time onwards.

[0961] Specific examples

[0962] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional analysis engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[0963] Example prompts for generative AI models

[0964] Write pseudocode for an app that analyzes user-provided image and text data and recommends personalized fashion items based on sentiment data. Use a sentiment analysis engine, artificial intelligence algorithms, and database classes to select items and schedule deliveries based on user preference information and sentiment data. Include user feedback processing.

[0965] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0966] Program processing flow and specific explanation

[0967] Processing Steps

[0968] Step 1:

[0969] Using their smartphones, users enter personal information and data about their preferences, style, and needs, including their name, address, favorite colors and styles, desired item types, etc. This input data is then sent from the device to a server.

[0970] Input: Personal information, preferences, style, requests

[0971] Output: Data sent to the server

[0972] Specific operation: The user fills in the data in the input form on their smartphone and presses the send button, which sends the data to the server.

[0973] Step 2:

[0974] The server stores the received user personal information and preference related data in a database.

[0975] Input: Received data (personal information, preferences, style, requests)

[0976] Output: Data stored in the database

[0977] Specific behavior: The server validates the received data and stores it in the corresponding fields in the database.

[0978] Step 3:

[0979] For emotion analysis, users input image and text data into their devices and send it to the server. The emotion analysis engine receives this data and performs the analysis.

[0980] Input: image, text data

[0981] Output: Parsed emotion data

[0982] How it works: When a user takes a picture with their smartphone camera or enters text and presses the send button, the data is sent to the server. The emotion analysis engine analyzes this data and generates emotion data.

[0983] Step 4:

[0984] The server stores the emotional data obtained from the emotion analysis engine in a database, combines it with the user's personal information and preference data, and runs an artificial intelligence algorithm to select the most suitable fashion items.

[0985] Input: Emotional data, personal information, preference data

[0986] Output: Selected fashion items

[0987] Specific operation: The server stores the emotional data in a database, and inputs the emotional data, personal information, and preference data into an artificial intelligence algorithm to select the most suitable item.

[0988] Step 5:

[0989] The server sends information about the selected fashion items to the user's smartphone and displays it to the user.

[0990] Input: Selected fashion item

[0991] Output: Item information displayed on the user's smartphone

[0992] Specific operation: The server sends information about the selected item to the smartphone, and the item information is displayed on the smartphone screen.

[0993] Step 6:

[0994] The server delivers the selected items to the user based on a regular delivery plan.

[0995] Input: Selected fashion items, delivery plan

[0996] Output: Items delivered to the user

[0997] Specific operation: The server sends the selected item and the user's address information to the delivery system, and delivery is arranged.

[0998] Step 7:

[0999] The user tries on the received item and chooses whether to keep it or return it, and the selection information is sent to the server.

[1000] Input: Information on whether to keep or return

[1001] Output: Selections sent to the server

[1002] Specific operation: The user selects an option on the smartphone screen and presses the send button, which sends the information to the server.

[1003] Step 8:

[1004] The server stores user selections and feedback in a database and uses it to update the artificial intelligence algorithms.

[1005] Input: Selection information, feedback

[1006] Output: Updated artificial intelligence algorithm

[1007] Specific operation: The server stores the selection information and feedback in a database and adjusts and updates the parameters of the artificial intelligence algorithm based on that information.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] [Third embodiment]

[1012] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1013] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1014] 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).

[1015] 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.

[1016] 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.

[1017] 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).

[1018] 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.

[1019] 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.

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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."

[1024] This invention is a subscription-based system that provides optimal fashion coordination tailored to a user's preferences, style, and needs. This system uses an AI algorithm to select fashion items based on information entered by the user and delivers them periodically, allowing the user to easily try on the items at home. Furthermore, the AI ​​continues to learn based on user feedback, making its next suggestions more personalized.

[1025] Registering users and setting preferences

[1026] Users first access the service using their device and register. On the registration page, they enter basic information and verify their email address. They then enter detailed information about their tastes, style, and needs, including their favorite colors, preferred types of items, and special events.

[1027] Sending and storing user information

[1028] The device sends the entered information to the server, which then stores it in a database and manages it as a user profile.

[1029] AI stylist selection

[1030] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This information is then sent to the user's device and displayed.

[1031] Regular deliveries

[1032] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, and the items are packed and delivered as instructed.

[1033] Trying on and holding items

[1034] The user tries on the received item and decides whether to keep it or return it, and then uses the terminal to notify the server of the result, which stores the information in a database.

[1035] Inputting feedback and training the AI

[1036] Users can use their devices to input feedback about the items they receive and send it to the server, which then updates the AI ​​algorithm based on that feedback and reflects it in the next item selection.

[1037] Specific examples

[1038] For example, a user might say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects a casual blue shirt and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user uses their device to notify the server of the results, and the AI ​​learns to make the next selection more in line with the user's preferences.

[1039] As described above, the system of the present invention proposes optimal fashion items that meet the user's preferences and needs, allowing the user to enjoy the latest fashions while reducing the burden on the user.

[1040] The processing flow will be explained below.

[1041] Registration and Preferences

[1042] Process flow:

[1043] Step 1:

[1044] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[1045] Step 2:

[1046] The user enters basic information and clicks the "Register" button.

[1047] Step 3:

[1048] The terminal transmits the input information to the server.

[1049] Step 4:

[1050] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[1051] Step 5:

[1052] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[1053] Step 6:

[1054] The user responds to the questionnaire form and clicks the "Submit" button.

[1055] Step 7:

[1056] The device sends information about preferences, style and needs to the server.

[1057] Step 8:

[1058] The server stores this information in a database and updates the user's profile.

[1059] AI stylist selection

[1060] Process flow:

[1061] Step 1:

[1062] The server periodically retrieves user information from the database.

[1063] Step 2:

[1064] The server uses the information it receives to run AI algorithms to select the best items, taking into account the user's preferences, style, season, temperature, and special events.

[1065] Step 3:

[1066] The server sends information about the selected item to the terminal.

[1067] Step 4:

[1068] The terminal displays the received item information to the user.

[1069] Regular deliveries

[1070] Process flow:

[1071] Step 1:

[1072] The server schedules the next delivery date based on the user's subscription plan.

[1073] Step 2:

[1074] When the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[1075] Step 3:

[1076] The server sends the generated delivery instructions to the warehouse system.

[1077] Step 4:

[1078] The warehouse system receives the instructions and picks and packs the specified items.

[1079] Step 5:

[1080] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[1081] Trying on and holding items

[1082] Process flow:

[1083] Step 1:

[1084] The user receives the delivered item.

[1085] Step 2:

[1086] The user checks the received item list on the terminal.

[1087] Step 3:

[1088] The user tries on the item and decides whether to keep it or return it.

[1089] Step 4:

[1090] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[1091] Step 5:

[1092] The terminal transmits the user's selection information to the server.

[1093] Step 6:

[1094] The server receives the selections, updates the database, and generates return labels for any returned items.

[1095] Inputting feedback and training the AI

[1096] Process flow:

[1097] Step 1:

[1098] The user enters feedback about the item received at the terminal.

[1099] Step 2:

[1100] The user completes their feedback and clicks the "Submit" button.

[1101] Step 3:

[1102] The terminal transmits the feedback information to the server.

[1103] Step 4:

[1104] The server receives the feedback information and stores it in a database.

[1105] Step 5:

[1106] The server updates the AI ​​algorithm based on the new feedback and reflects it in the next item selection.

[1107] The above is a detailed and specific explanation of each processing step, which allows the user to efficiently and accurately receive fashion items that suit their preferences.

[1108] Example 1

[1109] 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."

[1110] Conventional fashion coordination systems did not adequately select items that matched the user's preferences and style, making it difficult to personalize the system to meet the user's needs. Furthermore, there were issues with insufficient regular delivery and improvements based on feedback, resulting in a poor user experience. Furthermore, the AI ​​model did not efficiently suggest or learn appropriate items to select.

[1111] 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.

[1112] In this invention, the server includes means for executing an artificial intelligence model to select optimal items based on user information, means for displaying the selected items to the user, and means for periodically delivering the items based on a subscription plan, thereby enabling provision of personalized fashion items tailored to the user's preferences and style.

[1113] "User information" refers to detailed information entered by a user, such as personal information, preferences, style, and requests.

[1114] "Server" is a computer system that has the ability to receive and store user information and run artificial intelligence models to select the most suitable items for the user.

[1115] An "artificial intelligence model" is a machine learning algorithm that selects the most suitable fashion items based on user information.

[1116] "Means for displaying selected items to the user" refers to a technique or method for transmitting information about the fashion items selected by the server to the user's terminal and displaying it.

[1117] A "subscription plan" is a service contract under which a user receives fashion items delivered to them on a regular basis.

[1118] "Means for periodic delivery" refers to a technique or method by which the server issues delivery instructions to the warehouse system and sends items to the user based on the user's subscription plan.

[1119] "Feedback" is information that provides a user's impressions and evaluations of the items they have received.

[1120] A "prompt sentence" is a specific instruction to the AI ​​model to select fashion items based on the user's preferences and style.

[1121] The present invention is a subscription-based system that provides optimal fashion coordination based on a user's preferences, style, and needs. To build this system, the following hardware and software are used.

[1122] Hardware and software used

[1123] Hardware: User devices (smartphones, tablets, PCs), servers, warehouse systems

[1124] Software: Database management systems (MySQL, PostgreSQL, etc.), AI algorithms (TensorFlow, PyTorch, etc.), web application frameworks (React, Angular, etc.)

[1125] Specific operation of the system

[1126] 1. User Registration and Preferences

[1127] First, a user uses a terminal to access the system's registration page and enter basic information. Then, they answer detailed questions about their preferences and style. For example, by answering questions such as "What is your favorite color?" and "What style do you like?", the user's individual preferences and requests are provided to the system.

[1128] 2. Transmission and storage of user information

[1129] The terminal sends the input information to the server, which stores it in a database and manages it as a user profile.

[1130] 3. AI stylist selection

[1131] The server periodically retrieves user information from the database and uses AI algorithms to select fashion items that best suit the user's preferences and style. For example, it may select items for a particular season or event. The results of the selection are sent to the device and displayed to the user.

[1132] 4. Regular deliveries

[1133] The server schedules the next delivery date based on the user's subscription plan, and when this date approaches, the server issues a delivery instruction to the warehouse system, which then packs and delivers the item to the user.

[1134] 5. Trying on and holding items

[1135] The user tries on the received item and decides whether to keep it or return it. The result is notified to the server from the terminal, and the server stores the information in a database.

[1136] 6. Inputting feedback and training the AI

[1137] Users use their devices to provide feedback about the items they receive, and the server receives this feedback and updates the AI ​​algorithm, which helps ensure that the next item selection is more tailored to the user's preferences.

[1138] Examples and prompts

[1139] For example, based on a user's information such as "I like casual style, I like blue items, and I travel a lot in the summer," the AI ​​will select blue casual shirts and shorts and deliver them to the user periodically. Below is a specific example of such a prompt.

[1140] Prompt Sentence Examples

[1141] User Information:

[1142] 1. Style: Casual

[1143] 2. Favorite color: Blue

[1144] 3. Season: Summer

[1145] 4. Special requirements: I travel a lot

[1146] Prompt the AI ​​model:

[1147] Please suggest the best fashion items for a user who likes casual style, prefers blue items, and travels a lot in the summer.

[1148] The AI ​​model uses this prompt to select fashion items, and the server then processes the delivery based on the results. User feedback can be used to further personalize future recommendations, ensuring that the latest trends are always available.

[1149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1150] Step 1:

[1151] A user accesses the system using a terminal and enters basic information and information about preferences, style, and needs on a registration page.

[1152] Input: Details such as the user's name, email address, address, preferred color, style, and any special requests

[1153] Specific operation: The user enters information into a form on the device screen and presses the "Submit" button.

[1154] Output: The input information becomes data to be sent from the terminal.

[1155] Step 2:

[1156] The terminal transmits the input user information to the server.

[1157] Input: User information entered in step 1

[1158] Specific operation: The device sends data to the server using the HTTPS protocol.

[1159] Output: The server receives the user information data.

[1160] Step 3:

[1161] The server stores the received user information in a database.

[1162] Input: User information data received in step 2

[1163] What happens: The server executes an SQL query to store the user information in a database (e.g., MySQL or PostgreSQL).

[1164] Output: User information is saved in the database.

[1165] Step 4:

[1166] The server periodically retrieves user information from the database and uses a generative AI model to select the most suitable items.

[1167] Input: User information, season, temperature, special event information retrieved from the database

[1168] Specific operation: The server executes an SQL query to obtain user information, inputs that information into the AI ​​model as a prompt, and the AI ​​model selects an item.

[1169] Output: Selected fashion item information is generated.

[1170] Step 5:

[1171] The server sends the selected item to the user's terminal and displays it.

[1172] Input: Fashion item information generated in step 4

[1173] Specific operation: The server sends the selected item information to the user's device. The device displays the received information.

[1174] Output: The selected item information is displayed on the user's device.

[1175] Step 6:

[1176] The server schedules the next delivery date based on the user's subscription plan and issues a delivery instruction to the warehouse system.

[1177] Input: User's subscription plan information and selected item information

[1178] Specific operation: The server uses the scheduler to set the next delivery date and sends packing and delivery instructions to the warehouse system via API.

[1179] Output: The item is packaged and delivered to the user.

[1180] Step 7:

[1181] The user tries on the received item and uses the terminal to decide whether to keep it or return it.

[1182] Input: User feedback about the item received

[1183] Specific operation: The user selects the option to keep or return using the terminal application and sends the result.

[1184] Output: The selection is sent to the server.

[1185] Step 8:

[1186] The server stores the received feedback and selection information in a database and updates the AI ​​model to reflect this in the next item selection.

[1187] Input: Feedback and selections received from users

[1188] How it works: The server stores the feedback information in a database and uses that data to retrain the AI ​​model.

[1189] Output: The next selection made by the updated AI model will be more in line with the user's preferences.

[1190] (Application example 1)

[1191] 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."

[1192] Conventional fashion coordination suggestion systems lack sufficient personalization based on the user's preferences, style, and needs, making it difficult to provide appropriate suggestions. Furthermore, there is a lack of technology to efficiently utilize user feedback, limiting the improvement of user satisfaction. Furthermore, there is a need for a method that allows users to easily try on suggested items and choose whether to keep them or return them.

[1193] 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.

[1194] In this invention, the server includes a means for allowing a user to input preferences and feedback using a smartphone, and for AI to select and suggest personalized fashion items based on the input, a means for transmitting information about the user and information about preferences, style, and needs to the server, and a means for executing an AI algorithm to select optimal items based on the user information, thereby enabling efficient input of user information and provision of personalized fashion suggestions.

[1195] The "means for inputting user information" refers to an interface that allows a user to input information such as their personal information, preferences, style, and requests into the system.

[1196] The "means for entering information about the user's preferences, style, and requests" is a function that allows the user to enter detailed information about specific fashion preferences, style, requests regarding events, and the like.

[1197] The "means for transmitting the user's information and information regarding preferences, style, and needs to the server" refers to a communication means for transmitting the information input by the user to the central server.

[1198] "Means for executing an AI algorithm to select optimal items based on user information" refers to a system that runs on a server and executes an algorithm to select fashion items that match the user's preferences, style, and requests.

[1199] The "means for displaying selected items to the user" refers to an interface for displaying the fashion items selected by the AI ​​algorithm on the user's device.

[1200] "Means for periodically delivering items based on a subscription plan" refers to the procedures and means for periodically delivering fashion items to a user's address based on a plan selected by the user.

[1201] "A means for users to provide feedback on the items they receive and update the AI ​​algorithm based on that feedback" refers to a system in which users input their impressions and ratings of the items they try on, and that information is used to improve and update the AI ​​algorithm.

[1202] "A means for AI to select and suggest personalized fashion items based on the input of user preferences and feedback using a smartphone" refers to a system in which users input preferences and feedback through a smartphone application, and AI selects and suggests personalized fashion items based on that information.

[1203] This invention relates to a subscription system that proposes and periodically delivers optimal fashion items based on a user's preferences, style, and needs. The system consists of multiple components, including a smartphone application, a server, and an AI algorithm.

[1204] User registration and information entry

[1205] Users first access the service using their smartphone and register. On the registration page, they enter basic information and verify their email address. Next, users enter detailed information about their preferences, style, and needs, including favorite colors, preferred types of items, and special events.

[1206] Sending and storing information

[1207] The information entered by the user is sent from the smartphone to a central server, which then stores it in a database. The stored information is managed as a user profile and used for subsequent processing.

[1208] Item selection by AI algorithm

[1209] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This selection information is then sent to the user's smartphone and displayed.

[1210] Regular deliveries

[1211] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, which then packs the items and delivers them to the user's address.

[1212] Trying on items and giving feedback

[1213] The user tries on the received item and decides whether to keep it or return it. They can then use their smartphone to notify the server of their decision. The server stores this information in a database and uses it to select items for the next purchase. The user can also enter feedback about the received item and send it back to the server. The server uses this feedback to retrain the AI ​​algorithm and make the next recommendation even more personalized.

[1214] Hardware and software used

[1215] Hardware: Smartphones, servers, database systems (e.g., PostgreSQL)

[1216] Software: Smartphone application, Python code for server-side processing (e.g., Flask), AI algorithms (e.g., TensorFlow or PyTorch)

[1217] Specific examples

[1218] For example, a user may say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects blue casual shirts and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user then notifies the server of the results using their smartphone, allowing the AI ​​to learn so that the next selection will better suit the user's preferences.

[1219] Prompt Sentence Examples

[1220] "Suggest fashion items for a summer trip for a user who likes casual style and is looking for something blue."

[1221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1222] Step 1:

[1223] Users access the service using their smartphones and enter basic information as well as detailed information about their preferences, style and desires.

[1224] Input: User information (name, email address, favorite colors, style, special events, etc.)

[1225] Output: The entered user information is saved in the smartphone application.

[1226] Step 2:

[1227] The entered user information is sent from the smartphone to the server.

[1228] Input: User information on smartphone

[1229] Output: The server receives the user information and stores it in the database.

[1230] Step 3:

[1231] The server periodically retrieves user information from the database and runs an AI algorithm to select fashion items that best suit the user's preferences and style.

[1232] Input: User information stored in the database

[1233] Output: A list of fashion items selected by an AI algorithm

[1234] Step 4:

[1235] The selected fashion items are sent to a smartphone and displayed to the user.

[1236] Input: A list of items selected by an AI algorithm

[1237] Output: Item list displayed on smartphone

[1238] Step 5:

[1239] The server schedules the next delivery based on the user's subscription plan and generates delivery instructions for the warehouse system.

[1240] Input: Subscription plan information, selected item list

[1241] Output: Delivery schedule information, delivery instructions

[1242] Step 6:

[1243] The user tries on the received item, chooses on their smartphone whether to keep it or return it, and notifies the server of the result.

[1244] Input: Choice of keeping or returning the item you tried on

[1245] Output: Selections sent to the server

[1246] Step 7:

[1247] The server stores the selection information in a database and uses it as feedback to reflect in the next item selection. User feedback information is also sent to the server and used as data for retraining the AI ​​algorithm.

[1248] Input: Selection information, feedback information

[1249] Output: Database update, AI algorithm retraining

[1250] Through the above processing steps, the system will continually suggest optimal fashion items based on the user's preferences and feedback.

[1251] 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.

[1252] This invention is a fashion coordination provision system that combines an emotion engine that recognizes the user's emotions, achieving a high level of personalization based not only on the user's preferences and requests but also on their emotions. By utilizing the information entered by the user as well as the emotional data analyzed by the emotion engine, the AI ​​algorithm selects the most suitable fashion items for the user and delivers them periodically. This allows users to easily try on items at home and enjoy highly personalized coordination.

[1253] Registering users and setting preferences

[1254] Using their device, users access the service's registration page. They create an account by entering basic information such as their name, email address, and password. They also enter detailed information about their preferences, style, and needs, including favorite colors, types of items they like, and special events. This information is sent via the device to the server and stored in a database.

[1255] AI stylist selection and user emotion recognition

[1256] The server periodically retrieves the user's basic information and preference information from the database. In addition, it uses an emotion engine to analyze the user's emotion data. The emotion engine recognizes and analyzes emotions from images, videos, text, etc. input or provided by the user. The analysis results are sent to the server.

[1257] Optimal item selection

[1258] The AI ​​algorithm on the server selects the most suitable fashion items based on the user's basic information, preferences, and emotional data. The AI ​​algorithm also takes into consideration the season, temperature, special events, and other factors when selecting items. The results of this selection are sent to the device and displayed to the user.

[1259] Regular deliveries

[1260] The server schedules the next delivery date based on the user's subscription plan. When the delivery date approaches, the server generates a delivery instruction for the warehouse system, and the specified items are picked and packed. The items are then handed over to a delivery company for delivery to the user.

[1261] Trying on and holding items

[1262] The user receives the delivered items and tries them on. The user checks the item list on the terminal and decides whether to keep or return the items after trying them on. The result is notified to the server via the terminal, and the server updates the database.

[1263] Feedback and sentiment data input

[1264] The user enters feedback about the item they received on the device, including their opinion on size, color, and style. The user's emotional data is also collected. This information is then sent to the server and stored in a database.

[1265] AI learning and item selection optimization

[1266] The server updates the AI ​​algorithm based on the newly received feedback and emotional data, taking into account the user's emotional tendencies and optimizing the next item selection. For example, if a user expresses strong emotions about a particular item, that information will be reflected in the next selection.

[1267] Specific examples

[1268] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[1269] As described above, the system of the present invention realizes a high level of personalization that takes into consideration the user's emotions, and provides efficient and appropriate fashion coordination.

[1270] The processing flow will be explained below.

[1271] Registering users and setting preferences

[1272] Process flow:

[1273] Step 1:

[1274] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[1275] Step 2:

[1276] The user enters basic information and clicks the "Register" button.

[1277] Step 3:

[1278] The terminal transmits the input information to the server.

[1279] Step 4:

[1280] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[1281] Step 5:

[1282] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[1283] Step 6:

[1284] The user responds to the questionnaire form and clicks the "Submit" button.

[1285] Step 7:

[1286] The device sends information about preferences, style and needs to the server.

[1287] Step 8:

[1288] The server stores this information in a database and updates the user's profile.

[1289] AI stylist selection and user emotion recognition

[1290] Process flow:

[1291] Step 1:

[1292] The server periodically retrieves the user's basic information and preferences from the database.

[1293] Step 2:

[1294] The server activates an emotion engine and analyzes data such as images, videos, and text provided by the user to recognize emotions.

[1295] Step 3:

[1296] The emotion engine analyzes the user's emotions and sends the results to the server.

[1297] Step 4:

[1298] The AI ​​algorithm on the server selects the most suitable items based on the user's basic information, preferences, and emotional data.

[1299] Step 5:

[1300] The server sends information about the selected item to the terminal.

[1301] Step 6:

[1302] The terminal displays the received item information to the user.

[1303] Regular deliveries

[1304] Process flow:

[1305] Step 1:

[1306] The server schedules the next delivery date based on the user's subscription plan.

[1307] Step 2:

[1308] As the delivery date approaches, the server generates delivery instructions for the warehouse system.

[1309] Step 3:

[1310] The server sends the generated delivery instructions to the warehouse system.

[1311] Step 4:

[1312] The warehouse system receives the instructions and picks and packs the specified items.

[1313] Step 5:

[1314] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[1315] Trying on and holding items

[1316] Process flow:

[1317] Step 1:

[1318] The user receives the delivered item.

[1319] Step 2:

[1320] The user checks the received item list on the terminal.

[1321] Step 3:

[1322] The user tries on the item and decides whether to keep it or return it.

[1323] Step 4:

[1324] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[1325] Step 5:

[1326] The terminal transmits the user's selection information to the server.

[1327] Step 6:

[1328] The server receives the selections, updates the database, and generates return labels for any returned items.

[1329] Feedback and sentiment data input

[1330] Process flow:

[1331] Step 1:

[1332] The user enters feedback about the received item at the terminal.

[1333] Step 2:

[1334] The user completes their feedback and clicks the "Submit" button.

[1335] Step 3:

[1336] The terminal transmits the feedback information to the server.

[1337] Step 4:

[1338] The server receives the feedback information and stores it in a database.

[1339] Step 5:

[1340] The server restarts the emotion engine and collects and analyzes the user's emotion data.

[1341] Step 6:

[1342] The emotion engine sends the analysis results to the server.

[1343] AI learning and item selection optimization

[1344] Process flow:

[1345] Step 1:

[1346] The server updates its AI algorithms based on newly received feedback and emotional data.

[1347] Step 2:

[1348] The AI ​​algorithm takes into account the user's emotional tendencies to further optimize the next item selection.

[1349] Step 3:

[1350] The server will perform new item selection based on the updated AI algorithm.

[1351] Step 4:

[1352] Information about the selected item is sent to the terminal and displayed to the user.

[1353] Specific examples

[1354] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on the subscription plan. The user can try them on at home, keep them, and return them, and the feedback and emotional data will be reflected in the next item selection.

[1355] The above is a detailed processing flow of the system that includes an emotion engine that recognizes the user's emotions. This enables advanced personalization that takes the user's emotions into consideration, providing efficient and appropriate fashion coordination.

[1356] Example 2

[1357] 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."

[1358] Conventional fashion coordination systems only select items based on the user's preferences, style, and needs, but do not provide personalization that takes into account the user's emotions. Furthermore, because the selected items do not necessarily match the user's emotions or mood, it is difficult to improve user satisfaction. Furthermore, updating the AI ​​algorithm based on feedback is inefficient, resulting in insufficient optimization of the next item selection.

[1359] 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.

[1360] In this invention, the server includes means for using an emotion recognition engine to analyze user input data and emotion data, means for executing an AI algorithm to select optimal items based on the analysis results, and means for learning and updating the AI ​​algorithm based on user feedback and emotion data. This enables advanced personalization tailored to the user's emotions, improving the accuracy of item selection and user satisfaction.

[1361] "User information" refers to basic personal information such as the user's name, email address, and password.

[1362] "Information about preferences, styles, and needs" is detailed information such as the user's favorite colors, types of items, and needs for special events.

[1363] The "server" is a computer system that receives user information and preference information, stores it in a database, and then selects items using AI algorithms and emotion recognition engines.

[1364] An "emotion recognition engine" is a program that analyzes emotions from images, videos, text, etc. provided by the user and generates emotional data.

[1365] "AI algorithm" is an artificial intelligence technology that selects the most suitable items based on the user's basic information, preferences, and emotional data.

[1366] "Feedback" refers to information about opinions and ratings regarding the size, color, and style of the items the user has tried on.

[1367] A "subscription plan" is a service agreement under which a user receives items on a regular basis, including the frequency and terms of delivery.

[1368] MODE FOR CARRYING OUT THE INVENTION

[1369] The present invention is a fashion coordination system that combines an emotion recognition engine that recognizes the user's emotions. This system realizes advanced personalization based on emotions as well as the user's preferences and requests.

[1370] First, a user uses their device to access the service's registration page and create an account by entering basic information such as their name, email address, and password. Next, the user enters detailed information about their preferences, style, and needs. This information is sent via the device to a server and stored in a database.

[1371] The server uses an emotion recognition engine (e.g., Google Cloud Vision API or IBM Watson) to analyze emotions from images, videos, and text provided by the user and generate emotion data. The analysis results are sent to the server and stored in a database.

[1372] The AI ​​algorithm selects the most suitable fashion items based on the user's basic information, preferences, and emotional data stored on the server. This selection is performed using a generative AI model. For example, open-source machine learning libraries such as TensorFlow and PyTorch are used as generative AI models. The selected items are then sent to the device and displayed to the user.

[1373] The server schedules the next delivery date based on the user's subscription plan. As the delivery date approaches, the server generates a delivery instruction for the warehouse system. The warehouse system picks, packs, and delivers the specified items to a delivery company. The items are then delivered to the user.

[1374] The user receives the delivered item and tries it on. After trying it on, the user uses a terminal to select whether to keep it or return it. This selection information is sent to the server, which updates the database.

[1375] Users can use the device to provide feedback on the items they receive, including opinions on size, color, and style. In addition, emotional data is collected and sent to the server where it is stored in a database.

[1376] The server updates the AI ​​algorithm based on new feedback and emotional data. The generative AI model further refines personalization based on the user's emotional tendencies, resulting in a more optimized selection of items for the next round.

[1377] For example, a user might input, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion recognition engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). An example of a prompt sentence might be, "The user likes a casual style and would like me to select blue items. However, I'm currently feeling stressed, so I'd like you to recommend some relaxing fashion." This is input to the generative AI model.

[1378] As described above, the system of the present invention realizes a high level of personalization that takes into account the user's emotions, and provides efficient and accurate fashion coordination. This system aims to improve user satisfaction and is capable of always providing optimal coordination.

[1379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1380] Step 1: User Registration

[1381] The user accesses the service's registration page using a device and enters basic information such as name, email address, and password. The device then sends this information to the server, which then stores it in a database.

[1382] Input: User's name, email address, and password

[1383] Output: Basic information of the user stored on the server

[1384] Specific operation: The user enters information into the input form and clicks the "Register" button. The device sends the input data to the server via a POST request. The server saves the information in the database and returns a message to the device indicating registration is complete.

[1385] Step 2: Set your preferences

[1386] Users enter details about their preferences, style and needs, and the device sends this information to the server, which stores it in a database.

[1387] Input: User preferences (color, item type, special events, etc.)

[1388] Output: User details stored on the server

[1389] How it works: The user enters their preferred information by manipulating multiple checkboxes and text fields on the device screen. The device then sends this data to the server, which stores the information in a database.

[1390] Step 3: Emotion Recognition

[1391] The server uses an emotion recognition engine to analyze emotions from images, videos, and text provided by the user, and the analysis results are stored on the server as emotion data.

[1392] Input: User-uploaded images, videos, and text

[1393] Output: Parsed emotion data

[1394] How it works: A user uploads images, videos, and text using their device. The device then sends this data to the server, which then uses an emotion recognition engine to analyze the emotions and records the results in a database.

[1395] Step 4: Selecting items

[1396] The AI ​​algorithm runs on the server and combines the user's basic information, preferences, and emotional data to select the most suitable fashion items. The results are then sent to the device and displayed to the user.

[1397] Input: User's basic information, preference information, emotional data

[1398] Output: A list of selected fashion items

[1399] Specific operation: The server retrieves registered user information from the database and selects items using a generative AI model. The selection results are sent to the device and displayed on the device.

[1400] Step 5: Arrange shipping

[1401] The server schedules the next delivery date based on the subscription plan, and when the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[1402] Input: User's subscription plan

[1403] Output: Delivery instructions, picking list

[1404] Specific operation: The server checks the delivery schedule, sends an API request to the warehouse management system, and creates a picking list. Warehouse workers pick items based on the list, pack them, and hand them over to the delivery company.

[1405] Step 6: Try on the item and decide whether to keep it

[1406] The user receives the delivered item, tries it on, and selects whether to keep it or return it. This selection information is then sent to the server, which updates the database.

[1407] Input: User's retention or return decision

[1408] Output: Updated retention information on the server

[1409] Specific operation: The user clicks the confirmation button on the device and selects the items to keep and the items to return. The device sends this information to the server, which updates the database.

[1410] Step 7: Collect feedback and sentiment data

[1411] Users input their feedback about the items they receive on the device, and emotional data is collected. This information is then sent to the server and stored in a database.

[1412] Input: User feedback, emotional data

[1413] Output: Feedback and emotion data stored in a database

[1414] Specific operation: The user enters their opinion and evaluation score in the feedback form on the device and uploads emotional data (e.g., photos and text). This data is sent to the server, which then stores it in a database.

[1415] Step 8: Training the AI ​​algorithm

[1416] The server updates the AI ​​algorithm based on new feedback and emotion data, which further optimizes the next item selection.

[1417] Input: User feedback, emotional data

[1418] Output: Updated AI algorithm

[1419] How it works: The server inputs the received data into the generative AI model and uses it as new training data. The AI ​​algorithm learns and improves the accuracy of the next item selection.

[1420] (Application example 2)

[1421] 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."

[1422] Conventional fashion coordination systems provide personalization based on the user's preferences, style, and needs, but do not provide advanced personalization based on the user's emotions. This makes it difficult for users to select the optimal fashion items that match their emotions at any given time, and this has led to the issue of not being able to sufficiently increase user satisfaction.

[1423] 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.

[1424] In this invention, the server includes means for executing an artificial intelligence algorithm for selecting optimal items based on the user's personal information and emotion data obtained by an emotion analysis engine, means for displaying the selected items to the user, and means for delivering the items to the user based on a regular delivery schedule. This enables advanced personalization based on the user's emotions and makes it possible to provide the user with the most optimal fashion items.

[1425] Definition of each word

[1426] "Personal information" refers to information that identifies an individual, such as the user's name, address, email address, and telephone number.

[1427] "Preferences" is information about the user's preferences such as colors, styles, and fashion items.

[1428] "Style" is information about the type and design of fashion that the user normally wears.

[1429] "Request" is information about items or services that a user desires for a specific event or purpose.

[1430] "Data" refers to any information processed by the system, including users' personal information, preferences, style, needs, and emotional data.

[1431] A "server" is a computer system for storing and processing data sent by users.

[1432] An "emotion analysis engine" is software that analyzes a user's emotions from input data such as images and text provided by the user and generates data.

[1433] "Emotion data" is information about the user's emotional state obtained by the emotion analysis engine.

[1434] An "artificial intelligence algorithm" is a computer program that selects items suitable for a user through a process of data analysis and learning.

[1435] The "display means" is a function for displaying information about the selected item on the user's terminal screen.

[1436] A "delivery plan" is a schedule for periodically sending selected items to a user.

[1437] "Feedback" refers to the thoughts and opinions of users about the items they receive, which helps improve the system.

[1438] "Mode for Carrying Out the Invention"

[1439] This invention is a system that analyzes a user's emotions and suggests fashion coordination. The system collects and analyzes the user's emotional data, selects optimal fashion items based on that data, and delivers them to the user on a regular basis. The configuration and operation of this system are described below.

[1440] Hardware and software used

[1441] Hardware:

[1442] Smartphone: A device that allows users to input information and interact with systems.

[1443] Server: A system for storing data and running AI algorithms and sentiment analysis engines.

[1444] software:

[1445] Emotion analysis engine (EmotionEngine): Software that analyzes images and text provided by users and generates emotional data.

[1446] Artificial intelligence algorithm (FashionAI): An algorithm that selects fashion items based on a user's personal information, preferences, style, and emotional data.

[1447] Database: Stores and manages users' personal information, preferences, style, feedback, and emotional data.

[1448] Delivery System: Software that executes a schedule for the periodic delivery of selected items to users.

[1449] System Overview

[1450] The server receives personal information and preference data entered by the user and stores the data in a database. The user's emotional data is extracted from the images and text provided by the user using an emotional analysis engine. The server then runs an artificial intelligence algorithm based on the user's personal information, preferences, style, and emotional data to select the most suitable fashion items.

[1451] The selected fashion items are displayed on the user's smartphone, allowing the user to check the items. The selected items are then delivered to the user based on a regular delivery schedule. The user can try on the received items and choose whether to keep them or return them. This selection information and feedback are then sent back to the server and stored in a database.

[1452] The server updates its AI algorithm based on this feedback and emotional data, which will further optimize item selection for the user from the next time onwards.

[1453] Specific examples

[1454] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional analysis engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[1455] Example prompts for generative AI models

[1456] Write pseudocode for an app that analyzes user-provided image and text data and recommends personalized fashion items based on sentiment data. Use a sentiment analysis engine, artificial intelligence algorithms, and database classes to select items and schedule deliveries based on user preference information and sentiment data. Include user feedback processing.

[1457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1458] Program processing flow and specific explanation

[1459] Processing Steps

[1460] Step 1:

[1461] Using their smartphones, users enter personal information and data about their preferences, style, and needs, including their name, address, favorite colors and styles, desired item types, etc. This input data is then sent from the device to a server.

[1462] Input: Personal information, preferences, style, requests

[1463] Output: Data sent to the server

[1464] Specific operation: The user fills in the data in the input form on their smartphone and presses the send button, which sends the data to the server.

[1465] Step 2:

[1466] The server stores the received user personal information and preference related data in a database.

[1467] Input: Received data (personal information, preferences, style, requests)

[1468] Output: Data stored in the database

[1469] Specific behavior: The server validates the received data and stores it in the corresponding fields in the database.

[1470] Step 3:

[1471] For emotion analysis, users input image and text data into their devices and send it to the server. The emotion analysis engine receives this data and performs the analysis.

[1472] Input: image, text data

[1473] Output: Parsed emotion data

[1474] How it works: When a user takes a picture with their smartphone camera or enters text and presses the send button, the data is sent to the server. The emotion analysis engine analyzes this data and generates emotion data.

[1475] Step 4:

[1476] The server stores the emotional data obtained from the emotion analysis engine in a database, combines it with the user's personal information and preference data, and runs an artificial intelligence algorithm to select the most suitable fashion items.

[1477] Input: Emotional data, personal information, preference data

[1478] Output: Selected fashion items

[1479] Specific operation: The server stores the emotional data in a database, and inputs the emotional data, personal information, and preference data into an artificial intelligence algorithm to select the most suitable item.

[1480] Step 5:

[1481] The server sends information about the selected fashion items to the user's smartphone and displays it to the user.

[1482] Input: Selected fashion item

[1483] Output: Item information displayed on the user's smartphone

[1484] Specific operation: The server sends information about the selected item to the smartphone, and the item information is displayed on the smartphone screen.

[1485] Step 6:

[1486] The server delivers the selected items to the user based on a regular delivery plan.

[1487] Input: Selected fashion items, delivery plan

[1488] Output: Items delivered to the user

[1489] Specific operation: The server sends the selected item and the user's address information to the delivery system, and delivery is arranged.

[1490] Step 7:

[1491] The user tries on the received item and chooses whether to keep it or return it, and the selection information is sent to the server.

[1492] Input: Information on whether to keep or return

[1493] Output: Selections sent to the server

[1494] Specific operation: The user selects an option on the smartphone screen and presses the send button, which sends the information to the server.

[1495] Step 8:

[1496] The server stores user selections and feedback in a database and uses it to update the artificial intelligence algorithms.

[1497] Input: Selection information, feedback

[1498] Output: Updated artificial intelligence algorithm

[1499] Specific operation: The server stores the selection information and feedback in a database and adjusts and updates the parameters of the artificial intelligence algorithm based on that information.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] [Fourth embodiment]

[1504] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1505] 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.

[1506] 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).

[1507] 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.

[1508] 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.

[1509] 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).

[1510] 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.

[1511] 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.

[1512] 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.

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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."

[1517] This invention is a subscription-based system that provides optimal fashion coordination tailored to a user's preferences, style, and needs. This system uses an AI algorithm to select fashion items based on information entered by the user and delivers them periodically, allowing the user to easily try on the items at home. Furthermore, the AI ​​continues to learn based on user feedback, making its next suggestions more personalized.

[1518] Registering users and setting preferences

[1519] Users first access the service using their device and register. On the registration page, they enter basic information and verify their email address. They then enter detailed information about their tastes, style, and needs, including their favorite colors, preferred types of items, and special events.

[1520] Sending and storing user information

[1521] The device sends the entered information to the server, which then stores it in a database and manages it as a user profile.

[1522] AI stylist selection

[1523] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This information is then sent to the user's device and displayed.

[1524] Regular deliveries

[1525] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, and the items are packed and delivered as instructed.

[1526] Trying on and holding items

[1527] The user tries on the received item and decides whether to keep it or return it, and then uses the terminal to notify the server of the result, which stores the information in a database.

[1528] Inputting feedback and training the AI

[1529] Users can use their devices to input feedback about the items they receive and send it to the server, which then updates the AI ​​algorithm based on that feedback and reflects it in the next item selection.

[1530] Specific examples

[1531] For example, a user might say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects a casual blue shirt and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user uses their device to notify the server of the results, and the AI ​​learns to make the next selection more in line with the user's preferences.

[1532] As described above, the system of the present invention proposes optimal fashion items that meet the user's preferences and needs, allowing the user to enjoy the latest fashions while reducing the burden on the user.

[1533] The processing flow will be explained below.

[1534] Registration and Preferences

[1535] Process flow:

[1536] Step 1:

[1537] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[1538] Step 2:

[1539] The user enters basic information and clicks the "Register" button.

[1540] Step 3:

[1541] The terminal transmits the input information to the server.

[1542] Step 4:

[1543] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[1544] Step 5:

[1545] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[1546] Step 6:

[1547] The user responds to the questionnaire form and clicks the "Submit" button.

[1548] Step 7:

[1549] The device sends information about preferences, style and needs to the server.

[1550] Step 8:

[1551] The server stores this information in a database and updates the user's profile.

[1552] AI stylist selection

[1553] Process flow:

[1554] Step 1:

[1555] The server periodically retrieves user information from the database.

[1556] Step 2:

[1557] The server uses the information it receives to run AI algorithms to select the best items, taking into account the user's preferences, style, season, temperature, and special events.

[1558] Step 3:

[1559] The server sends information about the selected item to the terminal.

[1560] Step 4:

[1561] The terminal displays the received item information to the user.

[1562] Regular deliveries

[1563] Process flow:

[1564] Step 1:

[1565] The server schedules the next delivery date based on the user's subscription plan.

[1566] Step 2:

[1567] When the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[1568] Step 3:

[1569] The server sends the generated delivery instructions to the warehouse system.

[1570] Step 4:

[1571] The warehouse system receives the instructions and picks and packs the specified items.

[1572] Step 5:

[1573] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[1574] Trying on and holding items

[1575] Process flow:

[1576] Step 1:

[1577] The user receives the delivered item.

[1578] Step 2:

[1579] The user checks the received item list on the terminal.

[1580] Step 3:

[1581] The user tries on the item and decides whether to keep it or return it.

[1582] Step 4:

[1583] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[1584] Step 5:

[1585] The terminal transmits the user's selection information to the server.

[1586] Step 6:

[1587] The server receives the selections, updates the database, and generates return labels for any returned items.

[1588] Inputting feedback and training the AI

[1589] Process flow:

[1590] Step 1:

[1591] The user enters feedback about the item received at the terminal.

[1592] Step 2:

[1593] The user completes their feedback and clicks the "Submit" button.

[1594] Step 3:

[1595] The terminal transmits the feedback information to the server.

[1596] Step 4:

[1597] The server receives the feedback information and stores it in a database.

[1598] Step 5:

[1599] The server updates the AI ​​algorithm based on the new feedback and reflects it in the next item selection.

[1600] The above is a detailed and specific explanation of each processing step, which allows the user to efficiently and accurately receive fashion items that suit their preferences.

[1601] Example 1

[1602] 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."

[1603] Conventional fashion coordination systems did not adequately select items that matched the user's preferences and style, making it difficult to personalize the system to meet the user's needs. Furthermore, there were issues with insufficient regular delivery and improvements based on feedback, resulting in a poor user experience. Furthermore, the AI ​​model did not efficiently suggest or learn appropriate items to select.

[1604] 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.

[1605] In this invention, the server includes means for executing an artificial intelligence model to select optimal items based on user information, means for displaying the selected items to the user, and means for periodically delivering the items based on a subscription plan, thereby enabling provision of personalized fashion items tailored to the user's preferences and style.

[1606] "User information" refers to detailed information entered by a user, such as personal information, preferences, style, and requests.

[1607] "Server" is a computer system that has the ability to receive and store user information and run artificial intelligence models to select the most suitable items for the user.

[1608] An "artificial intelligence model" is a machine learning algorithm that selects the most suitable fashion items based on user information.

[1609] "Means for displaying selected items to the user" refers to a technique or method for transmitting information about the fashion items selected by the server to the user's terminal and displaying it.

[1610] A "subscription plan" is a service contract under which a user receives fashion items delivered to them on a regular basis.

[1611] "Means for periodic delivery" refers to a technique or method by which the server issues delivery instructions to the warehouse system and sends items to the user based on the user's subscription plan.

[1612] "Feedback" is information that provides a user's impressions and evaluations of the items they have received.

[1613] A "prompt sentence" is a specific instruction to the AI ​​model to select fashion items based on the user's preferences and style.

[1614] The present invention is a subscription-based system that provides optimal fashion coordination based on a user's preferences, style, and needs. To build this system, the following hardware and software are used.

[1615] Hardware and software used

[1616] Hardware: User devices (smartphones, tablets, PCs), servers, warehouse systems

[1617] Software: Database management systems (MySQL, PostgreSQL, etc.), AI algorithms (TensorFlow, PyTorch, etc.), web application frameworks (React, Angular, etc.)

[1618] Specific operation of the system

[1619] 1. User Registration and Preferences

[1620] First, a user uses a terminal to access the system's registration page and enter basic information. Then, they answer detailed questions about their preferences and style. For example, by answering questions such as "What is your favorite color?" and "What style do you like?", the user's individual preferences and requests are provided to the system.

[1621] 2. Transmission and storage of user information

[1622] The terminal sends the input information to the server, which stores it in a database and manages it as a user profile.

[1623] 3. AI stylist selection

[1624] The server periodically retrieves user information from the database and uses AI algorithms to select fashion items that best suit the user's preferences and style. For example, it may select items for a particular season or event. The results of the selection are sent to the device and displayed to the user.

[1625] 4. Regular deliveries

[1626] The server schedules the next delivery date based on the user's subscription plan, and when this date approaches, the server issues a delivery instruction to the warehouse system, which then packs and delivers the item to the user.

[1627] 5. Trying on and holding items

[1628] The user tries on the received item and decides whether to keep it or return it. The result is notified to the server from the terminal, and the server stores the information in a database.

[1629] 6. Inputting feedback and training the AI

[1630] Users use their devices to provide feedback about the items they receive, and the server receives this feedback and updates the AI ​​algorithm, which helps ensure that the next item selection is more tailored to the user's preferences.

[1631] Examples and prompts

[1632] For example, based on a user's information such as "I like casual style, I like blue items, and I travel a lot in the summer," the AI ​​will select blue casual shirts and shorts and deliver them to the user periodically. Below is a specific example of such a prompt.

[1633] Prompt Sentence Examples

[1634] User Information:

[1635] 1. Style: Casual

[1636] 2. Favorite color: Blue

[1637] 3. Season: Summer

[1638] 4. Special requirements: I travel a lot

[1639] Prompt the AI ​​model:

[1640] Please suggest the best fashion items for a user who likes casual style, prefers blue items, and travels a lot in the summer.

[1641] The AI ​​model uses this prompt to select fashion items, and the server then processes the delivery based on the results. User feedback can be used to further personalize future recommendations, ensuring that the latest trends are always available.

[1642] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1643] Step 1:

[1644] A user accesses the system using a terminal and enters basic information and information about preferences, style, and needs on a registration page.

[1645] Input: Details such as the user's name, email address, address, preferred color, style, and any special requests

[1646] Specific operation: The user enters information into a form on the device screen and presses the "Submit" button.

[1647] Output: The input information becomes data to be sent from the terminal.

[1648] Step 2:

[1649] The terminal transmits the input user information to the server.

[1650] Input: User information entered in step 1

[1651] Specific operation: The device sends data to the server using the HTTPS protocol.

[1652] Output: The server receives the user information data.

[1653] Step 3:

[1654] The server stores the received user information in a database.

[1655] Input: User information data received in step 2

[1656] What happens: The server executes an SQL query to store the user information in a database (e.g., MySQL or PostgreSQL).

[1657] Output: User information is saved in the database.

[1658] Step 4:

[1659] The server periodically retrieves user information from the database and uses a generative AI model to select the most suitable items.

[1660] Input: User information, season, temperature, special event information retrieved from the database

[1661] Specific operation: The server executes an SQL query to obtain user information, inputs that information into the AI ​​model as a prompt, and the AI ​​model selects an item.

[1662] Output: Selected fashion item information is generated.

[1663] Step 5:

[1664] The server sends the selected item to the user's terminal and displays it.

[1665] Input: Fashion item information generated in step 4

[1666] Specific operation: The server sends the selected item information to the user's device. The device displays the received information.

[1667] Output: The selected item information is displayed on the user's device.

[1668] Step 6:

[1669] The server schedules the next delivery date based on the user's subscription plan and issues a delivery instruction to the warehouse system.

[1670] Input: User's subscription plan information and selected item information

[1671] Specific operation: The server uses the scheduler to set the next delivery date and sends packing and delivery instructions to the warehouse system via API.

[1672] Output: The item is packaged and delivered to the user.

[1673] Step 7:

[1674] The user tries on the received item and uses the terminal to decide whether to keep it or return it.

[1675] Input: User feedback about the item received

[1676] Specific operation: The user selects the option to keep or return using the terminal application and sends the result.

[1677] Output: The selection is sent to the server.

[1678] Step 8:

[1679] The server stores the received feedback and selection information in a database and updates the AI ​​model to reflect this in the next item selection.

[1680] Input: Feedback and selections received from users

[1681] How it works: The server stores the feedback information in a database and uses that data to retrain the AI ​​model.

[1682] Output: The next selection made by the updated AI model will be more in line with the user's preferences.

[1683] (Application example 1)

[1684] 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."

[1685] Conventional fashion coordination suggestion systems lack sufficient personalization based on the user's preferences, style, and needs, making it difficult to provide appropriate suggestions. Furthermore, there is a lack of technology to efficiently utilize user feedback, limiting the improvement of user satisfaction. Furthermore, there is a need for a method that allows users to easily try on suggested items and choose whether to keep them or return them.

[1686] 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.

[1687] In this invention, the server includes a means for allowing a user to input preferences and feedback using a smartphone, and for AI to select and suggest personalized fashion items based on the input, a means for transmitting information about the user and information about preferences, style, and needs to the server, and a means for executing an AI algorithm to select optimal items based on the user information, thereby enabling efficient input of user information and provision of personalized fashion suggestions.

[1688] The "means for inputting user information" refers to an interface that allows a user to input information such as their personal information, preferences, style, and requests into the system.

[1689] The "means for entering information about the user's preferences, style, and requests" is a function that allows the user to enter detailed information about specific fashion preferences, style, requests regarding events, and the like.

[1690] The "means for transmitting the user's information and information regarding preferences, style, and needs to the server" refers to a communication means for transmitting the information input by the user to the central server.

[1691] "Means for executing an AI algorithm to select optimal items based on user information" refers to a system that runs on a server and executes an algorithm to select fashion items that match the user's preferences, style, and requests.

[1692] The "means for displaying selected items to the user" refers to an interface for displaying the fashion items selected by the AI ​​algorithm on the user's device.

[1693] "Means for periodically delivering items based on a subscription plan" refers to the procedures and means for periodically delivering fashion items to a user's address based on a plan selected by the user.

[1694] "A means for users to provide feedback on the items they receive and update the AI ​​algorithm based on that feedback" refers to a system in which users input their impressions and ratings of the items they try on, and that information is used to improve and update the AI ​​algorithm.

[1695] "A means for AI to select and suggest personalized fashion items based on the input of user preferences and feedback using a smartphone" refers to a system in which users input preferences and feedback through a smartphone application, and AI selects and suggests personalized fashion items based on that information.

[1696] This invention relates to a subscription system that proposes and periodically delivers optimal fashion items based on a user's preferences, style, and needs. The system consists of multiple components, including a smartphone application, a server, and an AI algorithm.

[1697] User registration and information entry

[1698] Users first access the service using their smartphone and register. On the registration page, they enter basic information and verify their email address. Next, users enter detailed information about their preferences, style, and needs, including favorite colors, preferred types of items, and special events.

[1699] Sending and storing information

[1700] The information entered by the user is sent from the smartphone to a central server, which then stores it in a database. The stored information is managed as a user profile and used for subsequent processing.

[1701] Item selection by AI algorithm

[1702] The server periodically retrieves user information from the database and runs an AI algorithm to select items that best suit the user's preferences and style. The AI ​​takes into account the season, temperature, and special events when selecting items. This selection information is then sent to the user's smartphone and displayed.

[1703] Regular deliveries

[1704] The server schedules the next delivery date based on the user's subscription plan. As this date approaches, the server generates delivery instructions for the warehouse system, which then packs the items and delivers them to the user's address.

[1705] Trying on items and giving feedback

[1706] The user tries on the received item and decides whether to keep it or return it. They can then use their smartphone to notify the server of their decision. The server stores this information in a database and uses it to select items for the next purchase. The user can also enter feedback about the received item and send it back to the server. The server uses this feedback to retrain the AI ​​algorithm and make the next recommendation even more personalized.

[1707] Hardware and software used

[1708] Hardware: Smartphones, servers, database systems (e.g., PostgreSQL)

[1709] Software: Smartphone application, Python code for server-side processing (e.g., Flask), AI algorithms (e.g., TensorFlow or PyTorch)

[1710] Specific examples

[1711] For example, a user may say, "I like casual style, I like blue items, and I travel a lot in the summer." This information is entered and sent to a server. Based on this information, the AI ​​selects blue casual shirts and shorts, which are then delivered to the user periodically based on a subscription plan. The user tries on the items and decides to keep the shirt and return the shorts. The user then notifies the server of the results using their smartphone, allowing the AI ​​to learn so that the next selection will better suit the user's preferences.

[1712] Prompt Sentence Examples

[1713] "Suggest fashion items for a summer trip for a user who likes casual style and is looking for something blue."

[1714] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1715] Step 1:

[1716] Users access the service using their smartphones and enter basic information as well as detailed information about their preferences, style and desires.

[1717] Input: User information (name, email address, favorite colors, style, special events, etc.)

[1718] Output: The entered user information is saved in the smartphone application.

[1719] Step 2:

[1720] The entered user information is sent from the smartphone to the server.

[1721] Input: User information on smartphone

[1722] Output: The server receives the user information and stores it in the database.

[1723] Step 3:

[1724] The server periodically retrieves user information from the database and runs an AI algorithm to select fashion items that best suit the user's preferences and style.

[1725] Input: User information stored in the database

[1726] Output: A list of fashion items selected by an AI algorithm

[1727] Step 4:

[1728] The selected fashion items are sent to a smartphone and displayed to the user.

[1729] Input: A list of items selected by an AI algorithm

[1730] Output: Item list displayed on smartphone

[1731] Step 5:

[1732] The server schedules the next delivery based on the user's subscription plan and generates delivery instructions for the warehouse system.

[1733] Input: Subscription plan information, selected item list

[1734] Output: Delivery schedule information, delivery instructions

[1735] Step 6:

[1736] The user tries on the received item, chooses on their smartphone whether to keep it or return it, and notifies the server of the result.

[1737] Input: Choice of keeping or returning the item you tried on

[1738] Output: Selections sent to the server

[1739] Step 7:

[1740] The server stores the selection information in a database and uses it as feedback to reflect in the next item selection. User feedback information is also sent to the server and used as data for retraining the AI ​​algorithm.

[1741] Input: Selection information, feedback information

[1742] Output: Database update, AI algorithm retraining

[1743] Through the above processing steps, the system will continually suggest optimal fashion items based on the user's preferences and feedback.

[1744] 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.

[1745] This invention is a fashion coordination provision system that combines an emotion engine that recognizes the user's emotions, achieving a high level of personalization based not only on the user's preferences and requests but also on their emotions. By utilizing the information entered by the user as well as the emotional data analyzed by the emotion engine, the AI ​​algorithm selects the most suitable fashion items for the user and delivers them periodically. This allows users to easily try on items at home and enjoy highly personalized coordination.

[1746] Registering users and setting preferences

[1747] Using their device, users access the service's registration page. They create an account by entering basic information such as their name, email address, and password. They also enter detailed information about their preferences, style, and needs, including favorite colors, types of items they like, and special events. This information is sent via the device to the server and stored in a database.

[1748] AI stylist selection and user emotion recognition

[1749] The server periodically retrieves the user's basic information and preference information from the database. In addition, it uses an emotion engine to analyze the user's emotion data. The emotion engine recognizes and analyzes emotions from images, videos, text, etc. input or provided by the user. The analysis results are sent to the server.

[1750] Optimal item selection

[1751] The AI ​​algorithm on the server selects the most suitable fashion items based on the user's basic information, preferences, and emotional data. The AI ​​algorithm also takes into consideration the season, temperature, special events, and other factors when selecting items. The results of this selection are sent to the device and displayed to the user.

[1752] Regular deliveries

[1753] The server schedules the next delivery date based on the user's subscription plan. When the delivery date approaches, the server generates a delivery instruction for the warehouse system, and the specified items are picked and packed. The items are then handed over to a delivery company for delivery to the user.

[1754] Trying on and holding items

[1755] The user receives the delivered items and tries them on. The user checks the item list on the terminal and decides whether to keep or return the items after trying them on. The result is notified to the server via the terminal, and the server updates the database.

[1756] Feedback and sentiment data input

[1757] The user enters feedback about the item they received on the device, including their opinion on size, color, and style. The user's emotional data is also collected. This information is then sent to the server and stored in a database.

[1758] AI learning and item selection optimization

[1759] The server updates the AI ​​algorithm based on the newly received feedback and emotional data, taking into account the user's emotional tendencies and optimizing the next item selection. For example, if a user expresses strong emotions about a particular item, that information will be reflected in the next selection.

[1760] Specific examples

[1761] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[1762] As described above, the system of the present invention realizes a high level of personalization that takes into consideration the user's emotions, and provides efficient and appropriate fashion coordination.

[1763] The processing flow will be explained below.

[1764] Registering users and setting preferences

[1765] Process flow:

[1766] Step 1:

[1767] The user accesses the service's registration page on their device, where they are presented with a form to enter basic information such as their name, email address, and password.

[1768] Step 2:

[1769] The user enters basic information and clicks the "Register" button.

[1770] Step 3:

[1771] The terminal transmits the input information to the server.

[1772] Step 4:

[1773] The server receives the user information, stores it in a database, and then sends a confirmation email to the user.

[1774] Step 5:

[1775] Users click on a link in the confirmation email to access a survey form about their preferences, style and needs.

[1776] Step 6:

[1777] The user responds to the questionnaire form and clicks the "Submit" button.

[1778] Step 7:

[1779] The device sends information about preferences, style and needs to the server.

[1780] Step 8:

[1781] The server stores this information in a database and updates the user's profile.

[1782] AI stylist selection and user emotion recognition

[1783] Process flow:

[1784] Step 1:

[1785] The server periodically retrieves the user's basic information and preferences from the database.

[1786] Step 2:

[1787] The server activates an emotion engine and analyzes data such as images, videos, and text provided by the user to recognize emotions.

[1788] Step 3:

[1789] The emotion engine analyzes the user's emotions and sends the results to the server.

[1790] Step 4:

[1791] The AI ​​algorithm on the server selects the most suitable items based on the user's basic information, preferences, and emotional data.

[1792] Step 5:

[1793] The server sends information about the selected item to the terminal.

[1794] Step 6:

[1795] The terminal displays the received item information to the user.

[1796] Regular deliveries

[1797] Process flow:

[1798] Step 1:

[1799] The server schedules the next delivery date based on the user's subscription plan.

[1800] Step 2:

[1801] As the delivery date approaches, the server generates delivery instructions for the warehouse system.

[1802] Step 3:

[1803] The server sends the generated delivery instructions to the warehouse system.

[1804] Step 4:

[1805] The warehouse system receives the instructions and picks and packs the specified items.

[1806] Step 5:

[1807] The warehouse system hands the item over to the delivery company to initiate delivery to the user.

[1808] Trying on and holding items

[1809] Process flow:

[1810] Step 1:

[1811] The user receives the delivered item.

[1812] Step 2:

[1813] The user checks the received item list on the terminal.

[1814] Step 3:

[1815] The user tries on the item and decides whether to keep it or return it.

[1816] Step 4:

[1817] The user uses the terminal to notify the server of the selection of items to keep and items to return.

[1818] Step 5:

[1819] The terminal transmits the user's selection information to the server.

[1820] Step 6:

[1821] The server receives the selections, updates the database, and generates return labels for any returned items.

[1822] Feedback and sentiment data input

[1823] Process flow:

[1824] Step 1:

[1825] The user enters feedback about the received item at the terminal.

[1826] Step 2:

[1827] The user completes their feedback and clicks the "Submit" button.

[1828] Step 3:

[1829] The terminal transmits the feedback information to the server.

[1830] Step 4:

[1831] The server receives the feedback information and stores it in a database.

[1832] Step 5:

[1833] The server restarts the emotion engine and collects and analyzes the user's emotion data.

[1834] Step 6:

[1835] The emotion engine sends the analysis results to the server.

[1836] AI learning and item selection optimization

[1837] Process flow:

[1838] Step 1:

[1839] The server updates its AI algorithms based on newly received feedback and emotional data.

[1840] Step 2:

[1841] The AI ​​algorithm takes into account the user's emotional tendencies to further optimize the next item selection.

[1842] Step 3:

[1843] The server will perform new item selection based on the updated AI algorithm.

[1844] Step 4:

[1845] Information about the selected item is sent to the terminal and displayed to the user.

[1846] Specific examples

[1847] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on the subscription plan. The user can try them on at home, keep them, and return them, and the feedback and emotional data will be reflected in the next item selection.

[1848] The above is a detailed processing flow of the system that includes an emotion engine that recognizes the user's emotions. This enables advanced personalization that takes the user's emotions into consideration, providing efficient and appropriate fashion coordination.

[1849] Example 2

[1850] 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."

[1851] Conventional fashion coordination systems only select items based on the user's preferences, style, and needs, but do not provide personalization that takes into account the user's emotions. Furthermore, because the selected items do not necessarily match the user's emotions or mood, it is difficult to improve user satisfaction. Furthermore, updating the AI ​​algorithm based on feedback is inefficient, resulting in insufficient optimization of the next item selection.

[1852] 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.

[1853] In this invention, the server includes means for using an emotion recognition engine to analyze user input data and emotion data, means for executing an AI algorithm to select optimal items based on the analysis results, and means for learning and updating the AI ​​algorithm based on user feedback and emotion data. This enables advanced personalization tailored to the user's emotions, improving the accuracy of item selection and user satisfaction.

[1854] "User information" refers to basic personal information such as the user's name, email address, and password.

[1855] "Information about preferences, styles, and needs" is detailed information such as the user's favorite colors, types of items, and needs for special events.

[1856] The "server" is a computer system that receives user information and preference information, stores it in a database, and then selects items using AI algorithms and emotion recognition engines.

[1857] An "emotion recognition engine" is a program that analyzes emotions from images, videos, text, etc. provided by the user and generates emotional data.

[1858] "AI algorithm" is an artificial intelligence technology that selects the most suitable items based on the user's basic information, preferences, and emotional data.

[1859] "Feedback" refers to information about opinions and ratings regarding the size, color, and style of the items the user has tried on.

[1860] A "subscription plan" is a service agreement under which a user receives items on a regular basis, including the frequency and terms of delivery.

[1861] MODE FOR CARRYING OUT THE INVENTION

[1862] The present invention is a fashion coordination system that combines an emotion recognition engine that recognizes the user's emotions. This system realizes advanced personalization based on emotions as well as the user's preferences and requests.

[1863] First, a user uses their device to access the service's registration page and create an account by entering basic information such as their name, email address, and password. Next, the user enters detailed information about their preferences, style, and needs. This information is sent via the device to a server and stored in a database.

[1864] The server uses an emotion recognition engine (e.g., Google Cloud Vision API or IBM Watson) to analyze emotions from images, videos, and text provided by the user and generate emotion data. The analysis results are sent to the server and stored in a database.

[1865] The AI ​​algorithm selects the most suitable fashion items based on the user's basic information, preferences, and emotional data stored on the server. This selection is performed using a generative AI model. For example, open-source machine learning libraries such as TensorFlow and PyTorch are used as generative AI models. The selected items are then sent to the device and displayed to the user.

[1866] The server schedules the next delivery date based on the user's subscription plan. As the delivery date approaches, the server generates a delivery instruction for the warehouse system. The warehouse system picks, packs, and delivers the specified items to a delivery company. The items are then delivered to the user.

[1867] The user receives the delivered item and tries it on. After trying it on, the user uses a terminal to select whether to keep it or return it. This selection information is sent to the server, which updates the database.

[1868] Users can use the device to provide feedback on the items they receive, including opinions on size, color, and style. In addition, emotional data is collected and sent to the server where it is stored in a database.

[1869] The server updates the AI ​​algorithm based on new feedback and emotional data. The generative AI model further refines personalization based on the user's emotional tendencies, resulting in a more optimized selection of items for the next round.

[1870] For example, a user might input, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotion recognition engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). An example of a prompt sentence might be, "The user likes a casual style and would like me to select blue items. However, I'm currently feeling stressed, so I'd like you to recommend some relaxing fashion." This is input to the generative AI model.

[1871] As described above, the system of the present invention realizes a high level of personalization that takes into account the user's emotions, and provides efficient and accurate fashion coordination. This system aims to improve user satisfaction and is capable of always providing optimal coordination.

[1872] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1873] Step 1: User Registration

[1874] The user accesses the service's registration page using a device and enters basic information such as name, email address, and password. The device then sends this information to the server, which then stores it in a database.

[1875] Input: User's name, email address, and password

[1876] Output: Basic information of the user stored on the server

[1877] Specific operation: The user enters information into the input form and clicks the "Register" button. The device sends the input data to the server via a POST request. The server saves the information in the database and returns a message to the device indicating registration is complete.

[1878] Step 2: Set your preferences

[1879] Users enter details about their preferences, style and needs, and the device sends this information to the server, which stores it in a database.

[1880] Input: User preferences (color, item type, special events, etc.)

[1881] Output: User details stored on the server

[1882] How it works: The user enters their preferred information by manipulating multiple checkboxes and text fields on the device screen. The device then sends this data to the server, which stores the information in a database.

[1883] Step 3: Emotion Recognition

[1884] The server uses an emotion recognition engine to analyze emotions from images, videos, and text provided by the user, and the analysis results are stored on the server as emotion data.

[1885] Input: User-uploaded images, videos, and text

[1886] Output: Parsed emotion data

[1887] How it works: A user uploads images, videos, and text using their device. The device then sends this data to the server, which then uses an emotion recognition engine to analyze the emotions and records the results in a database.

[1888] Step 4: Selecting items

[1889] The AI ​​algorithm runs on the server and combines the user's basic information, preferences, and emotional data to select the most suitable fashion items. The results are then sent to the device and displayed to the user.

[1890] Input: User's basic information, preference information, emotional data

[1891] Output: A list of selected fashion items

[1892] Specific operation: The server retrieves registered user information from the database and selects items using a generative AI model. The selection results are sent to the device and displayed on the device.

[1893] Step 5: Arrange shipping

[1894] The server schedules the next delivery date based on the subscription plan, and when the delivery date approaches, the server generates a delivery instruction for the warehouse system.

[1895] Input: User's subscription plan

[1896] Output: Delivery instructions, picking list

[1897] Specific operation: The server checks the delivery schedule, sends an API request to the warehouse management system, and creates a picking list. Warehouse workers pick items based on the list, pack them, and hand them over to the delivery company.

[1898] Step 6: Try on the item and decide whether to keep it

[1899] The user receives the delivered item, tries it on, and selects whether to keep it or return it. This selection information is then sent to the server, which updates the database.

[1900] Input: User's retention or return decision

[1901] Output: Updated retention information on the server

[1902] Specific operation: The user clicks the confirmation button on the device and selects the items to keep and the items to return. The device sends this information to the server, which updates the database.

[1903] Step 7: Collect feedback and sentiment data

[1904] Users input their feedback about the items they receive on the device, and emotional data is collected. This information is then sent to the server and stored in a database.

[1905] Input: User feedback, emotional data

[1906] Output: Feedback and emotion data stored in a database

[1907] Specific operation: The user enters their opinion and evaluation score in the feedback form on the device and uploads emotional data (e.g., photos and text). This data is sent to the server, which then stores it in a database.

[1908] Step 8: Training the AI ​​algorithm

[1909] The server updates the AI ​​algorithm based on new feedback and emotion data, which further optimizes the next item selection.

[1910] Input: User feedback, emotional data

[1911] Output: Updated AI algorithm

[1912] How it works: The server inputs the received data into the generative AI model and uses it as new training data. The AI ​​algorithm learns and improves the accuracy of the next item selection.

[1913] (Application example 2)

[1914] 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."

[1915] Conventional fashion coordination systems provide personalization based on the user's preferences, style, and needs, but do not provide advanced personalization based on the user's emotions. This makes it difficult for users to select the optimal fashion items that match their emotions at any given time, and this has led to the issue of not being able to sufficiently increase user satisfaction.

[1916] 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.

[1917] In this invention, the server includes means for executing an artificial intelligence algorithm for selecting optimal items based on the user's personal information and emotion data obtained by an emotion analysis engine, means for displaying the selected items to the user, and means for delivering the items to the user based on a regular delivery schedule. This enables advanced personalization based on the user's emotions and makes it possible to provide the user with the most optimal fashion items.

[1918] Definition of each word

[1919] "Personal information" refers to information that identifies an individual, such as the user's name, address, email address, and telephone number.

[1920] "Preferences" is information about the user's preferences such as colors, styles, and fashion items.

[1921] "Style" is information about the type and design of fashion that the user normally wears.

[1922] "Request" is information about items or services that a user desires for a specific event or purpose.

[1923] "Data" refers to any information processed by the system, including users' personal information, preferences, style, needs, and emotional data.

[1924] A "server" is a computer system for storing and processing data sent by users.

[1925] An "emotion analysis engine" is software that analyzes a user's emotions from input data such as images and text provided by the user and generates data.

[1926] "Emotion data" is information about the user's emotional state obtained by the emotion analysis engine.

[1927] An "artificial intelligence algorithm" is a computer program that selects items suitable for a user through a process of data analysis and learning.

[1928] The "display means" is a function for displaying information about the selected item on the user's terminal screen.

[1929] A "delivery plan" is a schedule for periodically sending selected items to a user.

[1930] "Feedback" refers to the thoughts and opinions of users about the items they receive, which helps improve the system.

[1931] "Mode for Carrying Out the Invention"

[1932] This invention is a system that analyzes a user's emotions and suggests fashion coordination. The system collects and analyzes the user's emotional data, selects optimal fashion items based on that data, and delivers them to the user on a regular basis. The configuration and operation of this system are described below.

[1933] Hardware and software used

[1934] Hardware:

[1935] Smartphone: A device that allows users to input information and interact with systems.

[1936] Server: A system for storing data and running AI algorithms and sentiment analysis engines.

[1937] software:

[1938] Emotion analysis engine (EmotionEngine): Software that analyzes images and text provided by users and generates emotional data.

[1939] Artificial intelligence algorithm (FashionAI): An algorithm that selects fashion items based on a user's personal information, preferences, style, and emotional data.

[1940] Database: Stores and manages users' personal information, preferences, style, feedback, and emotional data.

[1941] Delivery System: Software that executes a schedule for the periodic delivery of selected items to users.

[1942] System Overview

[1943] The server receives personal information and preference data entered by the user and stores the data in a database. The user's emotional data is extracted from the images and text provided by the user using an emotional analysis engine. The server then runs an artificial intelligence algorithm based on the user's personal information, preferences, style, and emotional data to select the most suitable fashion items.

[1944] The selected fashion items are displayed on the user's smartphone, allowing the user to check the items. The selected items are then delivered to the user based on a regular delivery schedule. The user can try on the received items and choose whether to keep them or return them. This selection information and feedback are then sent back to the server and stored in a database.

[1945] The server updates its AI algorithm based on this feedback and emotional data, which will further optimize item selection for the user from the next time onwards.

[1946] Specific examples

[1947] For example, a user may say, "I like a casual style and prefer blue items, but I've been feeling stressed lately." This information and emotional data are sent to the server and analyzed by an AI algorithm. The emotional analysis engine detects the user's stress level and selects blue items that reflect a relaxed style (e.g., loose-fitting shirts and pants). The selected items are delivered to the user periodically based on their subscription plan, and the user can try them on at home, keep them, and return them. The feedback and emotional data are then reflected in the next item selection.

[1948] Example prompts for generative AI models

[1949] Write pseudocode for an app that analyzes user-provided image and text data and recommends personalized fashion items based on sentiment data. Use a sentiment analysis engine, artificial intelligence algorithms, and database classes to select items and schedule deliveries based on user preference information and sentiment data. Include user feedback processing.

[1950] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1951] Program processing flow and specific explanation

[1952] Processing Steps

[1953] Step 1:

[1954] Using their smartphones, users enter personal information and data about their preferences, style, and needs, including their name, address, favorite colors and styles, desired item types, etc. This input data is then sent from the device to a server.

[1955] Input: Personal information, preferences, style, requests

[1956] Output: Data sent to the server

[1957] Specific operation: The user fills in the data in the input form on their smartphone and presses the send button, which sends the data to the server.

[1958] Step 2:

[1959] The server stores the received user personal information and preference related data in a database.

[1960] Input: Received data (personal information, preferences, style, requests)

[1961] Output: Data stored in the database

[1962] Specific behavior: The server validates the received data and stores it in the corresponding fields in the database.

[1963] Step 3:

[1964] For emotion analysis, users input image and text data into their devices and send it to the server. The emotion analysis engine receives this data and performs the analysis.

[1965] Input: image, text data

[1966] Output: Parsed emotion data

[1967] How it works: When a user takes a picture with their smartphone camera or enters text and presses the send button, the data is sent to the server. The emotion analysis engine analyzes this data and generates emotion data.

[1968] Step 4:

[1969] The server stores the emotional data obtained from the emotion analysis engine in a database, combines it with the user's personal information and preference data, and runs an artificial intelligence algorithm to select the most suitable fashion items.

[1970] Input: Emotional data, personal information, preference data

[1971] Output: Selected fashion items

[1972] Specific operation: The server stores the emotional data in a database, and inputs the emotional data, personal information, and preference data into an artificial intelligence algorithm to select the most suitable item.

[1973] Step 5:

[1974] The server sends information about the selected fashion items to the user's smartphone and displays it to the user.

[1975] Input: Selected fashion item

[1976] Output: Item information displayed on the user's smartphone

[1977] Specific operation: The server sends information about the selected item to the smartphone, and the item information is displayed on the smartphone screen.

[1978] Step 6:

[1979] The server delivers the selected items to the user based on a regular delivery plan.

[1980] Input: Selected fashion items, delivery plan

[1981] Output: Items delivered to the user

[1982] Specific operation: The server sends the selected item and the user's address information to the delivery system, and delivery is arranged.

[1983] Step 7:

[1984] The user tries on the received item and chooses whether to keep it or return it, and the selection information is sent to the server.

[1985] Input: Information on whether to keep or return

[1986] Output: Selections sent to the server

[1987] Specific operation: The user selects an option on the smartphone screen and presses the send button, which sends the information to the server.

[1988] Step 8:

[1989] The server stores user selections and feedback in a database and uses it to update the artificial intelligence algorithms.

[1990] Input: Selection information, feedback

[1991] Output: Updated artificial intelligence algorithm

[1992] Specific operation: The server stores the selection information and feedback in a database and adjusts and updates the parameters of the artificial intelligence algorithm based on that information.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] 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.

[1997] FIG. 9 is a diagram illustrating 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 actions 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.

[1998] 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.

[1999] 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).

[2000] 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.

[2001] 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."

[2002] 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.

[2003] 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).

[2004] 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.

[2005] 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.

[2006] 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.

[2007] 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.

[2008] 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.

[2009] 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.

[2010] 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.

[2011] 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.

[2012] 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.

[2013] 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.

[2014] The following is further disclosed regarding the above embodiment.

[2015] (Claim 1)

[2016] a means for inputting user information;

[2017] means for entering information about the user's preferences, style, and needs;

[2018] means for transmitting information about said user and information about their preferences, styles and needs to a server;

[2019] A means for executing an AI algorithm to select the most suitable items based on the user's information;

[2020] means for displaying the selected item to the user;

[2021] A means of delivering items on a recurring basis based on a subscription plan; and

[2022] A way for users to provide feedback on the items they receive and use that feedback to update the AI ​​algorithm.

[2023] A system including:

[2024] (Claim 2)

[2025] The device further includes a means for allowing the user to try on the received item and choose whether to keep it or return it;

[2026] means for transmitting the selected information to a server and updating a database based on the selected information;

[2027] 10. The system of claim 1.

[2028] (Claim 3)

[2029] It also has the means to learn and update its AI algorithms based on user feedback.

[2030] Includes a means to adjust the next item selection based on the above feedback information.

[2031] 10. The system of claim 1.

[2032] "Example 1"

[2033] (Claim 1)

[2034] a means for inputting user information;

[2035] means for entering information about the user's preferences, style, and needs;

[2036] means for transmitting information about said user and information about their preferences, styles and needs to a server;

[2037] means for executing an artificial intelligence model to select the most suitable item based on the user's information;

[2038] means for displaying the selected item to the user;

[2039] A means of delivering items on a recurring basis based on a subscription plan; and

[2040] A means for users to provide feedback about the items they receive and update the AI ​​model accordingly;

[2041] A means for expressing the items selected using the artificial intelligence model in a prompt sentence according to the purpose;

[2042] A system including:

[2043] (Claim 2)

[2044] The device further includes a means for allowing the user to try on the received item and choose whether to keep it or return it;

[2045] means for transmitting the selected information to a server and updating a database based on the selected information;

[2046] 10. The system of claim 1.

[2047] (Claim 3)

[2048] Further comprising means for learning and updating the artificial intelligence model based on user feedback;

[2049] Includes a means to adjust the next item selection based on the above feedback information.

[2050] 10. The system of claim 1.

[2051] "Application Example 1"

[2052] (Claim 1)

[2053] a means for inputting user information;

[2054] means for entering information about the user's preferences, style, and needs;

[2055] means for transmitting information about said user and information about their preferences, styles and needs to a server;

[2056] A means for executing an AI algorithm to select the most suitable items based on the user's information;

[2057] means for displaying the selected item to the user;

[2058] A means of delivering items on a recurring basis based on a subscription plan; and

[2059] A way for users to provide feedback on the items they receive and use that feedback to update the AI ​​algorithm.

[2060] A method in which users input their preferences and feedback using a smartphone, and AI then selects and suggests personalized fashion items based on that.

[2061] A system including:

[2062] (Claim 2)

[2063] The device further includes a means for allowing the user to try on the received item and choose whether to keep it or return it;

[2064] means for transmitting the selected information to a server and updating a database based on the selected information;

[2065] 10. The system of claim 1.

[2066] (Claim 3)

[2067] It also has the means to learn and update the AI ​​algorithm based on user feedback and adjust the next item selection.

[2068] This includes a means to analyze user feedback using a generative AI model and provide the results to the AI ​​algorithm as prompts.

[2069] 10. The system of claim 1.

[2070] "Example 2: Combining Emotion Engines"

[2071] (Claim 1)

[2072] a means for inputting user information;

[2073] means for entering information about the user's preferences, style, and needs;

[2074] means for transmitting information about said user and information about their preferences, styles and needs to a server;

[2075] means for using an emotion recognition engine to analyze user input data and emotion data;

[2076] A means for executing an AI algorithm to select the most suitable item based on the analysis results;

[2077] means for displaying the selected item to the user;

[2078] A means of delivering items on a recurring basis based on a subscription plan; and

[2079] A way for users to provide feedback on the items they receive and use that feedback to update the AI ​​algorithm.

[2080] A system including:

[2081] (Claim 2)

[2082] The device further includes a means for allowing the user to try on the received item and choose whether to keep it or return it;

[2083] means for transmitting the selected information to a server and updating a database based on the selected information;

[2084] 10. The system of claim 1.

[2085] (Claim 3)

[2086] It also has a means to learn and update its AI algorithms based on user feedback and emotional data;

[2087] Includes a means to adjust the next item selection based on the above feedback information.

[2088] 10. The system of claim 1.

[2089] "Application example 2 when combining emotion engines"

[2090] Revised claims based on new inventions

[2091] (Claim 1)

[2092] means for inputting personal information of the user;

[2093] means for inputting data relating to the user's preferences, style and needs;

[2094] means for transmitting data relating to the user's personal information and preferences, style and needs to a server;

[2095] means for executing an artificial intelligence algorithm to select the most suitable item based on the user's personal information and emotion data obtained by an emotion analysis engine;

[2096] means for displaying the selected item to the user;

[2097] means for delivering items to users based on a periodic delivery schedule;

[2098] a means for the user to provide feedback on the received item and update the artificial intelligence algorithm with the emotional data;

[2099] A system including:

[2100] (Claim 2)

[2101] further comprising a means for the user to try on the received item and choose whether to keep or return it;

[2102] The selected data is sent to the server, and the database is updated based on the selected data.

[2103] 10. The system of claim 1.

[2104] (Claim 3)

[2105] further comprising means for learning and updating the artificial intelligence algorithm based on user feedback and emotional data;

[2106] Adjust the next item selection based on the above feedback information and emotion data

[2107] 10. The system of claim 1. [Explanation of symbols]

[2108] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting user information; means for entering information about the user's preferences, style, and needs; means for transmitting information about said user and information about their preferences, styles and needs to a server; A means for executing an AI algorithm to select the most suitable items based on the user's information; means for displaying the selected item to the user; A means of delivering items on a recurring basis based on a subscription plan; and A way for users to provide feedback on the items they receive and use that feedback to update the AI ​​algorithm. A system including:

2. The device further includes a means for allowing the user to try on the received item and choose whether to keep it or return it; means for transmitting the selected information to a server and updating a database based on the selected information; The system of claim 1 .

3. It also has the means to learn and update its AI algorithms based on user feedback. Includes a means to adjust the next item selection based on the above feedback information. The system of claim 1 .

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