System

Abstract

A system is provided.SOLUTION: A system including means for acquiring schedule data of a user, means for acquiring location information of the user, means for acquiring images of clothes and small articles possessed by the user and classifying and storing the images in a database, means for acquiring weather forecast data, means for analyzing characteristics of participants of a conference or an event, means for learning past selections of the user and grasping preferences, means for comprehensively analyzing the acquired data and generating optimal coordination for a specific event, means for displaying the generated coordination proposal to the user, and means for providing an online purchase link.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 modern society, many people own a large number of clothing items for both personal and business use, but they often struggle to determine which outfits are appropriate for specific occasions. This challenge is particularly pronounced in situations where appropriate clothing choices are required, resulting in a tendency to repeatedly choose the same outfits. This problem hinders the maximum use of clothing and hinders maintaining a stylish appearance. Furthermore, the time spent thinking about appropriate outfits can impair the efficiency of daily life. [Means for solving the problem]

[0005] The system of the present invention solves the problem of users choosing outfits by using the following means. First, it provides a means for acquiring the user's schedule data, a means for acquiring location information, and a means for acquiring images of the user's clothing and accessories, which are then classified and stored in a database. It also includes a means for acquiring weather forecast data, a means for analyzing the characteristics of meeting and event attendees, and a means for learning the user's past choices and understanding their preferences. The system then comprehensively analyzes this data to generate the optimal outfit for a specific event, and provides a means for displaying the generated outfit suggestions to the user and a means for providing an online purchase link, thereby enabling quick and appropriate fashion coordination.

[0006] "Schedule data" refers to information including a user's plans and schedules, specifically event information entered in a calendar app or manually.

[0007] "Location information" is geographical information that indicates a user's current location and range of movement, and is obtained through devices such as GPS.

[0008] "Clothing and accessory images" refers to photographs and image data of clothing and accessories owned by the user.

[0009] The "database" is a system for organizing and storing acquired data, including information about the user's clothing and accessories.

[0010] "Weather forecast data" refers to weather forecast information for a specific day or time, and is obtained through weather forecast APIs, etc.

[0011] "Participant characteristics" refers to the attributes and characteristics of other people attending a meeting or event, including information such as age, gender, job position, and relationships.

[0012] Learning "past choices" refers to a means of analyzing and understanding a user's preferences based on the choices and ratings of outfits the user has made in the past.

[0013] "Generate" refers to the act of analyzing acquired data to produce specific results or recommendations.

[0014] "Coordination suggestions" refers to suggesting appropriate outfits based on the user's schedule and other data, and includes advice on specific combinations of clothing and accessories.

[0015] "Online Purchase Link" means the URL or link information that enables the suggested item to be purchased online. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[0038] Overall system configuration

[0039] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[0040] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[0041] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[0042] Program processing flow

[0043] server

[0044] 1. Receiving requests from users

[0045] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[0046] 2. Retrieving schedule data

[0047] The server works with the user's calendar app to obtain schedule data.

[0048] 3. Obtaining location information

[0049] The server obtains the user's location information and verifies the location of the event or meeting.

[0050] 4. Storage and analysis of clothing and accessory images

[0051] The server analyzes the uploaded images and classifies and stores them in a database. Image analysis uses image recognition technology to identify the type, color, and characteristics of the item.

[0052] 5. Obtaining weather forecast data

[0053] The server uses a weather forecast API to obtain weather data for the date and time of the event or meeting.

[0054] 6. Participant characteristics analysis

[0055] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[0056] 7. Learning your preferences

[0057] The server learns the user's preferences based on past outfit choices and ratings, allowing it to make more personalized suggestions.

[0058] 8. Coordination Proposal Generation

[0059] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[0060] 9. Sending outfit suggestions and online purchase links

[0061] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[0062] Terminal

[0063] 1. Launch the app and log in

[0064] The service begins when the user launches the app and logs in.

[0065] 2. Schedule input or synchronization

[0066] Users can enter schedule data using the synchronization function with a calendar app.

[0067] 3. Upload images of clothing and accessories

[0068] Users take pictures of their clothing and accessories and upload them to a server via the app.

[0069] 4. Display of outfit suggestions

[0070] The coordination suggestions sent from the server are displayed on the app.

[0071] 5. Review and evaluation of proposals

[0072] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[0073] 6. Use of online purchases

[0074] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[0075] Specific examples

[0076] 1. For business meetings

[0077] The app starts with a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to retrieve weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[0078] 2. For casual events

[0079] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[0080] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions in response to various user requirements.

[0081] The processing flow will be explained below.

[0082] Step 1:

[0083] The user launches the app and logs in, which loads the user's account information onto the device.

[0084] Step 2:

[0085] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[0086] Step 3:

[0087] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[0088] Step 4:

[0089] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[0090] Step 5:

[0091] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[0092] Step 6:

[0093] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[0094] Step 7:

[0095] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[0096] Step 8:

[0097] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[0098] Step 9:

[0099] The server then performs a comprehensive analysis of all data and generates the optimal coordination for a particular event or meeting, taking into account the weather, the characteristics of the participants, and the user's preferences.

[0100] Step 10:

[0101] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[0102] Step 11:

[0103] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[0104] Step 12:

[0105] Users can prepare the specified items based on the suggested outfits, and can also purchase the required items by clicking on the relevant online purchase links.

[0106] Step 13:

[0107] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[0108] Step 14:

[0109] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[0110] Example 1

[0111] 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."

[0112] Conventional systems have difficulty suggesting optimal outfits based on a user's schedule and belongings, and lack the data collection and analysis necessary to provide personalized suggestions. As a result, they are unable to suggest practical clothing and accessories to users, resulting in monotonous outfit choices and often suggesting outfits that do not match the user's preferences. Furthermore, when providing online purchase links, it is difficult to appropriately suggest products that the user is interested in. This invention aims to solve these problems and build a system that provides optimal and personalized outfit suggestions to users.

[0113] 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.

[0114] In this invention, the server includes means for acquiring a user's schedule data, means for acquiring the user's location information, means for acquiring images of the user's clothing and accessories and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing the characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing an online purchase link, means for linking with the database to be used, and means for modeling the user's preferences using a machine learning algorithm, thereby enabling optimal and personalized outfit suggestions based on the user's individual schedule and preferences.

[0115] "User Schedule Data" means information about the dates, times, and details of events and meetings that a User has entered into a calendar application or to-do list.

[0116] "User location information" refers to information about the geographic location based on GPS data, IP address, etc. obtained from the user's device.

[0117] "Clothing and accessory images" refers to photographic images of items such as clothing and accessories owned by the user.

[0118] "Weather Forecast Data" means forecast information about weather conditions for a particular date, time, and location.

[0119] "Participant characteristics" refers to attribute information that indicates the age, gender, position, relationships, etc. of people attending a meeting or event.

[0120] "User's past choices" refers to the outfits the user has chosen in the past and the evaluation data from those outfits.

[0121] "Coordination suggestions" refers to recommendations of clothing and accessory combinations that are determined to be optimal based on the user's schedule and preferences.

[0122] "Online Purchase Link" means a link to a website where a User can purchase a suggested item.

[0123] A "database" refers to a structured collection of data for efficiently storing, managing, and retrieving information.

[0124] "Machine learning algorithms" refer to mathematical models and methods that allow computers to learn patterns and rules from data and apply them to new data.

[0125] "Image recognition technology" refers to technology for identifying specific objects from images and specifying their attributes.

[0126] "API" stands for Application Program Interface, and refers to an interface for sharing functions and data between different software systems.

[0127] "Cloud storage" refers to a service that stores and manages data on a remote server via the Internet.

[0128] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[0129] server

[0130] The server uses the following hardware and software to collect and analyze user data and generate coordination suggestions.

[0131] Hardware: Cloud servers (e.g., Amazon EC2, Google Cloud Platform)

[0132] software:

[0133] Database: MySQL, PostgreSQL, etc.

[0134] Image analysis: Google Cloud Vision API, Amazon Rekognition

[0135] Weather API: OpenWeatherMap, WeatherAPI

[0136] Machine learning: Scikit-learn, TensorFlow

[0137] Location information acquisition: Google Maps API

[0138] Terminal

[0139] The terminal is used by the user through an application installed on the device (smartphone, tablet, PC, etc.) The terminal has the following functions:

[0140] User schedule data input and synchronization

[0141] Upload images of clothing and accessories

[0142] View and rate outfit suggestions

[0143] Use the online purchase link

[0144] user

[0145] As the final user of the system, the user enters the following data:

[0146] Add a schedule or sync with a calendar app

[0147] Upload images of clothing and accessories

[0148] Check and evaluate outfit suggestions

[0149] Specific operation flow

[0150] 1. For business meetings

[0151] A user launches the app and schedules an important business meeting. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API (e.g., OpenWeatherMap) to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including high-ranking executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[0152] 2. For casual events

[0153] Suppose a user is planning a casual evening out with friends over the weekend. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (sunny weather forecast) using a weather forecast API (e.g., WeatherAPI). The server takes into account the relaxed atmosphere of the event and generates an outfit consisting of denim pants, a white T-shirt, and sneakers. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[0154] Prompt Sentence Examples

[0155] Below is an example of a prompt sentence to input to the generative AI model.

[0156] "I have an important business meeting tomorrow and would like to wear my navy suit, white shirt, and black shoes. The weather forecast predicts rain, and the attendees include my superiors. Please suggest the best outfit based on these conditions."

[0157] "I have a casual evening out with friends this weekend. Can you suggest an outfit that would be suitable for a sunny day using my denim pants, white T-shirt, and sneakers?"

[0158] This invention allows users to receive optimal and personalized outfit suggestions based on their schedules and preferences, enabling them to choose practical and effective outfits.

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1: Receiving a request from the user

[0161] The server receives requests for schedule information and image uploads of clothing and accessories sent by users through the app, specifically as HTTP or HTTPS requests.

[0162] Input: User schedule information, image data

[0163] Data processing: Request analysis, temporary data storage

[0164] Output: Pending requests

[0165] Step 2: Retrieving schedule data

[0166] The server connects to the user's calendar app via API to retrieve schedule data, securely accessing it using OAuth 2.0 authentication.

[0167] Input: User's OAuth token

[0168] Data processing: Sending API requests and retrieving calendar event data

[0169] Output: Schedule data

[0170] Step 3: Obtaining location information

[0171] The server collects location information from users' devices to identify the location of events and meetings, specifically using GPS data and IP addresses.

[0172] Input: GPS data, IP address

[0173] Data processing: Geographical information analysis, event location identification

[0174] Output: Event location data

[0175] Step 4: Saving and analyzing clothing and accessory images

[0176] The server receives the image data uploaded by the user, stores it in cloud storage, and passes the URL to the image recognition API for analysis.

[0177] Input: Image data

[0178] Data processing: Data storage, image recognition API requests

[0179] Output: Item type, color, and characteristics data

[0180] Step 5: Obtaining weather forecast data

[0181] The server sends a request to the weather forecast API based on the specified date, time and location to retrieve the required weather data.

[0182] Input: Event date, time, and location data

[0183] Data processing: Sending API requests, analyzing climate data

[0184] Output: Weather forecast data

[0185] Step 6: Participant characteristics analysis

[0186] The server obtains participant information from a contact database or social media API, and analyzes it to extract characteristics such as age, gender, and job position.

[0187] Input: Participant contact data, social media data

[0188] Data processing: natural language processing, statistical analysis

[0189] Output: Participant characteristics data

[0190] Step 7: Learning user preferences

[0191] The server uses machine learning algorithms to model the user's preferences based on past outfit choices and evaluation data.

[0192] Input: Past coordination data, evaluation data

[0193] Data processing: training machine learning models

[0194] Output: User preference model

[0195] Step 8: Generate outfit suggestions

[0196] The server comprehensively analyzes the various data it acquires and generates the optimal coordination for a specific event or meeting.

[0197] Input: Schedule data, location information, clothing and accessory characteristics data, weather forecast data, participant characteristics data, user preference model

[0198] Data processing: Comprehensive data analysis and application of proposed generation algorithms

[0199] Output: Coordination suggestions

[0200] Step 9: Send outfit suggestions and online purchase links

[0201] The server sends the generated coordination suggestions and online purchase links for the recommended items to the user's device.

[0202] Input: Coordination suggestions

[0203] Data processing: converting the format of the proposed data and generating a purchase link

[0204] Output: Coordination suggestions, purchase link

[0205] Step 10: Launch the app and log in

[0206] The service begins when a user launches the app and logs in. The login information is authenticated using OAuth or JWT tokens.

[0207] Input: Login information

[0208] Data processing: authentication processing, session creation

[0209] Output: Authentication token, home screen

[0210] Step 11: Schedule or Sync

[0211] Users can input schedule data using the sync function with their calendar app, which is then transferred to the server using an API request.

[0212] Input: Schedule data

[0213] Data processing: Synchronization processing, data format conversion

[0214] Output: Input schedule data

[0215] Step 12: Upload clothing and accessory images

[0216] Users take pictures of their clothing and accessories and upload them to the server via the app, using HTTP POST requests.

[0217] Input: Image data

[0218] Data processing: Sending image data, temporarily saving it on the server side

[0219] Output: Upload completion notification

[0220] Step 13: View outfit suggestions

[0221] The app displays outfit suggestions sent from the server, and a push notification notifies the user of the arrival of the suggestions.

[0222] Input: Coordination suggestions

[0223] Data processing: Analyze the proposed data and convert it into a display format

[0224] Output: Show suggestions, notifications

[0225] Step 14: Review and evaluate proposals

[0226] Users can check the proposed outfits and rate them. The rating data is sent to the server and reflected in the next proposal.

[0227] Input: Coordinate rating

[0228] Data processing: Sending evaluation data and saving it to a database

[0229] Output: Evaluation completion notification

[0230] Step 15: Use online purchases

[0231] Users click on the online purchase link included in the suggestion and purchase the desired item using the in-app browser.

[0232] Enter: Purchase Link

[0233] Data processing: opening links, viewing pages in browsers

[0234] Output: Product purchase page

[0235] This allows users to receive efficient and personalized outfit suggestions, enabling them to quickly prepare and purchase based on those suggestions.

[0236] (Application example 1)

[0237] 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."

[0238] Conventional outfit suggestion systems have difficulty making personalized suggestions by comprehensively utilizing multiple factors, such as the user's belongings, schedule, and the characteristics of the event they are attending. Furthermore, they lack a mechanism for providing online purchase links for products related to the suggested outfits, forcing users to go to the trouble of searching for and purchasing the products. A comprehensive system that can solve these issues is needed.

[0239] 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.

[0240] In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing online purchase links, and means for generating and providing to the user purchase links for related products based on the suggested outfits. This allows the user to not only receive personalized outfit suggestions but also easily purchase the suggested products.

[0241] "Means for obtaining user schedule data" refers to means for obtaining the user's calendar app and other schedule information.

[0242] "Means for obtaining user location information" refers to means for obtaining location information of the user's current location or the location of a specific event.

[0243] "Means for acquiring images of clothing and accessories owned by the user and classifying and storing them in a database" refers to means for acquiring images of clothing, accessories, etc. owned by the user and classifying and storing them in a database.

[0244] The "means for acquiring weather forecast data" is a means for acquiring weather information for a specific date, time and location.

[0245] The "means for analyzing characteristics of participants in a meeting or event" is a means for analyzing characteristics such as age, gender, job position, and relationship of participants.

[0246] "Means for learning the user's past choices and understanding preferences" refers to a means for learning the coordinations that the user has selected in the past and their evaluations, and understanding the user's preferences.

[0247] "Means for comprehensively analyzing acquired data and generating the optimal coordination for a specific event" refers to means for comprehensively analyzing collected schedule data, weather information, participant characteristics, user preferences, etc., to generate the optimal coordination for a specific event.

[0248] The "means for displaying the generated coordination proposal to the user" refers to a means for displaying the generated coordination on the user's terminal.

[0249] The "means for providing an online purchase link" is a means for providing a user with an online purchase link for a product related to the suggested coordination.

[0250] "Means for generating a purchase link for a related product based on a proposed coordination and providing it to a user" refers to means for generating a product link based on a proposed coordination and providing it to a user.

[0251] This invention relates to a system that collects and analyzes data on a user's schedule and the items they own, and suggests outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[0252] Overall system configuration

[0253] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[0254] Device: Operated through an application installed by the user on a device (such as a smartphone or tablet).

[0255] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[0256] Program processing flow

[0257] server

[0258] 1. Receiving requests from users

[0259] The server receives requests from users to register schedules and upload images of clothing and accessories. AWS EC2 instances are used for this.

[0260] 2. Retrieving schedule data

[0261] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app.

[0262] 3. Obtaining location information

[0263] The server obtains the user's location information and verifies the location of the event or meeting.

[0264] 4. Storage and analysis of clothing and accessory images

[0265] The server analyzes images of clothing and accessories uploaded by users, classifies them, and stores them in Amazon RDS. TensorFlow is used for image analysis.

[0266] 5. Obtaining weather forecast data

[0267] The server uses the OpenWeatherMap API to retrieve weather forecast data for a specific date, time, and location.

[0268] 6. Participant characteristics analysis

[0269] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[0270] 7. Learning your preferences

[0271] The server learns user preferences based on past outfit choices and ratings, using machine learning algorithms.

[0272] 8. Coordination Proposal Generation

[0273] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[0274] 9. Sending outfit suggestions and online purchase links

[0275] The server uses the Amazon Product Advertising API to send the generated outfit suggestions to the user's device and also provide online purchase links for the recommended items.

[0276] Terminal

[0277] 1. Launch the app and log in

[0278] A user launches the app and logs in, which starts the service.

[0279] 2. Schedule input or synchronization

[0280] Users can enter schedule data using the synchronization function with their calendar app.

[0281] 3. Upload images of clothing and accessories

[0282] Users take pictures of their clothing and accessories and upload them to a server via the app.

[0283] 4. Display of outfit suggestions

[0284] The coordination suggestions sent by the server are displayed in the app.

[0285] 5. Review and evaluation of proposals

[0286] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[0287] 6. Use of online purchases

[0288] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[0289] Specific examples

[0290] For business meetings

[0291] A user launches the app and schedules an important business meeting for the next day. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the meeting attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[0292] Example prompt:

[0293] "Here's a suggested outfit for tomorrow's important business meeting: navy suit, white shirt, and black shoes. If you don't have these items, you can purchase them at the link below."

[0294] (Link: Example: https: / / www.example.com / product / navy-suit)

[0295] For casual events

[0296] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[0297] Example prompt:

[0298] "Here's an outfit suggestion for a casual weekend event: denim pants, a white t-shirt, and sneakers. I also suggest buying an animal print belt to complete the look."

[0299] (Link: Example: https: / / www.example.com / product / animal-patterned-belt)

[0300] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions according to various user requirements.

[0301] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0302] Step 1:

[0303] The user makes a schedule registration request or a clothing / accessory image upload request through the app. The input is the user's schedule information and image data, which are then sent to the server. The server receives the request and starts the data retrieval process.

[0304] Step 2:

[0305] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app. The input is the user's calendar account information, and the output is the schedule data registered in that account. Based on this, the server determines the date, time, and location of events and meetings.

[0306] Step 3:

[0307] The server acquires the user's location information. The input is the location data of an event or meeting, and the output is the location information corresponding to that location. The location information is used for subsequent data acquisition and analysis.

[0308] Step 4:

[0309] The server uses a weather forecast API (e.g., OpenWeatherMap API) to get weather forecast data for a specific date, time, and location. The input is location information and date and time data, and the output is weather information for that date and time. The server stores this weather information in a database.

[0310] Step 5:

[0311] The user takes a photo of clothing or accessories through the app and uploads it to the server. The input is the image data taken by the user, and the output is the image data stored on the server.

[0312] Step 6:

[0313] The server uses TensorFlow image recognition technology to analyze images of clothing and accessories uploaded by users. The input is image data, and the output is data on the analyzed item's type, color, and characteristics. The analysis results are categorized and stored in a database.

[0314] Step 7:

[0315] The server analyzes the participant information for meetings and events. The input is the participant list obtained from the schedule data, and the output is characteristic data of the participants, such as age, gender, position, and relationship. This data is reflected in coordination suggestions.

[0316] Step 8:

[0317] The server learns from the user's past outfit selection data and understands the user's preferences. The input is past selection and evaluation data, and the output is patterns and trends related to the user's preferences. This enables personalized suggestions.

[0318] Step 9:

[0319] The server comprehensively analyzes the schedule data, location information, weather information, participant characteristics, and user preference data it acquires to generate the optimal coordination for a specific event or meeting. The input is this integrated data, and the output is a specific coordination proposal.

[0320] Step 10:

[0321] The server sends the generated coordination proposal to the user's terminal. The input is the generated coordination data, and the output is the coordination proposal displayed on the user's terminal.

[0322] Step 11:

[0323] The server generates a purchase link for related products based on the suggested outfits and provides it to the user. The input is the outfit suggestion data, and the output is information provided to the user along with the online purchase link.

[0324] Through this series of steps, the system can provide users with optimal outfit suggestions and links to purchase related products.

[0325] 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.

[0326] This invention relates to a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[0327] Overall system configuration

[0328] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[0329] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[0330] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[0331] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[0332] Program processing flow

[0333] server

[0334] 1. Receiving requests from users

[0335] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[0336] 2. Retrieving schedule data

[0337] The server works with the user's calendar app to obtain schedule data.

[0338] 3. Obtaining location information

[0339] The server obtains the user's location information and verifies the location of the event or meeting.

[0340] 4. Storage and analysis of clothing and accessory images

[0341] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[0342] 5. Obtaining weather forecast data

[0343] The server uses a weather forecast API to obtain weather data for the day of the event or meeting, which is then used to select appropriate clothing for the suggested outfit.

[0344] 6. Participant characteristics analysis

[0345] The server analyzes information about participants at events and meetings, identifying characteristics such as age, gender, position, and relationships, allowing it to select appropriate outfits for the occasion.

[0346] 7. Learning your preferences

[0347] The server learns the user's preferences based on past outfit selections and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[0348] 8. Acquiring Emotion Data

[0349] The server obtains the user's emotional data from the emotion engine, which is obtained through facial expression recognition and voice analysis.

[0350] 9. Generating Coordination Proposals

[0351] The server comprehensively analyzes all data and generates the optimal coordination for a specific event or meeting, taking into account the weather, participant characteristics, user preferences, and emotional data.

[0352] 10. Sending outfit suggestions and online purchase links

[0353] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[0354] Terminal

[0355] 1. Launch the app and log in

[0356] The service begins when the user launches the app and logs in.

[0357] 2. Schedule input or synchronization

[0358] Users can enter schedule data using the synchronization function with a calendar app.

[0359] 3. Upload images of clothing and accessories

[0360] Users take pictures of their clothing and accessories and upload them to a server via the app.

[0361] 4. Acquiring Emotion Data

[0362] To capture the user's emotional state, the app's emotion engine function performs facial expression recognition and voice analysis.

[0363] 5. Sending Emotional Data

[0364] The acquired emotion data is sent to the server and reflected in the coordination suggestions.

[0365] 6. Display of outfit suggestions

[0366] The coordination suggestions sent from the server are displayed on the app.

[0367] 7. Review and evaluation of proposals

[0368] The user checks the suggested outfits and, if they like them, prepares them accordingly. They also rate the suggestions and send the rating data to the server.

[0369] 8. Use of online purchases

[0370] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[0371] Specific examples

[0372] 1. For business meetings

[0373] The app begins by a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). When generating suggestions, the server also takes into account the user's emotional data obtained from the emotion engine and recommends a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[0374] 2. For casual events

[0375] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers and enters their schedule. The server retrieves weather data for the weekend (a sunny forecast) using a weather forecast API. The server takes into account the relaxed atmosphere of the event and generates a coordination of denim pants, a white T-shirt, and sneakers, incorporating the user's emotional data obtained from the emotion engine. The user proceeds with preparations based on the suggestions and also receives a link to purchase the animal print belt online.

[0376] In this way, the system of the present invention provides appropriate and efficient coordination suggestions in response to various user requirements, and by combining emotional data, it achieves even more personalized suggestions.

[0377] The processing flow will be explained below.

[0378] Step 1:

[0379] The user launches the app and logs in, which loads the user's account information onto the device.

[0380] Step 2:

[0381] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[0382] Step 3:

[0383] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[0384] Step 4:

[0385] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[0386] Step 5:

[0387] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[0388] Step 6:

[0389] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[0390] Step 7:

[0391] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[0392] Step 8:

[0393] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[0394] Step 9:

[0395] The device uses a built-in emotion engine to analyze the user's facial expressions and voice to recognize their emotions. Emotional data is obtained through facial recognition software and voice analysis software.

[0396] Step 10:

[0397] The device transmits the acquired emotion data to the server, which receives the emotion data and reflects it in its coordination suggestions.

[0398] Step 11:

[0399] The server comprehensively analyzes all data (schedules, clothing and accessory images, weather data, participant characteristics, user preferences, and emotional data) and generates the optimal outfit for a specific event or meeting.

[0400] Step 12:

[0401] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[0402] Step 13:

[0403] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[0404] Step 14:

[0405] Users can prepare the specified items based on the suggested outfits and then click on the relevant online purchase links to purchase the items they need.

[0406] Step 15:

[0407] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[0408] Step 16:

[0409] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[0410] Example 2

[0411] 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."

[0412] In modern society, users want to reduce the time and effort required to choose appropriate outfits in their busy daily lives. Furthermore, determining the optimal outfit is difficult, considering many variables, such as schedules, weather, participant characteristics, personal preferences, and even emotional states. Current systems lack coordination suggestions that comprehensively consider these factors, making it difficult to increase user satisfaction.

[0413] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event participants, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit proposal to the user, means for providing an online purchase link, means for acquiring and analyzing emotion data, and means for adjusting the outfit based on the acquired emotion data. This makes it possible to propose outfits that comprehensively consider various factors related to the user.

[0414] "Users" are the end users of this system, who input schedules, upload images, and check and evaluate coordination suggestions.

[0415] "Schedule data" refers to information about appointments and events that the user inputs or synchronizes with a calendar app.

[0416] "Location Information" refers to information about your current geographic location or the location of an event or meeting.

[0417] "Images of clothing and accessories" refers to photos of clothing and accessories owned by the user, which the system uses for analysis.

[0418] "Weather forecast data" refers to forecast information regarding weather conditions for a particular day or location.

[0419] "Participant characteristics" refers to attribute information such as age, gender, job position, and relationship of participants in an event or conference.

[0420] "User's past selections" refers to the selection history of outfits and their evaluation data.

[0421] "Emotional data" refers to information about a user's emotional state, such as data obtained through facial expression recognition or voice analysis.

[0422] "Online purchase link" refers to a link on the Internet where items related to the generated coordination can be purchased.

[0423] "Coordination" refers to the optimal combination of clothing and accessories that takes into account various factors related to the user.

[0424] "Comprehensive analysis" refers to using the various data acquired to perform a series of calculations and analyses to determine the optimal outfit.

[0425] This invention is a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[0426] Overall system configuration

[0427] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[0428] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[0429] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[0430] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[0431] server

[0432] The server receives schedule registration requests and image upload requests for clothing and accessories sent by users through the app. Specifically, it receives data using a RESTful API. At this time, a calendar synchronization tool such as the Google Calendar API is used to obtain the user's schedule data.

[0433] The server then obtains the user's location via a GPS API to confirm the location of events and meetings. It also uses image recognition software such as OpenCV and TensorFlow to analyze the uploaded images of clothing and accessories, storing the results in a database. During the analysis, specific algorithms are used to identify the type, color, and characteristics of the items.

[0434] Additionally, the server uses weather forecast APIs, such as the OpenWeatherMap API, to retrieve weather forecast data, which provides weather information for a given date, time, and location, and is an important factor in choosing appropriate clothing.

[0435] The server also analyzes information about event and conference attendees to identify attributes such as age, gender, and job position. This allows it to select appropriate outfits for each time, place, and occasion (TPO). It uses machine learning algorithms to learn user preferences based on the user's past outfit choices and evaluation data.

[0436] Emotion data is obtained from the emotion engine through facial expression recognition and voice analysis. Emotion recognition APIs (e.g., Amazon Rekognition and Microsoft Azure's Emotion Recognition API) are used to understand the user's emotional state, and this data is also used as part of the analysis.

[0437] The server integrates multiple data sources and uses a multi-criteria analysis algorithm to comprehensively analyze all data and generate optimal outfit suggestions. The resulting outfits and corresponding online purchase links are then sent to the user's device via push notification or email.

[0438] Terminal

[0439] The user launches the app on their smartphone or PC and authenticates themselves on the login screen. The OAuth 2.0 protocol is used for authentication. The authentication information entered by the user is securely sent to the server, and if authentication is successful, an access token is generated.

[0440] By using the sync function with the calendar app, the user's schedule data is entered, and the app retrieves the latest schedule information and sends it to the server.

[0441] Users take pictures of clothing and accessories and upload them to the server through the app. When uploading, the device displays a preview and allows users to confirm the recognized item information before sending it to the server. To acquire the user's emotional state, the camera and microphone are used to collect facial expressions and voice, which are then analyzed in real time.

[0442] The resulting emotion data is sent to the server and reflected in outfit suggestions. The generated outfit suggestions are displayed in the app, and include detailed descriptions and related online purchase links. Users can rate the suggested outfits using the rating button or feedback form, and the data is sent back to the server.

[0443] Specific examples

[0444] 1. For business meetings

[0445] When a user launches the app and schedules an important business meeting, they upload an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the participants (including senior executives). It also obtains emotional data and suggests outfits for the navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[0446] Example prompt: "User has a business meeting. Upload an image of a navy suit, white shirt, and black shoes. Considering that rain is forecast for the next day, suggest outfit suggestions. Also consider including senior executives, and provide online shopping links for appropriate items."

[0447] 2. For casual events

[0448] When a user plans a casual weekend getaway with friends, they upload an image of their denim pants, white T-shirt, and sneakers and enter their schedule. The server retrieves weather data (sunny weather forecast) for the weekend using a weather forecast API. Reflecting the user's relaxed emotional state, the server suggests coordinating denim pants with a white T-shirt and sneakers. It also provides a link to purchase an animal print belt online.

[0449] Example prompt: "The user is planning a casual evening out. Upload an image of jeans, a white t-shirt, and sneakers. Considering the sunny weather forecast for the weekend, we'd like to suggest some outfit suggestions. To create a relaxed vibe, we'd also like to provide online shopping links for the appropriate items."

[0450] As described above, the system of the present invention comprehensively considers various factors of the user, thereby providing more appropriate and personalized coordination suggestions and increasing user satisfaction.

[0451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0452] Step 1:

[0453] The user launches the app and logs in. They provide authentication information (username and password) as input. The device sends a POST request to the authentication API, and the server authenticates them. If authentication is successful, an access token is issued and the user can use the system.

[0454] (Input) Authentication information (user name, password)

[0455] (Processing) POST request to authentication API, authentication on the server

[0456] (Output) Access token

[0457] Step 2:

[0458] The user synchronizes with a calendar app to provide schedule data. The device then connects to a calendar service such as the Google Calendar API, obtains the schedule data, and sends it to the server.

[0459] (Input) Calendar sync request

[0460] (Process) Obtain schedule data from the calendar service and send it to the server

[0461] (Output) Schedule data

[0462] Step 3:

[0463] Users take pictures of their clothing and accessories and upload them to the server through the app. The device receives the image file, displays a preview, and sends it to the server as a POST request.

[0464] (Input) Images of clothing and accessories

[0465] (Processing) Capture images, display previews, and upload to the server

[0466] (Output) Image file

[0467] Step 4:

[0468] The server analyzes the images of the clothing and accessories it receives, using image recognition algorithms (OpenCV and TensorFlow) to identify the type, color, and characteristics of the item and store them in a database.

[0469] (Input) Image file

[0470] (Processing) Analysis using image recognition algorithms, storage in database

[0471] (Output) Analysis results (item type, color, characteristics)

[0472] Step 5:

[0473] The server uses a GPS API to obtain the user's location, which is then converted into specific location information for the event or meeting via a map service API.

[0474] (Input) Location data (longitude and latitude)

[0475] (Processing) Acquisition of location information and acquisition of location information using map service API

[0476] (Output) Specific location information

[0477] Step 6:

[0478] The server uses a weather forecast API to retrieve weather data for the day of the event or meeting, which then suggests appropriate clothing.

[0479] (Input) Location information, date and time

[0480] (Processing) Request to weather forecast API, obtain climate data

[0481] (Output) Weather forecast data

[0482] Step 7:

[0483] The server analyzes information about participants in events and meetings, and uses the participant list to understand characteristics such as age, gender, and job position, and suggests outfits appropriate for the occasion.

[0484] (Input) Participant list

[0485] (Processing) Analysis of participant information

[0486] (Output) Participant characteristics data (age, gender, job position)

[0487] Step 8:

[0488] The server uses machine learning algorithms to learn the user's preferences based on past outfit selections and evaluation data.

[0489] (Input) Past coordination data, evaluation data

[0490] (Processing) Learning by machine learning algorithms

[0491] (Output) User preference model

[0492] Step 9:

[0493] The server obtains the user's emotional data from the emotion engine, analyzes the data obtained through facial expression recognition and voice analysis, and reflects it in the coordination suggestions.

[0494] (Input) facial expression data, voice data

[0495] (Processing) Analysis using emotion recognition API

[0496] (Output) Emotion data

[0497] Step 10:

[0498] The server comprehensively analyzes all data and generates optimal outfit suggestions, taking into account the weather, participants' characteristics, user preferences, and emotional data.

[0499] (Input) Schedule data, location information, weather forecast data, participant characteristics data, user preference model, emotion data

[0500] (Processing) Comprehensive analysis of data, generation of proposals using multi-criteria analysis algorithms

[0501] (Output) Coordination suggestions

[0502] Step 11:

[0503] The server generates a coordination suggestion and sends it to the user's device along with an online purchase link. The server delivers the suggestion via push notification, email, or other methods.

[0504] (Input) Coordination suggestions

[0505] (Processing) Sending suggestions via push notification or email

[0506] (Output) Notification to user device (coordination suggestions and purchase links)

[0507] Step 12:

[0508] The terminal displays the coordination proposals sent from the server, and the user checks the proposals and sends their evaluation data to the server.

[0509] (Input) Coordination suggestions

[0510] (Processing) Display of coordinated suggestions, evaluation input

[0511] (Output) Evaluation data

[0512] Step 13:

[0513] The user purchases the desired item using the online purchase link included in the suggestion, and the device opens the purchasing site using a browser or an in-app web view.

[0514] (Enter) Online purchase link

[0515] (Processing) Clicking on a purchase link, launching a browser or in-app web view

[0516] (Output) Display of purchase site, purchase procedure

[0517] The above are the specific processing steps for carrying out the invention. This system comprehensively considers various factors of the user and provides personalized coordination suggestions.

[0518] (Application example 2)

[0519] 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."

[0520] This invention relates to a system that collects and analyzes a user's schedule, possessions, and emotional data to suggest outfits suitable for specific situations. Conventional outfit suggestion systems make suggestions based on static data such as the user's possessions and schedule, and therefore are unable to respond to the user's real-time emotions or to the immediate needs of a physical store. As a result, it is not possible to improve user satisfaction.

[0521] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing the characteristics of event participants, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit proposal to the user, means for providing an online purchase link, means for recognizing the user's facial expressions and voice in real time and acquiring emotional data, means for generating and adjusting outfit proposals based on the acquired emotional data, and means for making outfit proposals in a physical store that combine the user's clothing with items in the store. This enables autonomous and dynamic outfit proposals that reflect the user's emotions and real-time situation.

[0522] "User schedule data" refers to information that a user has registered in a schedule or calendar app about their daily activities and plans.

[0523] "User Location Information" means geographic data about a user's current location or a specific point.

[0524] "Images of clothing and accessories owned by the user" are photographs or drawings that contain visual information about clothing, accessories, etc. owned by the user.

[0525] "Means of classifying and storing in a database" refers to a technical method of organizing acquired information by category and storing it in a centralized manner.

[0526] "Weather forecast data" is forecast information about weather conditions in a specific area and at a specific time.

[0527] "Characteristics of event participants" are personal characteristics and attributes such as age, gender, job position, and relationship status of people attending a conference or event.

[0528] "Means of learning from a user's past choices and understanding preferences" refers to an algorithm that predicts and learns a user's preferences and tendencies based on the user's previously selected outfits and evaluation data.

[0529] "A means for generating the optimal outfit for a specific event" refers to a technology that comprehensively analyzes acquired data and suggests clothing and styles suitable for specific situations and occasions.

[0530] The "means for displaying the generated coordination proposal to the user" is a technology for visually presenting the proposed coordination on the screen of the user's device.

[0531] "Means for providing online purchase links" refers to technology that provides links where suggested items can be purchased online.

[0532] "Means of recognizing a user's facial expressions and voice in real time and acquiring emotional data" refers to technology that analyzes a user's facial expressions and tone of voice to identify their current emotional state.

[0533] "Means for generating and adjusting coordination suggestions based on acquired emotional data" refers to technology that takes emotional data into consideration to create and modify coordination suggestions that will satisfy the user more.

[0534] "A means for suggesting outfits in physical stores that combine the user's clothing with items in the store" refers to technology that instantly suggests outfits that combine the clothing the user owns with items sold in physical stores.

[0535] The embodiments of the present invention will be described in detail below.

[0536] Overall system configuration

[0537] This system collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. The system operates in cooperation with a server, terminals, users, and an emotion engine.

[0538] Hardware and software used

[0539] Hardware: Smartphone (iOS / Android device), server (cloud service such as AWS or GCP), camera, microphone

[0540] Software: Calendar API (e.g. Google Calendar), OpenCV, Weather API, Google Cloud Speech-to-Text, Microsoft Azure Face API, Machine Learning Algorithms (TensorFlow)

[0541] Server Operation

[0542] The server works as follows:

[0543] 1. Get user schedule data

[0544] The server obtains the user's schedule data via the calendar API.

[0545] 2. Obtaining location information

[0546] Obtain GPS data to determine the user's location.

[0547] 3. Image Acquisition and Analysis of Clothing and Accessories

[0548] Images uploaded by users are analyzed using OpenCV to identify the item's type, color, and characteristics, and this data is then categorized and stored in a database.

[0549] 4. Obtaining weather forecast data

[0550] Use the weather forecast API to get weather data for a specific day.

[0551] 5. Analysis of participant characteristics

[0552] The age, gender, position, and relationships of participants in meetings and events are retrieved from a database and analyzed.

[0553] 6. Learning your preferences

[0554] It uses machine learning algorithms to learn user preferences based on past outfit choices and evaluation data.

[0555] 7. Acquiring Emotion Data

[0556] It uses the Microsoft Azure Face API and Google Cloud Speech-to-Text to obtain emotion data from the user's facial expressions and voice.

[0557] 8. Coordinate Generation

[0558] The acquired data is comprehensively analyzed to generate the best outfit for a specific situation.

[0559] 9. Submitting your proposal and providing an online purchase link

[0560] The generated outfit suggestions are sent to the user's device and a link to purchase online is also provided.

[0561] Device behavior

[0562] The device (smartphone) operates as follows:

[0563] 1. Launch the app and log in

[0564] Users start the service by launching the app and logging in.

[0565] 2. Schedule Sync

[0566] Sync with the calendar app to get schedule data.

[0567] 3. Upload an image

[0568] Take a picture of your items and upload it to the server.

[0569] 4. Acquiring Emotion Data

[0570] It uses the smartphone's camera and microphone to capture user emotion data.

[0571] 5. Receiving and displaying outfit suggestions

[0572] The coordination proposal sent from the server is displayed and an evaluation is submitted.

[0573] Specific examples

[0574] For business meetings

[0575] A user launches the app and schedules an important business meeting. The user uploads an image of a navy suit and a white shirt. The server uses a weather forecast API to obtain weather data for the next day and analyzes the characteristics of the participants. It also takes into account the user's emotional data obtained from the emotion engine, and recommends a navy suit, a white shirt, and black shoes.

[0576] For casual events

[0577] A user is planning a casual weekend getaway with friends. They upload an image of their outfit, including denim pants, a white T-shirt, and sneakers. The server retrieves weather data for the weekend using a weather forecast API. The server then generates a coordinated outfit for the pair, taking into account the atmosphere of the event and incorporating data from the emotion engine.

[0578] Prompt Sentence Examples

[0579] "For meetings scheduled on your calendar, we'll suggest shirts and ties to pair with your navy suit. Here's the perfect outfit, taking into account today's temperature and the characteristics of the attendees. You can also purchase ties online at the link below."

[0580] By combining these technologies, it is possible to realize autonomous and dynamic coordination suggestions that reflect the user's emotions and real-time situation.

[0581] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0582] Step 1: Retrieve schedule data

[0583] The server obtains the user's schedule data through the API of the calendar app (e.g., Google Calendar) used by the user. This schedule data includes details such as event start time, end time, location, and participants. The input is the calendar data obtained through the API, and the output is the schedule data for analysis.

[0584] Step 2: Obtaining location information

[0585] The server uses GPS data to obtain the user's current location, which allows it to identify the location of events and meetings. The input is the user's location, and the output is the coordinate data of the current location.

[0586] Step 3: Image capture and analysis of clothing and accessories

[0587] The device sends images of clothing and accessories uploaded by the user to the server, which analyzes these images and uses OpenCV to identify the item's type, color, and characteristics. The input is the image uploaded by the user, and the output is data on the owned items, including the analysis results.

[0588] Step 4: Obtaining weather forecast data

[0589] The server uses a weather forecast API to retrieve weather data for a specific day, which is then used to select appropriate outfits for the suggested outfits. The input is the date and location of the event or meeting, and the output is the weather data for that day.

[0590] Step 5: Analyze participant characteristics

[0591] The server retrieves and analyzes information about event and conference participants (such as age, gender, job title, and relationship) from a database. The input is a list of participants, and the output is participant characteristic data.

[0592] Step 6: Learning user preferences

[0593] The server learns user preferences based on past outfit selections and rating data. Here, a machine learning algorithm (e.g., TensorFlow) is used. The input is past selection data and rating data, and the output is a trained model of user preferences.

[0594] Step 7: Obtaining emotion data

[0595] The device uses the smartphone's camera and microphone to acquire emotion data from the user's facial expressions and voice. It then analyzes the data using an emotion engine (e.g., Microsoft Azure Face API or Google Cloud Speech-to-Text). The input is the user's real-time facial and voice data, and the output is analyzed emotion data.

[0596] Step 8: Generate coordinates

[0597] The server comprehensively analyzes all acquired data (schedule, location information, items in possession, weather forecast data, participant characteristics, user preferences, and emotional data) and generates the optimal outfit for a specific situation. The input is these multiple data, and the output is the generated outfit proposal.

[0598] Step 9: Submit your proposal and provide an online purchase link

[0599] The server sends the generated outfit suggestions to the user's device and, if necessary, provides an online purchase link. The input is the generated outfit suggestions, and the output is the outfit suggestions and purchase links displayed on the user's device.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Second embodiment]

[0604] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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."

[0616] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[0617] Overall system configuration

[0618] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[0619] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[0620] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[0621] Program processing flow

[0622] server

[0623] 1. Receiving requests from users

[0624] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[0625] 2. Retrieving schedule data

[0626] The server works with the user's calendar app to obtain schedule data.

[0627] 3. Obtaining location information

[0628] The server obtains the user's location information and verifies the location of the event or meeting.

[0629] 4. Storage and analysis of clothing and accessory images

[0630] The server analyzes the uploaded images and classifies and stores them in a database. Image analysis uses image recognition technology to identify the type, color, and characteristics of the item.

[0631] 5. Obtaining weather forecast data

[0632] The server uses a weather forecast API to obtain weather data for the date and time of the event or meeting.

[0633] 6. Participant characteristics analysis

[0634] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[0635] 7. Learning your preferences

[0636] The server learns the user's preferences based on past outfit choices and ratings, allowing it to make more personalized suggestions.

[0637] 8. Coordination Proposal Generation

[0638] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[0639] 9. Sending outfit suggestions and online purchase links

[0640] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[0641] Terminal

[0642] 1. Launch the app and log in

[0643] The service begins when the user launches the app and logs in.

[0644] 2. Schedule input or synchronization

[0645] Users can enter schedule data using the synchronization function with a calendar app.

[0646] 3. Upload images of clothing and accessories

[0647] Users take pictures of their clothing and accessories and upload them to a server via the app.

[0648] 4. Display of outfit suggestions

[0649] The coordination suggestions sent from the server are displayed on the app.

[0650] 5. Review and evaluation of proposals

[0651] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[0652] 6. Use of online purchases

[0653] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[0654] Specific examples

[0655] 1. For business meetings

[0656] The app starts with a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to retrieve weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[0657] 2. For casual events

[0658] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[0659] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions in response to various user requirements.

[0660] The processing flow will be explained below.

[0661] Step 1:

[0662] The user launches the app and logs in, which loads the user's account information onto the device.

[0663] Step 2:

[0664] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[0665] Step 3:

[0666] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[0667] Step 4:

[0668] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[0669] Step 5:

[0670] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[0671] Step 6:

[0672] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[0673] Step 7:

[0674] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[0675] Step 8:

[0676] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[0677] Step 9:

[0678] The server then performs a comprehensive analysis of all data and generates the optimal coordination for a particular event or meeting, taking into account the weather, the characteristics of the participants, and the user's preferences.

[0679] Step 10:

[0680] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[0681] Step 11:

[0682] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[0683] Step 12:

[0684] Users can prepare the specified items based on the suggested outfits, and can also purchase the required items by clicking on the relevant online purchase links.

[0685] Step 13:

[0686] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[0687] Step 14:

[0688] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[0689] Example 1

[0690] 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."

[0691] Conventional systems have difficulty suggesting optimal outfits based on a user's schedule and belongings, and lack the data collection and analysis necessary to provide personalized suggestions. As a result, they are unable to suggest practical clothing and accessories to users, resulting in monotonous outfit choices and often suggesting outfits that do not match the user's preferences. Furthermore, when providing online purchase links, it is difficult to appropriately suggest products that the user is interested in. This invention aims to solve these problems and build a system that provides optimal and personalized outfit suggestions to users.

[0692] 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.

[0693] In this invention, the server includes means for acquiring a user's schedule data, means for acquiring the user's location information, means for acquiring images of the user's clothing and accessories and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing the characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing an online purchase link, means for linking with the database to be used, and means for modeling the user's preferences using a machine learning algorithm, thereby enabling optimal and personalized outfit suggestions based on the user's individual schedule and preferences.

[0694] "User Schedule Data" means information about the dates, times, and details of events and meetings that a User has entered into a calendar application or to-do list.

[0695] "User location information" refers to information about the geographic location based on GPS data, IP address, etc. obtained from the user's device.

[0696] "Clothing and accessory images" refers to photographic images of items such as clothing and accessories owned by the user.

[0697] "Weather Forecast Data" means forecast information about weather conditions for a particular date, time, and location.

[0698] "Participant characteristics" refers to attribute information that indicates the age, gender, position, relationships, etc. of people attending a meeting or event.

[0699] "User's past choices" refers to the outfits the user has chosen in the past and the evaluation data from those outfits.

[0700] "Coordination suggestions" refers to recommendations of clothing and accessory combinations that are determined to be optimal based on the user's schedule and preferences.

[0701] "Online Purchase Link" means a link to a website where a User can purchase a suggested item.

[0702] A "database" refers to a structured collection of data for efficiently storing, managing, and retrieving information.

[0703] "Machine learning algorithms" refer to mathematical models and methods that allow computers to learn patterns and rules from data and apply them to new data.

[0704] "Image recognition technology" refers to technology for identifying specific objects from images and specifying their attributes.

[0705] "API" stands for Application Program Interface, and refers to an interface for sharing functions and data between different software systems.

[0706] "Cloud storage" refers to a service that stores and manages data on a remote server via the Internet.

[0707] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[0708] server

[0709] The server uses the following hardware and software to collect and analyze user data and generate coordination suggestions.

[0710] Hardware: Cloud servers (e.g., Amazon EC2, Google Cloud Platform)

[0711] software:

[0712] Database: MySQL, PostgreSQL, etc.

[0713] Image analysis: Google Cloud Vision API, Amazon Rekognition

[0714] Weather API: OpenWeatherMap, WeatherAPI

[0715] Machine learning: Scikit-learn, TensorFlow

[0716] Location information acquisition: Google Maps API

[0717] Terminal

[0718] The terminal is used by the user through an application installed on the device (smartphone, tablet, PC, etc.) The terminal has the following functions:

[0719] User schedule data input and synchronization

[0720] Upload images of clothing and accessories

[0721] View and rate outfit suggestions

[0722] Use the online purchase link

[0723] user

[0724] As the final user of the system, the user enters the following data:

[0725] Add a schedule or sync with a calendar app

[0726] Upload images of clothing and accessories

[0727] Check and evaluate outfit suggestions

[0728] Specific operation flow

[0729] 1. For business meetings

[0730] A user launches the app and schedules an important business meeting. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API (e.g., OpenWeatherMap) to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including high-ranking executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[0731] 2. For casual events

[0732] Suppose a user is planning a casual evening out with friends over the weekend. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (sunny weather forecast) using a weather forecast API (e.g., WeatherAPI). The server takes into account the relaxed atmosphere of the event and generates an outfit consisting of denim pants, a white T-shirt, and sneakers. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[0733] Prompt Sentence Examples

[0734] Below is an example of a prompt sentence to input to the generative AI model.

[0735] "I have an important business meeting tomorrow and would like to wear my navy suit, white shirt, and black shoes. The weather forecast predicts rain, and the attendees include my superiors. Please suggest the best outfit based on these conditions."

[0736] "I have a casual evening out with friends this weekend. Can you suggest an outfit that would be suitable for a sunny day using my denim pants, white T-shirt, and sneakers?"

[0737] This invention allows users to receive optimal and personalized outfit suggestions based on their schedules and preferences, enabling them to choose practical and effective outfits.

[0738] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0739] Step 1: Receiving a request from the user

[0740] The server receives requests for schedule information and image uploads of clothing and accessories sent by users through the app, specifically as HTTP or HTTPS requests.

[0741] Input: User schedule information, image data

[0742] Data processing: Request analysis, temporary data storage

[0743] Output: Pending requests

[0744] Step 2: Retrieving schedule data

[0745] The server connects to the user's calendar app via API to retrieve schedule data, securely accessing it using OAuth 2.0 authentication.

[0746] Input: User's OAuth token

[0747] Data processing: Sending API requests and retrieving calendar event data

[0748] Output: Schedule data

[0749] Step 3: Obtaining location information

[0750] The server collects location information from users' devices to identify the location of events and meetings, specifically using GPS data and IP addresses.

[0751] Input: GPS data, IP address

[0752] Data processing: Geographical information analysis, event location identification

[0753] Output: Event location data

[0754] Step 4: Saving and analyzing clothing and accessory images

[0755] The server receives the image data uploaded by the user, stores it in cloud storage, and passes the URL to the image recognition API for analysis.

[0756] Input: Image data

[0757] Data processing: Data storage, image recognition API requests

[0758] Output: Item type, color, and characteristics data

[0759] Step 5: Obtaining weather forecast data

[0760] The server sends a request to the weather forecast API based on the specified date, time and location to retrieve the required weather data.

[0761] Input: Event date, time, and location data

[0762] Data processing: Sending API requests, analyzing climate data

[0763] Output: Weather forecast data

[0764] Step 6: Participant characteristics analysis

[0765] The server obtains participant information from a contact database or social media API, and analyzes it to extract characteristics such as age, gender, and job position.

[0766] Input: Participant contact data, social media data

[0767] Data processing: natural language processing, statistical analysis

[0768] Output: Participant characteristics data

[0769] Step 7: Learning user preferences

[0770] The server uses machine learning algorithms to model the user's preferences based on past outfit choices and evaluation data.

[0771] Input: Past coordination data, evaluation data

[0772] Data processing: training machine learning models

[0773] Output: User preference model

[0774] Step 8: Generate outfit suggestions

[0775] The server comprehensively analyzes the various data it acquires and generates the optimal coordination for a specific event or meeting.

[0776] Input: Schedule data, location information, clothing and accessory characteristics data, weather forecast data, participant characteristics data, user preference model

[0777] Data processing: Comprehensive data analysis and application of proposed generation algorithms

[0778] Output: Coordination suggestions

[0779] Step 9: Send outfit suggestions and online purchase links

[0780] The server sends the generated coordination suggestions and online purchase links for the recommended items to the user's device.

[0781] Input: Coordination suggestions

[0782] Data processing: converting the format of the proposed data and generating a purchase link

[0783] Output: Coordination suggestions, purchase link

[0784] Step 10: Launch the app and log in

[0785] The service begins when a user launches the app and logs in. The login information is authenticated using OAuth or JWT tokens.

[0786] Input: Login information

[0787] Data processing: authentication processing, session creation

[0788] Output: Authentication token, home screen

[0789] Step 11: Schedule or Sync

[0790] Users can input schedule data using the sync function with their calendar app, which is then transferred to the server using an API request.

[0791] Input: Schedule data

[0792] Data processing: Synchronization processing, data format conversion

[0793] Output: Input schedule data

[0794] Step 12: Upload clothing and accessory images

[0795] Users take pictures of their clothing and accessories and upload them to the server via the app, using HTTP POST requests.

[0796] Input: Image data

[0797] Data processing: Sending image data, temporarily saving it on the server side

[0798] Output: Upload completion notification

[0799] Step 13: View outfit suggestions

[0800] The app displays outfit suggestions sent from the server, and a push notification notifies the user of the arrival of the suggestions.

[0801] Input: Coordination suggestions

[0802] Data processing: Analyze the proposed data and convert it into a display format

[0803] Output: Show suggestions, notifications

[0804] Step 14: Review and evaluate proposals

[0805] Users can check the proposed outfits and rate them. The rating data is sent to the server and reflected in the next proposal.

[0806] Input: Coordinate rating

[0807] Data processing: Sending evaluation data and saving it to a database

[0808] Output: Evaluation completion notification

[0809] Step 15: Use online purchases

[0810] Users click on the online purchase link included in the suggestion and purchase the desired item using the in-app browser.

[0811] Enter: Purchase Link

[0812] Data processing: opening links, viewing pages in browsers

[0813] Output: Product purchase page

[0814] This allows users to receive efficient and personalized outfit suggestions, enabling them to quickly prepare and purchase based on those suggestions.

[0815] (Application example 1)

[0816] 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."

[0817] Conventional outfit suggestion systems have difficulty making personalized suggestions by comprehensively utilizing multiple factors, such as the user's belongings, schedule, and the characteristics of the event they are attending. Furthermore, they lack a mechanism for providing online purchase links for products related to the suggested outfits, forcing users to go to the trouble of searching for and purchasing the products. A comprehensive system that can solve these issues is needed.

[0818] 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.

[0819] In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing online purchase links, and means for generating and providing to the user purchase links for related products based on the suggested outfits. This allows the user to not only receive personalized outfit suggestions but also easily purchase the suggested products.

[0820] "Means for obtaining user schedule data" refers to means for obtaining the user's calendar app and other schedule information.

[0821] "Means for obtaining user location information" refers to means for obtaining location information of the user's current location or the location of a specific event.

[0822] "Means for acquiring images of clothing and accessories owned by the user and classifying and storing them in a database" refers to means for acquiring images of clothing, accessories, etc. owned by the user and classifying and storing them in a database.

[0823] The "means for acquiring weather forecast data" is a means for acquiring weather information for a specific date, time and location.

[0824] The "means for analyzing characteristics of participants in a meeting or event" is a means for analyzing characteristics such as age, gender, job position, and relationship of participants.

[0825] "Means for learning the user's past choices and understanding preferences" refers to a means for learning the coordinations that the user has selected in the past and their evaluations, and understanding the user's preferences.

[0826] "Means for comprehensively analyzing acquired data and generating the optimal coordination for a specific event" refers to means for comprehensively analyzing collected schedule data, weather information, participant characteristics, user preferences, etc., to generate the optimal coordination for a specific event.

[0827] The "means for displaying the generated coordination proposal to the user" refers to a means for displaying the generated coordination on the user's terminal.

[0828] The "means for providing an online purchase link" is a means for providing a user with an online purchase link for a product related to the suggested coordination.

[0829] "Means for generating a purchase link for a related product based on a proposed coordination and providing it to a user" refers to means for generating a product link based on a proposed coordination and providing it to a user.

[0830] This invention relates to a system that collects and analyzes data on a user's schedule and the items they own, and suggests outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[0831] Overall system configuration

[0832] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[0833] Device: Operated through an application installed by the user on a device (such as a smartphone or tablet).

[0834] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[0835] Program processing flow

[0836] server

[0837] 1. Receiving requests from users

[0838] The server receives requests from users to register schedules and upload images of clothing and accessories. AWS EC2 instances are used for this.

[0839] 2. Retrieving schedule data

[0840] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app.

[0841] 3. Obtaining location information

[0842] The server obtains the user's location information and verifies the location of the event or meeting.

[0843] 4. Storage and analysis of clothing and accessory images

[0844] The server analyzes images of clothing and accessories uploaded by users, classifies them, and stores them in Amazon RDS. TensorFlow is used for image analysis.

[0845] 5. Obtaining weather forecast data

[0846] The server uses the OpenWeatherMap API to retrieve weather forecast data for a specific date, time, and location.

[0847] 6. Participant characteristics analysis

[0848] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[0849] 7. Learning your preferences

[0850] The server learns user preferences based on past outfit choices and ratings, using machine learning algorithms.

[0851] 8. Coordination Proposal Generation

[0852] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[0853] 9. Sending outfit suggestions and online purchase links

[0854] The server uses the Amazon Product Advertising API to send the generated outfit suggestions to the user's device and also provide online purchase links for the recommended items.

[0855] Terminal

[0856] 1. Launch the app and log in

[0857] A user launches the app and logs in, which starts the service.

[0858] 2. Schedule input or synchronization

[0859] Users can enter schedule data using the synchronization function with their calendar app.

[0860] 3. Upload images of clothing and accessories

[0861] Users take pictures of their clothing and accessories and upload them to a server via the app.

[0862] 4. Display of outfit suggestions

[0863] The coordination suggestions sent by the server are displayed in the app.

[0864] 5. Review and evaluation of proposals

[0865] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[0866] 6. Use of online purchases

[0867] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[0868] Specific examples

[0869] For business meetings

[0870] A user launches the app and schedules an important business meeting for the next day. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the meeting attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[0871] Example prompt:

[0872] "Here's a suggested outfit for tomorrow's important business meeting: navy suit, white shirt, and black shoes. If you don't have these items, you can purchase them at the link below."

[0873] (Link: Example: https: / / www.example.com / product / navy-suit)

[0874] For casual events

[0875] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[0876] Example prompt:

[0877] "Here's an outfit suggestion for a casual weekend event: denim pants, a white t-shirt, and sneakers. I also suggest buying an animal print belt to complete the look."

[0878] (Link: Example: https: / / www.example.com / product / animal-patterned-belt)

[0879] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions according to various user requirements.

[0880] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0881] Step 1:

[0882] The user makes a schedule registration request or a clothing / accessory image upload request through the app. The input is the user's schedule information and image data, which are then sent to the server. The server receives the request and starts the data retrieval process.

[0883] Step 2:

[0884] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app. The input is the user's calendar account information, and the output is the schedule data registered in that account. Based on this, the server determines the date, time, and location of events and meetings.

[0885] Step 3:

[0886] The server acquires the user's location information. The input is the location data of an event or meeting, and the output is the location information corresponding to that location. The location information is used for subsequent data acquisition and analysis.

[0887] Step 4:

[0888] The server uses a weather forecast API (e.g., OpenWeatherMap API) to get weather forecast data for a specific date, time, and location. The input is location information and date and time data, and the output is weather information for that date and time. The server stores this weather information in a database.

[0889] Step 5:

[0890] The user takes a photo of clothing or accessories through the app and uploads it to the server. The input is the image data taken by the user, and the output is the image data stored on the server.

[0891] Step 6:

[0892] The server uses TensorFlow image recognition technology to analyze images of clothing and accessories uploaded by users. The input is image data, and the output is data on the analyzed item's type, color, and characteristics. The analysis results are categorized and stored in a database.

[0893] Step 7:

[0894] The server analyzes the participant information for meetings and events. The input is the participant list obtained from the schedule data, and the output is characteristic data of the participants, such as age, gender, position, and relationship. This data is reflected in coordination suggestions.

[0895] Step 8:

[0896] The server learns from the user's past outfit selection data and understands the user's preferences. The input is past selection and evaluation data, and the output is patterns and trends related to the user's preferences. This enables personalized suggestions.

[0897] Step 9:

[0898] The server comprehensively analyzes the schedule data, location information, weather information, participant characteristics, and user preference data it acquires to generate the optimal coordination for a specific event or meeting. The input is this integrated data, and the output is a specific coordination proposal.

[0899] Step 10:

[0900] The server sends the generated coordination proposal to the user's terminal. The input is the generated coordination data, and the output is the coordination proposal displayed on the user's terminal.

[0901] Step 11:

[0902] The server generates a purchase link for related products based on the suggested outfits and provides it to the user. The input is the outfit suggestion data, and the output is information provided to the user along with the online purchase link.

[0903] Through this series of steps, the system can provide users with optimal outfit suggestions and links to purchase related products.

[0904] 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.

[0905] This invention relates to a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[0906] Overall system configuration

[0907] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[0908] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[0909] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[0910] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[0911] Program processing flow

[0912] server

[0913] 1. Receiving requests from users

[0914] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[0915] 2. Retrieving schedule data

[0916] The server works with the user's calendar app to obtain schedule data.

[0917] 3. Obtaining location information

[0918] The server obtains the user's location information and verifies the location of the event or meeting.

[0919] 4. Storage and analysis of clothing and accessory images

[0920] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[0921] 5. Obtaining weather forecast data

[0922] The server uses a weather forecast API to obtain weather data for the day of the event or meeting, which is then used to select appropriate clothing for the suggested outfit.

[0923] 6. Participant characteristics analysis

[0924] The server analyzes information about participants at events and meetings, identifying characteristics such as age, gender, position, and relationships, allowing it to select appropriate outfits for the occasion.

[0925] 7. Learning your preferences

[0926] The server learns the user's preferences based on past outfit selections and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[0927] 8. Acquiring Emotion Data

[0928] The server obtains the user's emotional data from the emotion engine, which is obtained through facial expression recognition and voice analysis.

[0929] 9. Generating Coordination Proposals

[0930] The server comprehensively analyzes all data and generates the optimal coordination for a specific event or meeting, taking into account the weather, participant characteristics, user preferences, and emotional data.

[0931] 10. Sending outfit suggestions and online purchase links

[0932] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[0933] Terminal

[0934] 1. Launch the app and log in

[0935] The service begins when the user launches the app and logs in.

[0936] 2. Schedule input or synchronization

[0937] Users can enter schedule data using the synchronization function with a calendar app.

[0938] 3. Upload images of clothing and accessories

[0939] Users take pictures of their clothing and accessories and upload them to a server via the app.

[0940] 4. Acquiring Emotion Data

[0941] To capture the user's emotional state, the app's emotion engine function performs facial expression recognition and voice analysis.

[0942] 5. Sending Emotional Data

[0943] The acquired emotion data is sent to the server and reflected in the coordination suggestions.

[0944] 6. Display of outfit suggestions

[0945] The coordination suggestions sent from the server are displayed on the app.

[0946] 7. Review and evaluation of proposals

[0947] The user checks the suggested outfits and, if they like them, prepares them accordingly. They also rate the suggestions and send the rating data to the server.

[0948] 8. Use of online purchases

[0949] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[0950] Specific examples

[0951] 1. For business meetings

[0952] The app begins by a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). When generating suggestions, the server also takes into account the user's emotional data obtained from the emotion engine and recommends a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[0953] 2. For casual events

[0954] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers and enters their schedule. The server retrieves weather data for the weekend (a sunny forecast) using a weather forecast API. The server takes into account the relaxed atmosphere of the event and generates a coordination of denim pants, a white T-shirt, and sneakers, incorporating the user's emotional data obtained from the emotion engine. The user proceeds with preparations based on the suggestions and also receives a link to purchase the animal print belt online.

[0955] In this way, the system of the present invention provides appropriate and efficient coordination suggestions in response to various user requirements, and by combining emotional data, it achieves even more personalized suggestions.

[0956] The processing flow will be explained below.

[0957] Step 1:

[0958] The user launches the app and logs in, which loads the user's account information onto the device.

[0959] Step 2:

[0960] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[0961] Step 3:

[0962] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[0963] Step 4:

[0964] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[0965] Step 5:

[0966] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[0967] Step 6:

[0968] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[0969] Step 7:

[0970] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[0971] Step 8:

[0972] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[0973] Step 9:

[0974] The device uses a built-in emotion engine to analyze the user's facial expressions and voice to recognize their emotions. Emotional data is obtained through facial recognition software and voice analysis software.

[0975] Step 10:

[0976] The device transmits the acquired emotion data to the server, which receives the emotion data and reflects it in its coordination suggestions.

[0977] Step 11:

[0978] The server comprehensively analyzes all data (schedules, clothing and accessory images, weather data, participant characteristics, user preferences, and emotional data) and generates the optimal outfit for a specific event or meeting.

[0979] Step 12:

[0980] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[0981] Step 13:

[0982] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[0983] Step 14:

[0984] Users can prepare the specified items based on the suggested outfits and then click on the relevant online purchase links to purchase the items they need.

[0985] Step 15:

[0986] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[0987] Step 16:

[0988] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[0989] Example 2

[0990] 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."

[0991] In modern society, users want to reduce the time and effort required to choose appropriate outfits in their busy daily lives. Furthermore, determining the optimal outfit is difficult, considering many variables, such as schedules, weather, participant characteristics, personal preferences, and even emotional states. Current systems lack coordination suggestions that comprehensively consider these factors, making it difficult to increase user satisfaction.

[0992] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event participants, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit proposal to the user, means for providing an online purchase link, means for acquiring and analyzing emotion data, and means for adjusting the outfit based on the acquired emotion data. This makes it possible to propose outfits that comprehensively consider various factors related to the user.

[0993] "Users" are the end users of this system, who input schedules, upload images, and check and evaluate coordination suggestions.

[0994] "Schedule data" refers to information about appointments and events that the user inputs or synchronizes with a calendar app.

[0995] "Location Information" refers to information about your current geographic location or the location of an event or meeting.

[0996] "Images of clothing and accessories" refers to photos of clothing and accessories owned by the user, which the system uses for analysis.

[0997] "Weather forecast data" refers to forecast information regarding weather conditions for a particular day or location.

[0998] "Participant characteristics" refers to attribute information such as age, gender, job position, and relationship of participants in an event or conference.

[0999] "User's past selections" refers to the selection history of outfits and their evaluation data.

[1000] "Emotional data" refers to information about a user's emotional state, such as data obtained through facial expression recognition or voice analysis.

[1001] "Online purchase link" refers to a link on the Internet where items related to the generated coordination can be purchased.

[1002] "Coordination" refers to the optimal combination of clothing and accessories that takes into account various factors related to the user.

[1003] "Comprehensive analysis" refers to using the various data acquired to perform a series of calculations and analyses to determine the optimal outfit.

[1004] This invention is a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[1005] Overall system configuration

[1006] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[1007] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[1008] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[1009] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[1010] server

[1011] The server receives schedule registration requests and image upload requests for clothing and accessories sent by users through the app. Specifically, it receives data using a RESTful API. At this time, a calendar synchronization tool such as the Google Calendar API is used to obtain the user's schedule data.

[1012] The server then obtains the user's location via a GPS API to confirm the location of events and meetings. It also uses image recognition software such as OpenCV and TensorFlow to analyze the uploaded images of clothing and accessories, storing the results in a database. During the analysis, specific algorithms are used to identify the type, color, and characteristics of the items.

[1013] Additionally, the server uses weather forecast APIs, such as the OpenWeatherMap API, to retrieve weather forecast data, which provides weather information for a given date, time, and location, and is an important factor in choosing appropriate clothing.

[1014] The server also analyzes information about event and conference attendees to identify attributes such as age, gender, and job position. This allows it to select appropriate outfits for each time, place, and occasion (TPO). It uses machine learning algorithms to learn user preferences based on the user's past outfit choices and evaluation data.

[1015] Emotion data is obtained from the emotion engine through facial expression recognition and voice analysis. Emotion recognition APIs (e.g., Amazon Rekognition and Microsoft Azure's Emotion Recognition API) are used to understand the user's emotional state, and this data is also used as part of the analysis.

[1016] The server integrates multiple data sources and uses a multi-criteria analysis algorithm to comprehensively analyze all data and generate optimal outfit suggestions. The resulting outfits and corresponding online purchase links are then sent to the user's device via push notification or email.

[1017] Terminal

[1018] The user launches the app on their smartphone or PC and authenticates themselves on the login screen. The OAuth 2.0 protocol is used for authentication. The authentication information entered by the user is securely sent to the server, and if authentication is successful, an access token is generated.

[1019] By using the sync function with the calendar app, the user's schedule data is entered, and the app retrieves the latest schedule information and sends it to the server.

[1020] Users take pictures of clothing and accessories and upload them to the server through the app. When uploading, the device displays a preview and allows users to confirm the recognized item information before sending it to the server. To acquire the user's emotional state, the camera and microphone are used to collect facial expressions and voice, which are then analyzed in real time.

[1021] The resulting emotion data is sent to the server and reflected in outfit suggestions. The generated outfit suggestions are displayed in the app, and include detailed descriptions and related online purchase links. Users can rate the suggested outfits using the rating button or feedback form, and the data is sent back to the server.

[1022] Specific examples

[1023] 1. For business meetings

[1024] When a user launches the app and schedules an important business meeting, they upload an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the participants (including senior executives). It also obtains emotional data and suggests outfits for the navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[1025] Example prompt: "User has a business meeting. Upload an image of a navy suit, white shirt, and black shoes. Considering that rain is forecast for the next day, suggest outfit suggestions. Also consider including senior executives, and provide online shopping links for appropriate items."

[1026] 2. For casual events

[1027] When a user plans a casual weekend getaway with friends, they upload an image of their denim pants, white T-shirt, and sneakers and enter their schedule. The server retrieves weather data (sunny weather forecast) for the weekend using a weather forecast API. Reflecting the user's relaxed emotional state, the server suggests coordinating denim pants with a white T-shirt and sneakers. It also provides a link to purchase an animal print belt online.

[1028] Example prompt: "The user is planning a casual evening out. Upload an image of jeans, a white t-shirt, and sneakers. Considering the sunny weather forecast for the weekend, we'd like to suggest some outfit suggestions. To create a relaxed vibe, we'd also like to provide online shopping links for the appropriate items."

[1029] As described above, the system of the present invention comprehensively considers various factors of the user, thereby providing more appropriate and personalized coordination suggestions and increasing user satisfaction.

[1030] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1031] Step 1:

[1032] The user launches the app and logs in. They provide authentication information (username and password) as input. The device sends a POST request to the authentication API, and the server authenticates them. If authentication is successful, an access token is issued and the user can use the system.

[1033] (Input) Authentication information (user name, password)

[1034] (Processing) POST request to authentication API, authentication on the server

[1035] (Output) Access token

[1036] Step 2:

[1037] The user synchronizes with a calendar app to provide schedule data. The device then connects to a calendar service such as the Google Calendar API, obtains the schedule data, and sends it to the server.

[1038] (Input) Calendar sync request

[1039] (Process) Obtain schedule data from the calendar service and send it to the server

[1040] (Output) Schedule data

[1041] Step 3:

[1042] Users take pictures of their clothing and accessories and upload them to the server through the app. The device receives the image file, displays a preview, and sends it to the server as a POST request.

[1043] (Input) Images of clothing and accessories

[1044] (Processing) Capture images, display previews, and upload to the server

[1045] (Output) Image file

[1046] Step 4:

[1047] The server analyzes the images of the clothing and accessories it receives, using image recognition algorithms (OpenCV and TensorFlow) to identify the type, color, and characteristics of the item and store them in a database.

[1048] (Input) Image file

[1049] (Processing) Analysis using image recognition algorithms, storage in database

[1050] (Output) Analysis results (item type, color, characteristics)

[1051] Step 5:

[1052] The server uses a GPS API to obtain the user's location, which is then converted into specific location information for the event or meeting via a map service API.

[1053] (Input) Location data (longitude and latitude)

[1054] (Processing) Acquisition of location information and acquisition of location information using map service API

[1055] (Output) Specific location information

[1056] Step 6:

[1057] The server uses a weather forecast API to retrieve weather data for the day of the event or meeting, which then suggests appropriate clothing.

[1058] (Input) Location information, date and time

[1059] (Processing) Request to weather forecast API, obtain climate data

[1060] (Output) Weather forecast data

[1061] Step 7:

[1062] The server analyzes information about participants in events and meetings, and uses the participant list to understand characteristics such as age, gender, and job position, and suggests outfits appropriate for the occasion.

[1063] (Input) Participant list

[1064] (Processing) Analysis of participant information

[1065] (Output) Participant characteristics data (age, gender, job position)

[1066] Step 8:

[1067] The server uses machine learning algorithms to learn the user's preferences based on past outfit selections and evaluation data.

[1068] (Input) Past coordination data, evaluation data

[1069] (Processing) Learning by machine learning algorithms

[1070] (Output) User preference model

[1071] Step 9:

[1072] The server obtains the user's emotional data from the emotion engine, analyzes the data obtained through facial expression recognition and voice analysis, and reflects it in the coordination suggestions.

[1073] (Input) facial expression data, voice data

[1074] (Processing) Analysis using emotion recognition API

[1075] (Output) Emotion data

[1076] Step 10:

[1077] The server comprehensively analyzes all data and generates optimal outfit suggestions, taking into account the weather, participants' characteristics, user preferences, and emotional data.

[1078] (Input) Schedule data, location information, weather forecast data, participant characteristics data, user preference model, emotion data

[1079] (Processing) Comprehensive analysis of data, generation of proposals using multi-criteria analysis algorithms

[1080] (Output) Coordination suggestions

[1081] Step 11:

[1082] The server generates a coordination suggestion and sends it to the user's device along with an online purchase link. The server delivers the suggestion via push notification, email, or other methods.

[1083] (Input) Coordination suggestions

[1084] (Processing) Sending suggestions via push notification or email

[1085] (Output) Notification to user device (coordination suggestions and purchase links)

[1086] Step 12:

[1087] The terminal displays the coordination proposals sent from the server, and the user checks the proposals and sends their evaluation data to the server.

[1088] (Input) Coordination suggestions

[1089] (Processing) Display of coordinated suggestions, evaluation input

[1090] (Output) Evaluation data

[1091] Step 13:

[1092] The user purchases the desired item using the online purchase link included in the suggestion, and the device opens the purchasing site using a browser or an in-app web view.

[1093] (Enter) Online purchase link

[1094] (Processing) Clicking on a purchase link, launching a browser or in-app web view

[1095] (Output) Display of purchase site, purchase procedure

[1096] The above are the specific processing steps for carrying out the invention. This system comprehensively considers various factors of the user and provides personalized coordination suggestions.

[1097] (Application example 2)

[1098] 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."

[1099] This invention relates to a system that collects and analyzes a user's schedule, possessions, and emotional data to suggest outfits suitable for specific situations. Conventional outfit suggestion systems make suggestions based on static data such as the user's possessions and schedule, and therefore are unable to respond to the user's real-time emotions or to the immediate needs of a physical store. As a result, it is not possible to improve user satisfaction.

[1100] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing the characteristics of event participants, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit proposal to the user, means for providing an online purchase link, means for recognizing the user's facial expressions and voice in real time and acquiring emotional data, means for generating and adjusting outfit proposals based on the acquired emotional data, and means for making outfit proposals in a physical store that combine the user's clothing with items in the store. This enables autonomous and dynamic outfit proposals that reflect the user's emotions and real-time situation.

[1101] "User schedule data" refers to information that a user has registered in a schedule or calendar app about their daily activities and plans.

[1102] "User Location Information" means geographic data about a user's current location or a specific point.

[1103] "Images of clothing and accessories owned by the user" are photographs or drawings that contain visual information about clothing, accessories, etc. owned by the user.

[1104] "Means of classifying and storing in a database" refers to a technical method of organizing acquired information by category and storing it in a centralized manner.

[1105] "Weather forecast data" is forecast information about weather conditions in a specific area and at a specific time.

[1106] "Characteristics of event participants" are personal characteristics and attributes such as age, gender, job position, and relationship status of people attending a conference or event.

[1107] "Means of learning from a user's past choices and understanding preferences" refers to an algorithm that predicts and learns a user's preferences and tendencies based on the user's previously selected outfits and evaluation data.

[1108] "A means for generating the optimal outfit for a specific event" refers to a technology that comprehensively analyzes acquired data and suggests clothing and styles suitable for specific situations and occasions.

[1109] The "means for displaying the generated coordination proposal to the user" is a technology for visually presenting the proposed coordination on the screen of the user's device.

[1110] "Means for providing online purchase links" refers to technology that provides links where suggested items can be purchased online.

[1111] "Means of recognizing a user's facial expressions and voice in real time and acquiring emotional data" refers to technology that analyzes a user's facial expressions and tone of voice to identify their current emotional state.

[1112] "Means for generating and adjusting coordination suggestions based on acquired emotional data" refers to technology that takes emotional data into consideration to create and modify coordination suggestions that will satisfy the user more.

[1113] "A means for suggesting outfits in physical stores that combine the user's clothing with items in the store" refers to technology that instantly suggests outfits that combine the clothing the user owns with items sold in physical stores.

[1114] The embodiments of the present invention will be described in detail below.

[1115] Overall system configuration

[1116] This system collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. The system operates in cooperation with a server, terminals, users, and an emotion engine.

[1117] Hardware and software used

[1118] Hardware: Smartphone (iOS / Android device), server (cloud service such as AWS or GCP), camera, microphone

[1119] Software: Calendar API (e.g. Google Calendar), OpenCV, Weather API, Google Cloud Speech-to-Text, Microsoft Azure Face API, Machine Learning Algorithms (TensorFlow)

[1120] Server Operation

[1121] The server works as follows:

[1122] 1. Get user schedule data

[1123] The server obtains the user's schedule data via the calendar API.

[1124] 2. Obtaining location information

[1125] Obtain GPS data to determine the user's location.

[1126] 3. Image Acquisition and Analysis of Clothing and Accessories

[1127] Images uploaded by users are analyzed using OpenCV to identify the item's type, color, and characteristics, and this data is then categorized and stored in a database.

[1128] 4. Obtaining weather forecast data

[1129] Use the weather forecast API to get weather data for a specific day.

[1130] 5. Analysis of participant characteristics

[1131] The age, gender, position, and relationships of participants in meetings and events are retrieved from a database and analyzed.

[1132] 6. Learning your preferences

[1133] It uses machine learning algorithms to learn user preferences based on past outfit choices and evaluation data.

[1134] 7. Acquiring Emotion Data

[1135] It uses the Microsoft Azure Face API and Google Cloud Speech-to-Text to obtain emotion data from the user's facial expressions and voice.

[1136] 8. Coordinate Generation

[1137] The acquired data is comprehensively analyzed to generate the best outfit for a specific situation.

[1138] 9. Submitting your proposal and providing an online purchase link

[1139] The generated outfit suggestions are sent to the user's device and a link to purchase online is also provided.

[1140] Device behavior

[1141] The device (smartphone) operates as follows:

[1142] 1. Launch the app and log in

[1143] Users start the service by launching the app and logging in.

[1144] 2. Schedule Sync

[1145] Sync with the calendar app to get schedule data.

[1146] 3. Upload an image

[1147] Take a picture of your items and upload it to the server.

[1148] 4. Acquiring Emotion Data

[1149] It uses the smartphone's camera and microphone to capture user emotion data.

[1150] 5. Receiving and displaying outfit suggestions

[1151] The coordination proposal sent from the server is displayed and an evaluation is submitted.

[1152] Specific examples

[1153] For business meetings

[1154] A user launches the app and schedules an important business meeting. The user uploads an image of a navy suit and a white shirt. The server uses a weather forecast API to obtain weather data for the next day and analyzes the characteristics of the participants. It also takes into account the user's emotional data obtained from the emotion engine, and recommends a navy suit, a white shirt, and black shoes.

[1155] For casual events

[1156] A user is planning a casual weekend getaway with friends. They upload an image of their outfit, including denim pants, a white T-shirt, and sneakers. The server retrieves weather data for the weekend using a weather forecast API. The server then generates a coordinated outfit for the pair, taking into account the atmosphere of the event and incorporating data from the emotion engine.

[1157] Prompt Sentence Examples

[1158] "For meetings scheduled on your calendar, we'll suggest shirts and ties to pair with your navy suit. Here's the perfect outfit, taking into account today's temperature and the characteristics of the attendees. You can also purchase ties online at the link below."

[1159] By combining these technologies, it is possible to realize autonomous and dynamic coordination suggestions that reflect the user's emotions and real-time situation.

[1160] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1161] Step 1: Retrieve schedule data

[1162] The server obtains the user's schedule data through the API of the calendar app (e.g., Google Calendar) used by the user. This schedule data includes details such as event start time, end time, location, and participants. The input is the calendar data obtained through the API, and the output is the schedule data for analysis.

[1163] Step 2: Obtaining location information

[1164] The server uses GPS data to obtain the user's current location, which allows it to identify the location of events and meetings. The input is the user's location, and the output is the coordinate data of the current location.

[1165] Step 3: Image capture and analysis of clothing and accessories

[1166] The device sends images of clothing and accessories uploaded by the user to the server, which analyzes these images and uses OpenCV to identify the item's type, color, and characteristics. The input is the image uploaded by the user, and the output is data on the owned items, including the analysis results.

[1167] Step 4: Obtaining weather forecast data

[1168] The server uses a weather forecast API to retrieve weather data for a specific day, which is then used to select appropriate outfits for the suggested outfits. The input is the date and location of the event or meeting, and the output is the weather data for that day.

[1169] Step 5: Analyze participant characteristics

[1170] The server retrieves and analyzes information about event and conference participants (such as age, gender, job title, and relationship) from a database. The input is a list of participants, and the output is participant characteristic data.

[1171] Step 6: Learning user preferences

[1172] The server learns user preferences based on past outfit selections and rating data. Here, a machine learning algorithm (e.g., TensorFlow) is used. The input is past selection data and rating data, and the output is a trained model of user preferences.

[1173] Step 7: Obtaining emotion data

[1174] The device uses the smartphone's camera and microphone to acquire emotion data from the user's facial expressions and voice. It then analyzes the data using an emotion engine (e.g., Microsoft Azure Face API or Google Cloud Speech-to-Text). The input is the user's real-time facial and voice data, and the output is analyzed emotion data.

[1175] Step 8: Generate coordinates

[1176] The server comprehensively analyzes all acquired data (schedule, location information, items in possession, weather forecast data, participant characteristics, user preferences, and emotional data) and generates the optimal outfit for a specific situation. The input is these multiple data, and the output is the generated outfit proposal.

[1177] Step 9: Submit your proposal and provide an online purchase link

[1178] The server sends the generated outfit suggestions to the user's device and, if necessary, provides an online purchase link. The input is the generated outfit suggestions, and the output is the outfit suggestions and purchase links displayed on the user's device.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] [Third embodiment]

[1183] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1184] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1185] 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).

[1186] 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.

[1187] 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.

[1188] 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).

[1189] 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.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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."

[1195] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[1196] Overall system configuration

[1197] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[1198] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[1199] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[1200] Program processing flow

[1201] server

[1202] 1. Receiving requests from users

[1203] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[1204] 2. Retrieving schedule data

[1205] The server works with the user's calendar app to obtain schedule data.

[1206] 3. Obtaining location information

[1207] The server obtains the user's location information and verifies the location of the event or meeting.

[1208] 4. Storage and analysis of clothing and accessory images

[1209] The server analyzes the uploaded images and classifies and stores them in a database. Image analysis uses image recognition technology to identify the type, color, and characteristics of the item.

[1210] 5. Obtaining weather forecast data

[1211] The server uses a weather forecast API to obtain weather data for the date and time of the event or meeting.

[1212] 6. Participant characteristics analysis

[1213] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[1214] 7. Learning your preferences

[1215] The server learns the user's preferences based on past outfit choices and ratings, allowing it to make more personalized suggestions.

[1216] 8. Coordination Proposal Generation

[1217] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[1218] 9. Sending outfit suggestions and online purchase links

[1219] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[1220] Terminal

[1221] 1. Launch the app and log in

[1222] The service begins when the user launches the app and logs in.

[1223] 2. Schedule input or synchronization

[1224] Users can enter schedule data using the synchronization function with a calendar app.

[1225] 3. Upload images of clothing and accessories

[1226] Users take pictures of their clothing and accessories and upload them to a server via the app.

[1227] 4. Display of outfit suggestions

[1228] The coordination suggestions sent from the server are displayed on the app.

[1229] 5. Review and evaluation of proposals

[1230] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[1231] 6. Use of online purchases

[1232] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[1233] Specific examples

[1234] 1. For business meetings

[1235] The app starts with a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to retrieve weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[1236] 2. For casual events

[1237] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[1238] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions in response to various user requirements.

[1239] The processing flow will be explained below.

[1240] Step 1:

[1241] The user launches the app and logs in, which loads the user's account information onto the device.

[1242] Step 2:

[1243] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[1244] Step 3:

[1245] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[1246] Step 4:

[1247] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[1248] Step 5:

[1249] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[1250] Step 6:

[1251] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[1252] Step 7:

[1253] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[1254] Step 8:

[1255] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[1256] Step 9:

[1257] The server then performs a comprehensive analysis of all data and generates the optimal coordination for a particular event or meeting, taking into account the weather, the characteristics of the participants, and the user's preferences.

[1258] Step 10:

[1259] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[1260] Step 11:

[1261] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[1262] Step 12:

[1263] Users can prepare the specified items based on the suggested outfits, and can also purchase the required items by clicking on the relevant online purchase links.

[1264] Step 13:

[1265] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[1266] Step 14:

[1267] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[1268] Example 1

[1269] 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."

[1270] Conventional systems have difficulty suggesting optimal outfits based on a user's schedule and belongings, and lack the data collection and analysis necessary to provide personalized suggestions. As a result, they are unable to suggest practical clothing and accessories to users, resulting in monotonous outfit choices and often suggesting outfits that do not match the user's preferences. Furthermore, when providing online purchase links, it is difficult to appropriately suggest products that the user is interested in. This invention aims to solve these problems and build a system that provides optimal and personalized outfit suggestions to users.

[1271] 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.

[1272] In this invention, the server includes means for acquiring a user's schedule data, means for acquiring the user's location information, means for acquiring images of the user's clothing and accessories and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing the characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing an online purchase link, means for linking with the database to be used, and means for modeling the user's preferences using a machine learning algorithm, thereby enabling optimal and personalized outfit suggestions based on the user's individual schedule and preferences.

[1273] "User Schedule Data" means information about the dates, times, and details of events and meetings that a User has entered into a calendar application or to-do list.

[1274] "User location information" refers to information about the geographic location based on GPS data, IP address, etc. obtained from the user's device.

[1275] "Clothing and accessory images" refers to photographic images of items such as clothing and accessories owned by the user.

[1276] "Weather Forecast Data" means forecast information about weather conditions for a particular date, time, and location.

[1277] "Participant characteristics" refers to attribute information that indicates the age, gender, position, relationships, etc. of people attending a meeting or event.

[1278] "User's past choices" refers to the outfits the user has chosen in the past and the evaluation data from those outfits.

[1279] "Coordination suggestions" refers to recommendations of clothing and accessory combinations that are determined to be optimal based on the user's schedule and preferences.

[1280] "Online Purchase Link" means a link to a website where a User can purchase a suggested item.

[1281] A "database" refers to a structured collection of data for efficiently storing, managing, and retrieving information.

[1282] "Machine learning algorithms" refer to mathematical models and methods that allow computers to learn patterns and rules from data and apply them to new data.

[1283] "Image recognition technology" refers to technology for identifying specific objects from images and specifying their attributes.

[1284] "API" stands for Application Program Interface, and refers to an interface for sharing functions and data between different software systems.

[1285] "Cloud storage" refers to a service that stores and manages data on a remote server via the Internet.

[1286] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[1287] server

[1288] The server uses the following hardware and software to collect and analyze user data and generate coordination suggestions.

[1289] Hardware: Cloud servers (e.g., Amazon EC2, Google Cloud Platform)

[1290] software:

[1291] Database: MySQL, PostgreSQL, etc.

[1292] Image analysis: Google Cloud Vision API, Amazon Rekognition

[1293] Weather API: OpenWeatherMap, WeatherAPI

[1294] Machine learning: Scikit-learn, TensorFlow

[1295] Location information acquisition: Google Maps API

[1296] Terminal

[1297] The terminal is used by the user through an application installed on the device (smartphone, tablet, PC, etc.) The terminal has the following functions:

[1298] User schedule data input and synchronization

[1299] Upload images of clothing and accessories

[1300] View and rate outfit suggestions

[1301] Use the online purchase link

[1302] user

[1303] As the final user of the system, the user enters the following data:

[1304] Add a schedule or sync with a calendar app

[1305] Upload images of clothing and accessories

[1306] Check and evaluate outfit suggestions

[1307] Specific operation flow

[1308] 1. For business meetings

[1309] A user launches the app and schedules an important business meeting. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API (e.g., OpenWeatherMap) to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including high-ranking executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[1310] 2. For casual events

[1311] Suppose a user is planning a casual evening out with friends over the weekend. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (sunny weather forecast) using a weather forecast API (e.g., WeatherAPI). The server takes into account the relaxed atmosphere of the event and generates an outfit consisting of denim pants, a white T-shirt, and sneakers. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[1312] Prompt Sentence Examples

[1313] Below is an example of a prompt sentence to input to the generative AI model.

[1314] "I have an important business meeting tomorrow and would like to wear my navy suit, white shirt, and black shoes. The weather forecast predicts rain, and the attendees include my superiors. Please suggest the best outfit based on these conditions."

[1315] "I have a casual evening out with friends this weekend. Can you suggest an outfit that would be suitable for a sunny day using my denim pants, white T-shirt, and sneakers?"

[1316] This invention allows users to receive optimal and personalized outfit suggestions based on their schedules and preferences, enabling them to choose practical and effective outfits.

[1317] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1318] Step 1: Receiving a request from the user

[1319] The server receives requests for schedule information and image uploads of clothing and accessories sent by users through the app, specifically as HTTP or HTTPS requests.

[1320] Input: User schedule information, image data

[1321] Data processing: Request analysis, temporary data storage

[1322] Output: Pending requests

[1323] Step 2: Retrieving schedule data

[1324] The server connects to the user's calendar app via API to retrieve schedule data, securely accessing it using OAuth 2.0 authentication.

[1325] Input: User's OAuth token

[1326] Data processing: Sending API requests and retrieving calendar event data

[1327] Output: Schedule data

[1328] Step 3: Obtaining location information

[1329] The server collects location information from users' devices to identify the location of events and meetings, specifically using GPS data and IP addresses.

[1330] Input: GPS data, IP address

[1331] Data processing: Geographical information analysis, event location identification

[1332] Output: Event location data

[1333] Step 4: Saving and analyzing clothing and accessory images

[1334] The server receives the image data uploaded by the user, stores it in cloud storage, and passes the URL to the image recognition API for analysis.

[1335] Input: Image data

[1336] Data processing: Data storage, image recognition API requests

[1337] Output: Item type, color, and characteristics data

[1338] Step 5: Obtaining weather forecast data

[1339] The server sends a request to the weather forecast API based on the specified date, time and location to retrieve the required weather data.

[1340] Input: Event date, time, and location data

[1341] Data processing: Sending API requests, analyzing climate data

[1342] Output: Weather forecast data

[1343] Step 6: Participant characteristics analysis

[1344] The server obtains participant information from a contact database or social media API, and analyzes it to extract characteristics such as age, gender, and job position.

[1345] Input: Participant contact data, social media data

[1346] Data processing: natural language processing, statistical analysis

[1347] Output: Participant characteristics data

[1348] Step 7: Learning user preferences

[1349] The server uses machine learning algorithms to model the user's preferences based on past outfit choices and evaluation data.

[1350] Input: Past coordination data, evaluation data

[1351] Data processing: training machine learning models

[1352] Output: User preference model

[1353] Step 8: Generate outfit suggestions

[1354] The server comprehensively analyzes the various data it acquires and generates the optimal coordination for a specific event or meeting.

[1355] Input: Schedule data, location information, clothing and accessory characteristics data, weather forecast data, participant characteristics data, user preference model

[1356] Data processing: Comprehensive data analysis and application of proposed generation algorithms

[1357] Output: Coordination suggestions

[1358] Step 9: Send outfit suggestions and online purchase links

[1359] The server sends the generated coordination suggestions and online purchase links for the recommended items to the user's device.

[1360] Input: Coordination suggestions

[1361] Data processing: converting the format of the proposed data and generating a purchase link

[1362] Output: Coordination suggestions, purchase link

[1363] Step 10: Launch the app and log in

[1364] The service begins when a user launches the app and logs in. The login information is authenticated using OAuth or JWT tokens.

[1365] Input: Login information

[1366] Data processing: authentication processing, session creation

[1367] Output: Authentication token, home screen

[1368] Step 11: Schedule or Sync

[1369] Users can input schedule data using the sync function with their calendar app, which is then transferred to the server using an API request.

[1370] Input: Schedule data

[1371] Data processing: Synchronization processing, data format conversion

[1372] Output: Input schedule data

[1373] Step 12: Upload clothing and accessory images

[1374] Users take pictures of their clothing and accessories and upload them to the server via the app, using HTTP POST requests.

[1375] Input: Image data

[1376] Data processing: Sending image data, temporarily saving it on the server side

[1377] Output: Upload completion notification

[1378] Step 13: View outfit suggestions

[1379] The app displays outfit suggestions sent from the server, and a push notification notifies the user of the arrival of the suggestions.

[1380] Input: Coordination suggestions

[1381] Data processing: Analyze the proposed data and convert it into a display format

[1382] Output: Show suggestions, notifications

[1383] Step 14: Review and evaluate proposals

[1384] Users can check the proposed outfits and rate them. The rating data is sent to the server and reflected in the next proposal.

[1385] Input: Coordinate rating

[1386] Data processing: Sending evaluation data and saving it to a database

[1387] Output: Evaluation completion notification

[1388] Step 15: Use online purchases

[1389] Users click on the online purchase link included in the suggestion and purchase the desired item using the in-app browser.

[1390] Enter: Purchase Link

[1391] Data processing: opening links, viewing pages in browsers

[1392] Output: Product purchase page

[1393] This allows users to receive efficient and personalized outfit suggestions, enabling them to quickly prepare and purchase based on those suggestions.

[1394] (Application example 1)

[1395] 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."

[1396] Conventional outfit suggestion systems have difficulty making personalized suggestions by comprehensively utilizing multiple factors, such as the user's belongings, schedule, and the characteristics of the event they are attending. Furthermore, they lack a mechanism for providing online purchase links for products related to the suggested outfits, forcing users to go to the trouble of searching for and purchasing the products. A comprehensive system that can solve these issues is needed.

[1397] 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.

[1398] In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing online purchase links, and means for generating and providing to the user purchase links for related products based on the suggested outfits. This allows the user to not only receive personalized outfit suggestions but also easily purchase the suggested products.

[1399] "Means for obtaining user schedule data" refers to means for obtaining the user's calendar app and other schedule information.

[1400] "Means for obtaining user location information" refers to means for obtaining location information of the user's current location or the location of a specific event.

[1401] "Means for acquiring images of clothing and accessories owned by the user and classifying and storing them in a database" refers to means for acquiring images of clothing, accessories, etc. owned by the user and classifying and storing them in a database.

[1402] The "means for acquiring weather forecast data" is a means for acquiring weather information for a specific date, time and location.

[1403] The "means for analyzing characteristics of participants in a meeting or event" is a means for analyzing characteristics such as age, gender, job position, and relationship of participants.

[1404] "Means for learning the user's past choices and understanding preferences" refers to a means for learning the coordinations that the user has selected in the past and their evaluations, and understanding the user's preferences.

[1405] "Means for comprehensively analyzing acquired data and generating the optimal coordination for a specific event" refers to means for comprehensively analyzing collected schedule data, weather information, participant characteristics, user preferences, etc., to generate the optimal coordination for a specific event.

[1406] The "means for displaying the generated coordination proposal to the user" refers to a means for displaying the generated coordination on the user's terminal.

[1407] The "means for providing an online purchase link" is a means for providing a user with an online purchase link for a product related to the suggested coordination.

[1408] "Means for generating a purchase link for a related product based on a proposed coordination and providing it to a user" refers to means for generating a product link based on a proposed coordination and providing it to a user.

[1409] This invention relates to a system that collects and analyzes data on a user's schedule and the items they own, and suggests outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[1410] Overall system configuration

[1411] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[1412] Device: Operated through an application installed by the user on a device (such as a smartphone or tablet).

[1413] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[1414] Program processing flow

[1415] server

[1416] 1. Receiving requests from users

[1417] The server receives requests from users to register schedules and upload images of clothing and accessories. AWS EC2 instances are used for this.

[1418] 2. Retrieving schedule data

[1419] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app.

[1420] 3. Obtaining location information

[1421] The server obtains the user's location information and verifies the location of the event or meeting.

[1422] 4. Storage and analysis of clothing and accessory images

[1423] The server analyzes images of clothing and accessories uploaded by users, classifies them, and stores them in Amazon RDS. TensorFlow is used for image analysis.

[1424] 5. Obtaining weather forecast data

[1425] The server uses the OpenWeatherMap API to retrieve weather forecast data for a specific date, time, and location.

[1426] 6. Participant characteristics analysis

[1427] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[1428] 7. Learning your preferences

[1429] The server learns user preferences based on past outfit choices and ratings, using machine learning algorithms.

[1430] 8. Coordination Proposal Generation

[1431] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[1432] 9. Sending outfit suggestions and online purchase links

[1433] The server uses the Amazon Product Advertising API to send the generated outfit suggestions to the user's device and also provide online purchase links for the recommended items.

[1434] Terminal

[1435] 1. Launch the app and log in

[1436] A user launches the app and logs in, which starts the service.

[1437] 2. Schedule input or synchronization

[1438] Users can enter schedule data using the synchronization function with their calendar app.

[1439] 3. Upload images of clothing and accessories

[1440] Users take pictures of their clothing and accessories and upload them to a server via the app.

[1441] 4. Display of outfit suggestions

[1442] The coordination suggestions sent by the server are displayed in the app.

[1443] 5. Review and evaluation of proposals

[1444] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[1445] 6. Use of online purchases

[1446] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[1447] Specific examples

[1448] For business meetings

[1449] A user launches the app and schedules an important business meeting for the next day. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the meeting attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[1450] Example prompt:

[1451] "Here's a suggested outfit for tomorrow's important business meeting: navy suit, white shirt, and black shoes. If you don't have these items, you can purchase them at the link below."

[1452] (Link: Example: https: / / www.example.com / product / navy-suit)

[1453] For casual events

[1454] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[1455] Example prompt:

[1456] "Here's an outfit suggestion for a casual weekend event: denim pants, a white t-shirt, and sneakers. I also suggest buying an animal print belt to complete the look."

[1457] (Link: Example: https: / / www.example.com / product / animal-patterned-belt)

[1458] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions according to various user requirements.

[1459] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1460] Step 1:

[1461] The user makes a schedule registration request or a clothing / accessory image upload request through the app. The input is the user's schedule information and image data, which are then sent to the server. The server receives the request and starts the data retrieval process.

[1462] Step 2:

[1463] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app. The input is the user's calendar account information, and the output is the schedule data registered in that account. Based on this, the server determines the date, time, and location of events and meetings.

[1464] Step 3:

[1465] The server acquires the user's location information. The input is the location data of an event or meeting, and the output is the location information corresponding to that location. The location information is used for subsequent data acquisition and analysis.

[1466] Step 4:

[1467] The server uses a weather forecast API (e.g., OpenWeatherMap API) to get weather forecast data for a specific date, time, and location. The input is location information and date and time data, and the output is weather information for that date and time. The server stores this weather information in a database.

[1468] Step 5:

[1469] The user takes a photo of clothing or accessories through the app and uploads it to the server. The input is the image data taken by the user, and the output is the image data stored on the server.

[1470] Step 6:

[1471] The server uses TensorFlow image recognition technology to analyze images of clothing and accessories uploaded by users. The input is image data, and the output is data on the analyzed item's type, color, and characteristics. The analysis results are categorized and stored in a database.

[1472] Step 7:

[1473] The server analyzes the participant information for meetings and events. The input is the participant list obtained from the schedule data, and the output is characteristic data of the participants, such as age, gender, position, and relationship. This data is reflected in coordination suggestions.

[1474] Step 8:

[1475] The server learns from the user's past outfit selection data and understands the user's preferences. The input is past selection and evaluation data, and the output is patterns and trends related to the user's preferences. This enables personalized suggestions.

[1476] Step 9:

[1477] The server comprehensively analyzes the schedule data, location information, weather information, participant characteristics, and user preference data it acquires to generate the optimal coordination for a specific event or meeting. The input is this integrated data, and the output is a specific coordination proposal.

[1478] Step 10:

[1479] The server sends the generated coordination proposal to the user's terminal. The input is the generated coordination data, and the output is the coordination proposal displayed on the user's terminal.

[1480] Step 11:

[1481] The server generates a purchase link for related products based on the suggested outfits and provides it to the user. The input is the outfit suggestion data, and the output is information provided to the user along with the online purchase link.

[1482] Through this series of steps, the system can provide users with optimal outfit suggestions and links to purchase related products.

[1483] 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.

[1484] This invention relates to a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[1485] Overall system configuration

[1486] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[1487] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[1488] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[1489] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[1490] Program processing flow

[1491] server

[1492] 1. Receiving requests from users

[1493] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[1494] 2. Retrieving schedule data

[1495] The server works with the user's calendar app to obtain schedule data.

[1496] 3. Obtaining location information

[1497] The server obtains the user's location information and verifies the location of the event or meeting.

[1498] 4. Storage and analysis of clothing and accessory images

[1499] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[1500] 5. Obtaining weather forecast data

[1501] The server uses a weather forecast API to obtain weather data for the day of the event or meeting, which is then used to select appropriate clothing for the suggested outfit.

[1502] 6. Participant characteristics analysis

[1503] The server analyzes information about participants at events and meetings, identifying characteristics such as age, gender, position, and relationships, allowing it to select appropriate outfits for the occasion.

[1504] 7. Learning your preferences

[1505] The server learns the user's preferences based on past outfit selections and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[1506] 8. Acquiring Emotion Data

[1507] The server obtains the user's emotional data from the emotion engine, which is obtained through facial expression recognition and voice analysis.

[1508] 9. Generating Coordination Proposals

[1509] The server comprehensively analyzes all data and generates the optimal coordination for a specific event or meeting, taking into account the weather, participant characteristics, user preferences, and emotional data.

[1510] 10. Sending outfit suggestions and online purchase links

[1511] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[1512] Terminal

[1513] 1. Launch the app and log in

[1514] The service begins when the user launches the app and logs in.

[1515] 2. Schedule input or synchronization

[1516] Users can enter schedule data using the synchronization function with a calendar app.

[1517] 3. Upload images of clothing and accessories

[1518] Users take pictures of their clothing and accessories and upload them to a server via the app.

[1519] 4. Acquiring Emotion Data

[1520] To capture the user's emotional state, the app's emotion engine function performs facial expression recognition and voice analysis.

[1521] 5. Sending Emotional Data

[1522] The acquired emotion data is sent to the server and reflected in the coordination suggestions.

[1523] 6. Display of outfit suggestions

[1524] The coordination suggestions sent from the server are displayed on the app.

[1525] 7. Review and evaluation of proposals

[1526] The user checks the suggested outfits and, if they like them, prepares them accordingly. They also rate the suggestions and send the rating data to the server.

[1527] 8. Use of online purchases

[1528] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[1529] Specific examples

[1530] 1. For business meetings

[1531] The app begins by a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). When generating suggestions, the server also takes into account the user's emotional data obtained from the emotion engine and recommends a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[1532] 2. For casual events

[1533] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers and enters their schedule. The server retrieves weather data for the weekend (a sunny forecast) using a weather forecast API. The server takes into account the relaxed atmosphere of the event and generates a coordination of denim pants, a white T-shirt, and sneakers, incorporating the user's emotional data obtained from the emotion engine. The user proceeds with preparations based on the suggestions and also receives a link to purchase the animal print belt online.

[1534] In this way, the system of the present invention provides appropriate and efficient coordination suggestions in response to various user requirements, and by combining emotional data, it achieves even more personalized suggestions.

[1535] The processing flow will be explained below.

[1536] Step 1:

[1537] The user launches the app and logs in, which loads the user's account information onto the device.

[1538] Step 2:

[1539] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[1540] Step 3:

[1541] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[1542] Step 4:

[1543] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[1544] Step 5:

[1545] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[1546] Step 6:

[1547] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[1548] Step 7:

[1549] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[1550] Step 8:

[1551] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[1552] Step 9:

[1553] The device uses a built-in emotion engine to analyze the user's facial expressions and voice to recognize their emotions. Emotional data is obtained through facial recognition software and voice analysis software.

[1554] Step 10:

[1555] The device transmits the acquired emotion data to the server, which receives the emotion data and reflects it in its coordination suggestions.

[1556] Step 11:

[1557] The server comprehensively analyzes all data (schedules, clothing and accessory images, weather data, participant characteristics, user preferences, and emotional data) and generates the optimal outfit for a specific event or meeting.

[1558] Step 12:

[1559] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[1560] Step 13:

[1561] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[1562] Step 14:

[1563] Users can prepare the specified items based on the suggested outfits and then click on the relevant online purchase links to purchase the items they need.

[1564] Step 15:

[1565] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[1566] Step 16:

[1567] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[1568] Example 2

[1569] 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."

[1570] In modern society, users want to reduce the time and effort required to choose appropriate outfits in their busy daily lives. Furthermore, determining the optimal outfit is difficult, considering many variables, such as schedules, weather, participant characteristics, personal preferences, and even emotional states. Current systems lack coordination suggestions that comprehensively consider these factors, making it difficult to increase user satisfaction.

[1571] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event participants, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit proposal to the user, means for providing an online purchase link, means for acquiring and analyzing emotion data, and means for adjusting the outfit based on the acquired emotion data. This makes it possible to propose outfits that comprehensively consider various factors related to the user.

[1572] "Users" are the end users of this system, who input schedules, upload images, and check and evaluate coordination suggestions.

[1573] "Schedule data" refers to information about appointments and events that the user inputs or synchronizes with a calendar app.

[1574] "Location Information" refers to information about your current geographic location or the location of an event or meeting.

[1575] "Images of clothing and accessories" refers to photos of clothing and accessories owned by the user, which the system uses for analysis.

[1576] "Weather forecast data" refers to forecast information regarding weather conditions for a particular day or location.

[1577] "Participant characteristics" refers to attribute information such as age, gender, job position, and relationship of participants in an event or conference.

[1578] "User's past selections" refers to the selection history of outfits and their evaluation data.

[1579] "Emotional data" refers to information about a user's emotional state, such as data obtained through facial expression recognition or voice analysis.

[1580] "Online purchase link" refers to a link on the Internet where items related to the generated coordination can be purchased.

[1581] "Coordination" refers to the optimal combination of clothing and accessories that takes into account various factors related to the user.

[1582] "Comprehensive analysis" refers to using the various data acquired to perform a series of calculations and analyses to determine the optimal outfit.

[1583] This invention is a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[1584] Overall system configuration

[1585] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[1586] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[1587] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[1588] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[1589] server

[1590] The server receives schedule registration requests and image upload requests for clothing and accessories sent by users through the app. Specifically, it receives data using a RESTful API. At this time, a calendar synchronization tool such as the Google Calendar API is used to obtain the user's schedule data.

[1591] The server then obtains the user's location via a GPS API to confirm the location of events and meetings. It also uses image recognition software such as OpenCV and TensorFlow to analyze the uploaded images of clothing and accessories, storing the results in a database. During the analysis, specific algorithms are used to identify the type, color, and characteristics of the items.

[1592] Additionally, the server uses weather forecast APIs, such as the OpenWeatherMap API, to retrieve weather forecast data, which provides weather information for a given date, time, and location, and is an important factor in choosing appropriate clothing.

[1593] The server also analyzes information about event and conference attendees to identify attributes such as age, gender, and job position. This allows it to select appropriate outfits for each time, place, and occasion (TPO). It uses machine learning algorithms to learn user preferences based on the user's past outfit choices and evaluation data.

[1594] Emotion data is obtained from the emotion engine through facial expression recognition and voice analysis. Emotion recognition APIs (e.g., Amazon Rekognition and Microsoft Azure's Emotion Recognition API) are used to understand the user's emotional state, and this data is also used as part of the analysis.

[1595] The server integrates multiple data sources and uses a multi-criteria analysis algorithm to comprehensively analyze all data and generate optimal outfit suggestions. The resulting outfits and corresponding online purchase links are then sent to the user's device via push notification or email.

[1596] Terminal

[1597] The user launches the app on their smartphone or PC and authenticates themselves on the login screen. The OAuth 2.0 protocol is used for authentication. The authentication information entered by the user is securely sent to the server, and if authentication is successful, an access token is generated.

[1598] By using the sync function with the calendar app, the user's schedule data is entered, and the app retrieves the latest schedule information and sends it to the server.

[1599] Users take pictures of clothing and accessories and upload them to the server through the app. When uploading, the device displays a preview and allows users to confirm the recognized item information before sending it to the server. To acquire the user's emotional state, the camera and microphone are used to collect facial expressions and voice, which are then analyzed in real time.

[1600] The resulting emotion data is sent to the server and reflected in outfit suggestions. The generated outfit suggestions are displayed in the app, and include detailed descriptions and related online purchase links. Users can rate the suggested outfits using the rating button or feedback form, and the data is sent back to the server.

[1601] Specific examples

[1602] 1. For business meetings

[1603] When a user launches the app and schedules an important business meeting, they upload an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the participants (including senior executives). It also obtains emotional data and suggests outfits for the navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[1604] Example prompt: "User has a business meeting. Upload an image of a navy suit, white shirt, and black shoes. Considering that rain is forecast for the next day, suggest outfit suggestions. Also consider including senior executives, and provide online shopping links for appropriate items."

[1605] 2. For casual events

[1606] When a user plans a casual weekend getaway with friends, they upload an image of their denim pants, white T-shirt, and sneakers and enter their schedule. The server retrieves weather data (sunny weather forecast) for the weekend using a weather forecast API. Reflecting the user's relaxed emotional state, the server suggests coordinating denim pants with a white T-shirt and sneakers. It also provides a link to purchase an animal print belt online.

[1607] Example prompt: "The user is planning a casual evening out. Upload an image of jeans, a white t-shirt, and sneakers. Considering the sunny weather forecast for the weekend, we'd like to suggest some outfit suggestions. To create a relaxed vibe, we'd also like to provide online shopping links for the appropriate items."

[1608] As described above, the system of the present invention comprehensively considers various factors of the user, thereby providing more appropriate and personalized coordination suggestions and increasing user satisfaction.

[1609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1610] Step 1:

[1611] The user launches the app and logs in. They provide authentication information (username and password) as input. The device sends a POST request to the authentication API, and the server authenticates them. If authentication is successful, an access token is issued and the user can use the system.

[1612] (Input) Authentication information (user name, password)

[1613] (Processing) POST request to authentication API, authentication on the server

[1614] (Output) Access token

[1615] Step 2:

[1616] The user synchronizes with a calendar app to provide schedule data. The device then connects to a calendar service such as the Google Calendar API, obtains the schedule data, and sends it to the server.

[1617] (Input) Calendar sync request

[1618] (Process) Obtain schedule data from the calendar service and send it to the server

[1619] (Output) Schedule data

[1620] Step 3:

[1621] Users take pictures of their clothing and accessories and upload them to the server through the app. The device receives the image file, displays a preview, and sends it to the server as a POST request.

[1622] (Input) Images of clothing and accessories

[1623] (Processing) Capture images, display previews, and upload to the server

[1624] (Output) Image file

[1625] Step 4:

[1626] The server analyzes the images of the clothing and accessories it receives, using image recognition algorithms (OpenCV and TensorFlow) to identify the type, color, and characteristics of the item and store them in a database.

[1627] (Input) Image file

[1628] (Processing) Analysis using image recognition algorithms, storage in database

[1629] (Output) Analysis results (item type, color, characteristics)

[1630] Step 5:

[1631] The server uses a GPS API to obtain the user's location, which is then converted into specific location information for the event or meeting via a map service API.

[1632] (Input) Location data (longitude and latitude)

[1633] (Processing) Acquisition of location information and acquisition of location information using map service API

[1634] (Output) Specific location information

[1635] Step 6:

[1636] The server uses a weather forecast API to retrieve weather data for the day of the event or meeting, which then suggests appropriate clothing.

[1637] (Input) Location information, date and time

[1638] (Processing) Request to weather forecast API, obtain climate data

[1639] (Output) Weather forecast data

[1640] Step 7:

[1641] The server analyzes information about participants in events and meetings, and uses the participant list to understand characteristics such as age, gender, and job position, and suggests outfits appropriate for the occasion.

[1642] (Input) Participant list

[1643] (Processing) Analysis of participant information

[1644] (Output) Participant characteristics data (age, gender, job position)

[1645] Step 8:

[1646] The server uses machine learning algorithms to learn the user's preferences based on past outfit selections and evaluation data.

[1647] (Input) Past coordination data, evaluation data

[1648] (Processing) Learning by machine learning algorithms

[1649] (Output) User preference model

[1650] Step 9:

[1651] The server obtains the user's emotional data from the emotion engine, analyzes the data obtained through facial expression recognition and voice analysis, and reflects it in the coordination suggestions.

[1652] (Input) facial expression data, voice data

[1653] (Processing) Analysis using emotion recognition API

[1654] (Output) Emotion data

[1655] Step 10:

[1656] The server comprehensively analyzes all data and generates optimal outfit suggestions, taking into account the weather, participants' characteristics, user preferences, and emotional data.

[1657] (Input) Schedule data, location information, weather forecast data, participant characteristics data, user preference model, emotion data

[1658] (Processing) Comprehensive analysis of data, generation of proposals using multi-criteria analysis algorithms

[1659] (Output) Coordination suggestions

[1660] Step 11:

[1661] The server generates a coordination suggestion and sends it to the user's device along with an online purchase link. The server delivers the suggestion via push notification, email, or other methods.

[1662] (Input) Coordination suggestions

[1663] (Processing) Sending suggestions via push notification or email

[1664] (Output) Notification to user device (coordination suggestions and purchase links)

[1665] Step 12:

[1666] The terminal displays the coordination proposals sent from the server, and the user checks the proposals and sends their evaluation data to the server.

[1667] (Input) Coordination suggestions

[1668] (Processing) Display of coordinated suggestions, evaluation input

[1669] (Output) Evaluation data

[1670] Step 13:

[1671] The user purchases the desired item using the online purchase link included in the suggestion, and the device opens the purchasing site using a browser or an in-app web view.

[1672] (Enter) Online purchase link

[1673] (Processing) Clicking on a purchase link, launching a browser or in-app web view

[1674] (Output) Display of purchase site, purchase procedure

[1675] The above are the specific processing steps for carrying out the invention. This system comprehensively considers various factors of the user and provides personalized coordination suggestions.

[1676] (Application example 2)

[1677] 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."

[1678] This invention relates to a system that collects and analyzes a user's schedule, possessions, and emotional data to suggest outfits suitable for specific situations. Conventional outfit suggestion systems make suggestions based on static data such as the user's possessions and schedule, and therefore are unable to respond to the user's real-time emotions or to the immediate needs of a physical store. As a result, it is not possible to improve user satisfaction.

[1679] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing the characteristics of event participants, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit proposal to the user, means for providing an online purchase link, means for recognizing the user's facial expressions and voice in real time and acquiring emotional data, means for generating and adjusting outfit proposals based on the acquired emotional data, and means for making outfit proposals in a physical store that combine the user's clothing with items in the store. This enables autonomous and dynamic outfit proposals that reflect the user's emotions and real-time situation.

[1680] "User schedule data" refers to information that a user has registered in a schedule or calendar app about their daily activities and plans.

[1681] "User Location Information" means geographic data about a user's current location or a specific point.

[1682] "Images of clothing and accessories owned by the user" are photographs or drawings that contain visual information about clothing, accessories, etc. owned by the user.

[1683] "Means of classifying and storing in a database" refers to a technical method of organizing acquired information by category and storing it in a centralized manner.

[1684] "Weather forecast data" is forecast information about weather conditions in a specific area and at a specific time.

[1685] "Characteristics of event participants" are personal characteristics and attributes such as age, gender, job position, and relationship status of people attending a conference or event.

[1686] "Means of learning from a user's past choices and understanding preferences" refers to an algorithm that predicts and learns a user's preferences and tendencies based on the user's previously selected outfits and evaluation data.

[1687] "A means for generating the optimal outfit for a specific event" refers to a technology that comprehensively analyzes acquired data and suggests clothing and styles suitable for specific situations and occasions.

[1688] The "means for displaying the generated coordination proposal to the user" is a technology for visually presenting the proposed coordination on the screen of the user's device.

[1689] "Means for providing online purchase links" refers to technology that provides links where suggested items can be purchased online.

[1690] "Means of recognizing a user's facial expressions and voice in real time and acquiring emotional data" refers to technology that analyzes a user's facial expressions and tone of voice to identify their current emotional state.

[1691] "Means for generating and adjusting coordination suggestions based on acquired emotional data" refers to technology that takes emotional data into consideration to create and modify coordination suggestions that will satisfy the user more.

[1692] "A means for suggesting outfits in physical stores that combine the user's clothing with items in the store" refers to technology that instantly suggests outfits that combine the clothing the user owns with items sold in physical stores.

[1693] The embodiments of the present invention will be described in detail below.

[1694] Overall system configuration

[1695] This system collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. The system operates in cooperation with a server, terminals, users, and an emotion engine.

[1696] Hardware and software used

[1697] Hardware: Smartphone (iOS / Android device), server (cloud service such as AWS or GCP), camera, microphone

[1698] Software: Calendar API (e.g. Google Calendar), OpenCV, Weather API, Google Cloud Speech-to-Text, Microsoft Azure Face API, Machine Learning Algorithms (TensorFlow)

[1699] Server Operation

[1700] The server works as follows:

[1701] 1. Get user schedule data

[1702] The server obtains the user's schedule data via the calendar API.

[1703] 2. Obtaining location information

[1704] Obtain GPS data to determine the user's location.

[1705] 3. Image Acquisition and Analysis of Clothing and Accessories

[1706] Images uploaded by users are analyzed using OpenCV to identify the item's type, color, and characteristics, and this data is then categorized and stored in a database.

[1707] 4. Obtaining weather forecast data

[1708] Use the weather forecast API to get weather data for a specific day.

[1709] 5. Analysis of participant characteristics

[1710] The age, gender, position, and relationships of participants in meetings and events are retrieved from a database and analyzed.

[1711] 6. Learning your preferences

[1712] It uses machine learning algorithms to learn user preferences based on past outfit choices and evaluation data.

[1713] 7. Acquiring Emotion Data

[1714] It uses the Microsoft Azure Face API and Google Cloud Speech-to-Text to obtain emotion data from the user's facial expressions and voice.

[1715] 8. Coordinate Generation

[1716] The acquired data is comprehensively analyzed to generate the best outfit for a specific situation.

[1717] 9. Submitting your proposal and providing an online purchase link

[1718] The generated outfit suggestions are sent to the user's device and a link to purchase online is also provided.

[1719] Device behavior

[1720] The device (smartphone) operates as follows:

[1721] 1. Launch the app and log in

[1722] Users start the service by launching the app and logging in.

[1723] 2. Schedule Sync

[1724] Sync with the calendar app to get schedule data.

[1725] 3. Upload an image

[1726] Take a picture of your items and upload it to the server.

[1727] 4. Acquiring Emotion Data

[1728] It uses the smartphone's camera and microphone to capture user emotion data.

[1729] 5. Receiving and displaying outfit suggestions

[1730] The coordination proposal sent from the server is displayed and an evaluation is submitted.

[1731] Specific examples

[1732] For business meetings

[1733] A user launches the app and schedules an important business meeting. The user uploads an image of a navy suit and a white shirt. The server uses a weather forecast API to obtain weather data for the next day and analyzes the characteristics of the participants. It also takes into account the user's emotional data obtained from the emotion engine, and recommends a navy suit, a white shirt, and black shoes.

[1734] For casual events

[1735] A user is planning a casual weekend getaway with friends. They upload an image of their outfit, including denim pants, a white T-shirt, and sneakers. The server retrieves weather data for the weekend using a weather forecast API. The server then generates a coordinated outfit for the pair, taking into account the atmosphere of the event and incorporating data from the emotion engine.

[1736] Prompt Sentence Examples

[1737] "For meetings scheduled on your calendar, we'll suggest shirts and ties to pair with your navy suit. Here's the perfect outfit, taking into account today's temperature and the characteristics of the attendees. You can also purchase ties online at the link below."

[1738] By combining these technologies, it is possible to realize autonomous and dynamic coordination suggestions that reflect the user's emotions and real-time situation.

[1739] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1740] Step 1: Retrieve schedule data

[1741] The server obtains the user's schedule data through the API of the calendar app (e.g., Google Calendar) used by the user. This schedule data includes details such as event start time, end time, location, and participants. The input is the calendar data obtained through the API, and the output is the schedule data for analysis.

[1742] Step 2: Obtaining location information

[1743] The server uses GPS data to obtain the user's current location, which allows it to identify the location of events and meetings. The input is the user's location, and the output is the coordinate data of the current location.

[1744] Step 3: Image capture and analysis of clothing and accessories

[1745] The device sends images of clothing and accessories uploaded by the user to the server, which analyzes these images and uses OpenCV to identify the item's type, color, and characteristics. The input is the image uploaded by the user, and the output is data on the owned items, including the analysis results.

[1746] Step 4: Obtaining weather forecast data

[1747] The server uses a weather forecast API to retrieve weather data for a specific day, which is then used to select appropriate outfits for the suggested outfits. The input is the date and location of the event or meeting, and the output is the weather data for that day.

[1748] Step 5: Analyze participant characteristics

[1749] The server retrieves and analyzes information about event and conference participants (such as age, gender, job title, and relationship) from a database. The input is a list of participants, and the output is participant characteristic data.

[1750] Step 6: Learning user preferences

[1751] The server learns user preferences based on past outfit selections and rating data. Here, a machine learning algorithm (e.g., TensorFlow) is used. The input is past selection data and rating data, and the output is a trained model of user preferences.

[1752] Step 7: Obtaining emotion data

[1753] The device uses the smartphone's camera and microphone to acquire emotion data from the user's facial expressions and voice. It then analyzes the data using an emotion engine (e.g., Microsoft Azure Face API or Google Cloud Speech-to-Text). The input is the user's real-time facial and voice data, and the output is analyzed emotion data.

[1754] Step 8: Generate coordinates

[1755] The server comprehensively analyzes all acquired data (schedule, location information, items in possession, weather forecast data, participant characteristics, user preferences, and emotional data) and generates the optimal outfit for a specific situation. The input is these multiple data, and the output is the generated outfit proposal.

[1756] Step 9: Submit your proposal and provide an online purchase link

[1757] The server sends the generated outfit suggestions to the user's device and, if necessary, provides an online purchase link. The input is the generated outfit suggestions, and the output is the outfit suggestions and purchase links displayed on the user's device.

[1758] 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.

[1759] 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.

[1760] 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.

[1761] [Fourth embodiment]

[1762] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1763] 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.

[1764] 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).

[1765] 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.

[1766] 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.

[1767] 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).

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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."

[1775] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[1776] Overall system configuration

[1777] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[1778] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[1779] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[1780] Program processing flow

[1781] server

[1782] 1. Receiving requests from users

[1783] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[1784] 2. Retrieving schedule data

[1785] The server works with the user's calendar app to obtain schedule data.

[1786] 3. Obtaining location information

[1787] The server obtains the user's location information and verifies the location of the event or meeting.

[1788] 4. Storage and analysis of clothing and accessory images

[1789] The server analyzes the uploaded images and classifies and stores them in a database. Image analysis uses image recognition technology to identify the type, color, and characteristics of the item.

[1790] 5. Obtaining weather forecast data

[1791] The server uses a weather forecast API to obtain weather data for the date and time of the event or meeting.

[1792] 6. Participant characteristics analysis

[1793] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[1794] 7. Learning your preferences

[1795] The server learns the user's preferences based on past outfit choices and ratings, allowing it to make more personalized suggestions.

[1796] 8. Coordination Proposal Generation

[1797] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[1798] 9. Sending outfit suggestions and online purchase links

[1799] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[1800] Terminal

[1801] 1. Launch the app and log in

[1802] The service begins when the user launches the app and logs in.

[1803] 2. Schedule input or synchronization

[1804] Users can enter schedule data using the synchronization function with a calendar app.

[1805] 3. Upload images of clothing and accessories

[1806] Users take pictures of their clothing and accessories and upload them to a server via the app.

[1807] 4. Display of outfit suggestions

[1808] The coordination suggestions sent from the server are displayed on the app.

[1809] 5. Review and evaluation of proposals

[1810] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[1811] 6. Use of online purchases

[1812] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[1813] Specific examples

[1814] 1. For business meetings

[1815] The app starts with a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to retrieve weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[1816] 2. For casual events

[1817] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[1818] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions in response to various user requirements.

[1819] The processing flow will be explained below.

[1820] Step 1:

[1821] The user launches the app and logs in, which loads the user's account information onto the device.

[1822] Step 2:

[1823] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[1824] Step 3:

[1825] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[1826] Step 4:

[1827] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[1828] Step 5:

[1829] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[1830] Step 6:

[1831] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[1832] Step 7:

[1833] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[1834] Step 8:

[1835] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[1836] Step 9:

[1837] The server then performs a comprehensive analysis of all data and generates the optimal coordination for a particular event or meeting, taking into account the weather, the characteristics of the participants, and the user's preferences.

[1838] Step 10:

[1839] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[1840] Step 11:

[1841] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[1842] Step 12:

[1843] Users can prepare the specified items based on the suggested outfits, and can also purchase the required items by clicking on the relevant online purchase links.

[1844] Step 13:

[1845] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[1846] Step 14:

[1847] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[1848] Example 1

[1849] 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."

[1850] Conventional systems have difficulty suggesting optimal outfits based on a user's schedule and belongings, and lack the data collection and analysis necessary to provide personalized suggestions. As a result, they are unable to suggest practical clothing and accessories to users, resulting in monotonous outfit choices and often suggesting outfits that do not match the user's preferences. Furthermore, when providing online purchase links, it is difficult to appropriately suggest products that the user is interested in. This invention aims to solve these problems and build a system that provides optimal and personalized outfit suggestions to users.

[1851] 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.

[1852] In this invention, the server includes means for acquiring a user's schedule data, means for acquiring the user's location information, means for acquiring images of the user's clothing and accessories and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing the characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing an online purchase link, means for linking with the database to be used, and means for modeling the user's preferences using a machine learning algorithm, thereby enabling optimal and personalized outfit suggestions based on the user's individual schedule and preferences.

[1853] "User Schedule Data" means information about the dates, times, and details of events and meetings that a User has entered into a calendar application or to-do list.

[1854] "User location information" refers to information about the geographic location based on GPS data, IP address, etc. obtained from the user's device.

[1855] "Clothing and accessory images" refers to photographic images of items such as clothing and accessories owned by the user.

[1856] "Weather Forecast Data" means forecast information about weather conditions for a particular date, time, and location.

[1857] "Participant characteristics" refers to attribute information that indicates the age, gender, position, relationships, etc. of people attending a meeting or event.

[1858] "User's past choices" refers to the outfits the user has chosen in the past and the evaluation data from those outfits.

[1859] "Coordination suggestions" refers to recommendations of clothing and accessory combinations that are determined to be optimal based on the user's schedule and preferences.

[1860] "Online Purchase Link" means a link to a website where a User can purchase a suggested item.

[1861] A "database" refers to a structured collection of data for efficiently storing, managing, and retrieving information.

[1862] "Machine learning algorithms" refer to mathematical models and methods that allow computers to learn patterns and rules from data and apply them to new data.

[1863] "Image recognition technology" refers to technology for identifying specific objects from images and specifying their attributes.

[1864] "API" stands for Application Program Interface, and refers to an interface for sharing functions and data between different software systems.

[1865] "Cloud storage" refers to a service that stores and manages data on a remote server via the Internet.

[1866] This invention relates to a system that collects and analyzes data on a user's schedule and possessions, and proposes outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[1867] server

[1868] The server uses the following hardware and software to collect and analyze user data and generate coordination suggestions.

[1869] Hardware: Cloud servers (e.g., Amazon EC2, Google Cloud Platform)

[1870] software:

[1871] Database: MySQL, PostgreSQL, etc.

[1872] Image analysis: Google Cloud Vision API, Amazon Rekognition

[1873] Weather API: OpenWeatherMap, WeatherAPI

[1874] Machine learning: Scikit-learn, TensorFlow

[1875] Location information acquisition: Google Maps API

[1876] Terminal

[1877] The terminal is used by the user through an application installed on the device (smartphone, tablet, PC, etc.) The terminal has the following functions:

[1878] User schedule data input and synchronization

[1879] Upload images of clothing and accessories

[1880] View and rate outfit suggestions

[1881] Use the online purchase link

[1882] user

[1883] As the final user of the system, the user enters the following data:

[1884] Add a schedule or sync with a calendar app

[1885] Upload images of clothing and accessories

[1886] Check and evaluate outfit suggestions

[1887] Specific operation flow

[1888] 1. For business meetings

[1889] A user launches the app and schedules an important business meeting. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API (e.g., OpenWeatherMap) to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including high-ranking executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[1890] 2. For casual events

[1891] Suppose a user is planning a casual evening out with friends over the weekend. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (sunny weather forecast) using a weather forecast API (e.g., WeatherAPI). The server takes into account the relaxed atmosphere of the event and generates an outfit consisting of denim pants, a white T-shirt, and sneakers. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[1892] Prompt Sentence Examples

[1893] Below is an example of a prompt sentence to input to the generative AI model.

[1894] "I have an important business meeting tomorrow and would like to wear my navy suit, white shirt, and black shoes. The weather forecast predicts rain, and the attendees include my superiors. Please suggest the best outfit based on these conditions."

[1895] "I have a casual evening out with friends this weekend. Can you suggest an outfit that would be suitable for a sunny day using my denim pants, white T-shirt, and sneakers?"

[1896] This invention allows users to receive optimal and personalized outfit suggestions based on their schedules and preferences, enabling them to choose practical and effective outfits.

[1897] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1898] Step 1: Receiving a request from the user

[1899] The server receives requests for schedule information and image uploads of clothing and accessories sent by users through the app, specifically as HTTP or HTTPS requests.

[1900] Input: User schedule information, image data

[1901] Data processing: Request analysis, temporary data storage

[1902] Output: Pending requests

[1903] Step 2: Retrieving schedule data

[1904] The server connects to the user's calendar app via API to retrieve schedule data, securely accessing it using OAuth 2.0 authentication.

[1905] Input: User's OAuth token

[1906] Data processing: Sending API requests and retrieving calendar event data

[1907] Output: Schedule data

[1908] Step 3: Obtaining location information

[1909] The server collects location information from users' devices to identify the location of events and meetings, specifically using GPS data and IP addresses.

[1910] Input: GPS data, IP address

[1911] Data processing: Geographical information analysis, event location identification

[1912] Output: Event location data

[1913] Step 4: Saving and analyzing clothing and accessory images

[1914] The server receives the image data uploaded by the user, stores it in cloud storage, and passes the URL to the image recognition API for analysis.

[1915] Input: Image data

[1916] Data processing: Data storage, image recognition API requests

[1917] Output: Item type, color, and characteristics data

[1918] Step 5: Obtaining weather forecast data

[1919] The server sends a request to the weather forecast API based on the specified date, time and location to retrieve the required weather data.

[1920] Input: Event date, time, and location data

[1921] Data processing: Sending API requests, analyzing climate data

[1922] Output: Weather forecast data

[1923] Step 6: Participant characteristics analysis

[1924] The server obtains participant information from a contact database or social media API, and analyzes it to extract characteristics such as age, gender, and job position.

[1925] Input: Participant contact data, social media data

[1926] Data processing: natural language processing, statistical analysis

[1927] Output: Participant characteristics data

[1928] Step 7: Learning user preferences

[1929] The server uses machine learning algorithms to model the user's preferences based on past outfit choices and evaluation data.

[1930] Input: Past coordination data, evaluation data

[1931] Data processing: training machine learning models

[1932] Output: User preference model

[1933] Step 8: Generate outfit suggestions

[1934] The server comprehensively analyzes the various data it acquires and generates the optimal coordination for a specific event or meeting.

[1935] Input: Schedule data, location information, clothing and accessory characteristics data, weather forecast data, participant characteristics data, user preference model

[1936] Data processing: Comprehensive data analysis and application of proposed generation algorithms

[1937] Output: Coordination suggestions

[1938] Step 9: Send outfit suggestions and online purchase links

[1939] The server sends the generated coordination suggestions and online purchase links for the recommended items to the user's device.

[1940] Input: Coordination suggestions

[1941] Data processing: converting the format of the proposed data and generating a purchase link

[1942] Output: Coordination suggestions, purchase link

[1943] Step 10: Launch the app and log in

[1944] The service begins when a user launches the app and logs in. The login information is authenticated using OAuth or JWT tokens.

[1945] Input: Login information

[1946] Data processing: authentication processing, session creation

[1947] Output: Authentication token, home screen

[1948] Step 11: Schedule or Sync

[1949] Users can input schedule data using the sync function with their calendar app, which is then transferred to the server using an API request.

[1950] Input: Schedule data

[1951] Data processing: Synchronization processing, data format conversion

[1952] Output: Input schedule data

[1953] Step 12: Upload clothing and accessory images

[1954] Users take pictures of their clothing and accessories and upload them to the server via the app, using HTTP POST requests.

[1955] Input: Image data

[1956] Data processing: Sending image data, temporarily saving it on the server side

[1957] Output: Upload completion notification

[1958] Step 13: View outfit suggestions

[1959] The app displays outfit suggestions sent from the server, and a push notification notifies the user of the arrival of the suggestions.

[1960] Input: Coordination suggestions

[1961] Data processing: Analyze the proposed data and convert it into a display format

[1962] Output: Show suggestions, notifications

[1963] Step 14: Review and evaluate proposals

[1964] Users can check the proposed outfits and rate them. The rating data is sent to the server and reflected in the next proposal.

[1965] Input: Coordinate rating

[1966] Data processing: Sending evaluation data and saving it to a database

[1967] Output: Evaluation completion notification

[1968] Step 15: Use online purchases

[1969] Users click on the online purchase link included in the suggestion and purchase the desired item using the in-app browser.

[1970] Enter: Purchase Link

[1971] Data processing: opening links, viewing pages in browsers

[1972] Output: Product purchase page

[1973] This allows users to receive efficient and personalized outfit suggestions, enabling them to quickly prepare and purchase based on those suggestions.

[1974] (Application example 1)

[1975] 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."

[1976] Conventional outfit suggestion systems have difficulty making personalized suggestions by comprehensively utilizing multiple factors, such as the user's belongings, schedule, and the characteristics of the event they are attending. Furthermore, they lack a mechanism for providing online purchase links for products related to the suggested outfits, forcing users to go to the trouble of searching for and purchasing the products. A comprehensive system that can solve these issues is needed.

[1977] 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.

[1978] In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event attendees, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit suggestions to the user, means for providing online purchase links, and means for generating and providing to the user purchase links for related products based on the suggested outfits. This allows the user to not only receive personalized outfit suggestions but also easily purchase the suggested products.

[1979] "Means for obtaining user schedule data" refers to means for obtaining the user's calendar app and other schedule information.

[1980] "Means for obtaining user location information" refers to means for obtaining location information of the user's current location or the location of a specific event.

[1981] "Means for acquiring images of clothing and accessories owned by the user and classifying and storing them in a database" refers to means for acquiring images of clothing, accessories, etc. owned by the user and classifying and storing them in a database.

[1982] The "means for acquiring weather forecast data" is a means for acquiring weather information for a specific date, time and location.

[1983] The "means for analyzing characteristics of participants in a meeting or event" is a means for analyzing characteristics such as age, gender, job position, and relationship of participants.

[1984] "Means for learning the user's past choices and understanding preferences" refers to a means for learning the coordinations that the user has selected in the past and their evaluations, and understanding the user's preferences.

[1985] "Means for comprehensively analyzing acquired data and generating the optimal coordination for a specific event" refers to means for comprehensively analyzing collected schedule data, weather information, participant characteristics, user preferences, etc., to generate the optimal coordination for a specific event.

[1986] The "means for displaying the generated coordination proposal to the user" refers to a means for displaying the generated coordination on the user's terminal.

[1987] The "means for providing an online purchase link" is a means for providing a user with an online purchase link for a product related to the suggested coordination.

[1988] "Means for generating a purchase link for a related product based on a proposed coordination and providing it to a user" refers to means for generating a product link based on a proposed coordination and providing it to a user.

[1989] This invention relates to a system that collects and analyzes data on a user's schedule and the items they own, and suggests outfits suitable for specific situations. This system operates in cooperation between a server, terminals, and users.

[1990] Overall system configuration

[1991] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[1992] Device: Operated through an application installed by the user on a device (such as a smartphone or tablet).

[1993] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[1994] Program processing flow

[1995] server

[1996] 1. Receiving requests from users

[1997] The server receives requests from users to register schedules and upload images of clothing and accessories. AWS EC2 instances are used for this.

[1998] 2. Retrieving schedule data

[1999] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app.

[2000] 3. Obtaining location information

[2001] The server obtains the user's location information and verifies the location of the event or meeting.

[2002] 4. Storage and analysis of clothing and accessory images

[2003] The server analyzes images of clothing and accessories uploaded by users, classifies them, and stores them in Amazon RDS. TensorFlow is used for image analysis.

[2004] 5. Obtaining weather forecast data

[2005] The server uses the OpenWeatherMap API to retrieve weather forecast data for a specific date, time, and location.

[2006] 6. Participant characteristics analysis

[2007] The server analyzes information about participants in events and meetings, taking into account characteristics such as age, gender, position, and relationships.

[2008] 7. Learning your preferences

[2009] The server learns user preferences based on past outfit choices and ratings, using machine learning algorithms.

[2010] 8. Coordination Proposal Generation

[2011] The server comprehensively analyzes the collected data and generates the optimal coordination for a specific event or meeting.

[2012] 9. Sending outfit suggestions and online purchase links

[2013] The server uses the Amazon Product Advertising API to send the generated outfit suggestions to the user's device and also provide online purchase links for the recommended items.

[2014] Terminal

[2015] 1. Launch the app and log in

[2016] A user launches the app and logs in, which starts the service.

[2017] 2. Schedule input or synchronization

[2018] Users can enter schedule data using the synchronization function with their calendar app.

[2019] 3. Upload images of clothing and accessories

[2020] Users take pictures of their clothing and accessories and upload them to a server via the app.

[2021] 4. Display of outfit suggestions

[2022] The coordination suggestions sent by the server are displayed in the app.

[2023] 5. Review and evaluation of proposals

[2024] The user checks the proposed outfits and prepares them as they are desired. The user also rates the suggestions and sends the rating data to the server.

[2025] 6. Use of online purchases

[2026] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[2027] Specific examples

[2028] For business meetings

[2029] A user launches the app and schedules an important business meeting for the next day. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the meeting attendees (including senior executives). The server generates a proposal, recommending a navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[2030] Example prompt:

[2031] "Here's a suggested outfit for tomorrow's important business meeting: navy suit, white shirt, and black shoes. If you don't have these items, you can purchase them at the link below."

[2032] (Link: Example: https: / / www.example.com / product / navy-suit)

[2033] For casual events

[2034] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers, and enters their schedule. The server retrieves weather data for the weekend (forecast for sunny skies) using a weather forecast API. The server generates an outfit for the denim pants, a white T-shirt, and sneakers, taking into account the relaxed atmosphere of the event. The user prepares based on the suggestions and also receives a link to purchase the animal print belt online.

[2035] Example prompt:

[2036] "Here's an outfit suggestion for a casual weekend event: denim pants, a white t-shirt, and sneakers. I also suggest buying an animal print belt to complete the look."

[2037] (Link: Example: https: / / www.example.com / product / animal-patterned-belt)

[2038] In this way, the system of the present invention can provide appropriate and efficient coordination suggestions according to various user requirements.

[2039] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2040] Step 1:

[2041] The user makes a schedule registration request or a clothing / accessory image upload request through the app. The input is the user's schedule information and image data, which are then sent to the server. The server receives the request and starts the data retrieval process.

[2042] Step 2:

[2043] The server uses the Google Calendar API to retrieve schedule data from the user's calendar app. The input is the user's calendar account information, and the output is the schedule data registered in that account. Based on this, the server determines the date, time, and location of events and meetings.

[2044] Step 3:

[2045] The server acquires the user's location information. The input is the location data of an event or meeting, and the output is the location information corresponding to that location. The location information is used for subsequent data acquisition and analysis.

[2046] Step 4:

[2047] The server uses a weather forecast API (e.g., OpenWeatherMap API) to get weather forecast data for a specific date, time, and location. The input is location information and date and time data, and the output is weather information for that date and time. The server stores this weather information in a database.

[2048] Step 5:

[2049] The user takes a photo of clothing or accessories through the app and uploads it to the server. The input is the image data taken by the user, and the output is the image data stored on the server.

[2050] Step 6:

[2051] The server uses TensorFlow image recognition technology to analyze images of clothing and accessories uploaded by users. The input is image data, and the output is data on the analyzed item's type, color, and characteristics. The analysis results are categorized and stored in a database.

[2052] Step 7:

[2053] The server analyzes the participant information for meetings and events. The input is the participant list obtained from the schedule data, and the output is characteristic data of the participants, such as age, gender, position, and relationship. This data is reflected in coordination suggestions.

[2054] Step 8:

[2055] The server learns from the user's past outfit selection data and understands the user's preferences. The input is past selection and evaluation data, and the output is patterns and trends related to the user's preferences. This enables personalized suggestions.

[2056] Step 9:

[2057] The server comprehensively analyzes the schedule data, location information, weather information, participant characteristics, and user preference data it acquires to generate the optimal coordination for a specific event or meeting. The input is this integrated data, and the output is a specific coordination proposal.

[2058] Step 10:

[2059] The server sends the generated coordination proposal to the user's terminal. The input is the generated coordination data, and the output is the coordination proposal displayed on the user's terminal.

[2060] Step 11:

[2061] The server generates a purchase link for related products based on the suggested outfits and provides it to the user. The input is the outfit suggestion data, and the output is information provided to the user along with the online purchase link.

[2062] Through this series of steps, the system can provide users with optimal outfit suggestions and links to purchase related products.

[2063] 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.

[2064] This invention relates to a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[2065] Overall system configuration

[2066] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[2067] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[2068] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[2069] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[2070] Program processing flow

[2071] server

[2072] 1. Receiving requests from users

[2073] The server receives schedule registration requests and requests to upload images of clothing and accessories from users.

[2074] 2. Retrieving schedule data

[2075] The server works with the user's calendar app to obtain schedule data.

[2076] 3. Obtaining location information

[2077] The server obtains the user's location information and verifies the location of the event or meeting.

[2078] 4. Storage and analysis of clothing and accessory images

[2079] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[2080] 5. Obtaining weather forecast data

[2081] The server uses a weather forecast API to obtain weather data for the day of the event or meeting, which is then used to select appropriate clothing for the suggested outfit.

[2082] 6. Participant characteristics analysis

[2083] The server analyzes information about participants at events and meetings, identifying characteristics such as age, gender, position, and relationships, allowing it to select appropriate outfits for the occasion.

[2084] 7. Learning your preferences

[2085] The server learns the user's preferences based on past outfit selections and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[2086] 8. Acquiring Emotion Data

[2087] The server obtains the user's emotional data from the emotion engine, which is obtained through facial expression recognition and voice analysis.

[2088] 9. Generating Coordination Proposals

[2089] The server comprehensively analyzes all data and generates the optimal coordination for a specific event or meeting, taking into account the weather, participant characteristics, user preferences, and emotional data.

[2090] 10. Sending outfit suggestions and online purchase links

[2091] The server sends the generated outfit suggestions to the user's device and also provides online purchase links for the recommended items.

[2092] Terminal

[2093] 1. Launch the app and log in

[2094] The service begins when the user launches the app and logs in.

[2095] 2. Schedule input or synchronization

[2096] Users can enter schedule data using the synchronization function with a calendar app.

[2097] 3. Upload images of clothing and accessories

[2098] Users take pictures of their clothing and accessories and upload them to a server via the app.

[2099] 4. Acquiring Emotion Data

[2100] To capture the user's emotional state, the app's emotion engine function performs facial expression recognition and voice analysis.

[2101] 5. Sending Emotional Data

[2102] The acquired emotion data is sent to the server and reflected in the coordination suggestions.

[2103] 6. Display of outfit suggestions

[2104] The coordination suggestions sent from the server are displayed on the app.

[2105] 7. Review and evaluation of proposals

[2106] The user checks the suggested outfits and, if they like them, prepares them accordingly. They also rate the suggestions and send the rating data to the server.

[2107] 8. Use of online purchases

[2108] Users can click on the online purchase link included in the suggestion to purchase the items they need.

[2109] Specific examples

[2110] 1. For business meetings

[2111] The app begins by a user opening the app and adding an important business meeting to their schedule. The user uploads an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the attendees (including senior executives). When generating suggestions, the server also takes into account the user's emotional data obtained from the emotion engine and recommends a navy suit, white shirt, and black shoes. Taking the user's preferences into account, the server also provides an online purchase link for brown leather shoes.

[2112] 2. For casual events

[2113] Suppose a user is planning a casual weekend getaway with friends. The user uploads an image of denim pants, a white T-shirt, and sneakers and enters their schedule. The server retrieves weather data for the weekend (a sunny forecast) using a weather forecast API. The server takes into account the relaxed atmosphere of the event and generates a coordination of denim pants, a white T-shirt, and sneakers, incorporating the user's emotional data obtained from the emotion engine. The user proceeds with preparations based on the suggestions and also receives a link to purchase the animal print belt online.

[2114] In this way, the system of the present invention provides appropriate and efficient coordination suggestions in response to various user requirements, and by combining emotional data, it achieves even more personalized suggestions.

[2115] The processing flow will be explained below.

[2116] Step 1:

[2117] The user launches the app and logs in, which loads the user's account information onto the device.

[2118] Step 2:

[2119] Users can enter their schedules using the calendar function within the app or sync with a calendar app, which will register their events on their device.

[2120] Step 3:

[2121] Users take pictures of their clothing and accessories and upload them to a server via their device. At this time, photos are taken of each item so that they can be clearly identified.

[2122] Step 4:

[2123] The server receives the uploaded image and uses image recognition software to analyze the item's type, color, and characteristics, storing the results in a database and managing them as part of the user's inventory.

[2124] Step 5:

[2125] The server analyzes the user's schedule data to retrieve details of events and meetings taking place on a particular day, including location information if necessary.

[2126] Step 6:

[2127] The server retrieves weather data for the day of the event or meeting through a weather forecast API, which then selects appropriate clothing for the suggested outfit.

[2128] Step 7:

[2129] The server analyzes information about event and conference participants, identifying their characteristics such as age, gender, position, and relationship, allowing it to select appropriate outfits for the occasion.

[2130] Step 8:

[2131] The server learns the user's preferences based on their past outfit choices and evaluation data, and uses machine learning algorithms to change and reflect the user's preferences.

[2132] Step 9:

[2133] The device uses a built-in emotion engine to analyze the user's facial expressions and voice to recognize their emotions. Emotional data is obtained through facial recognition software and voice analysis software.

[2134] Step 10:

[2135] The device transmits the acquired emotion data to the server, which receives the emotion data and reflects it in its coordination suggestions.

[2136] Step 11:

[2137] The server comprehensively analyzes all data (schedules, clothing and accessory images, weather data, participant characteristics, user preferences, and emotional data) and generates the optimal outfit for a specific event or meeting.

[2138] Step 12:

[2139] The server then sends the generated outfit suggestions to the user's device, including specific combinations of clothing and accessories.

[2140] Step 13:

[2141] The suggested outfits are displayed on the user's device, and the user can review the suggestions and select them if they like them.

[2142] Step 14:

[2143] Users can prepare the specified items based on the suggested outfits and then click on the relevant online purchase links to purchase the items they need.

[2144] Step 15:

[2145] Users rate the suggested outfits and send their ratings to the server, which then learns more about the user's preferences and reflects them in future suggestions.

[2146] Step 16:

[2147] The server analyzes the received rating data and stores it in a database, which is used to ensure that the next recommendation reflects the user's latest preferences.

[2148] Example 2

[2149] 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."

[2150] In modern society, users want to reduce the time and effort required to choose appropriate outfits in their busy daily lives. Furthermore, determining the optimal outfit is difficult, considering many variables, such as schedules, weather, participant characteristics, personal preferences, and even emotional states. Current systems lack coordination suggestions that comprehensively consider these factors, making it difficult to increase user satisfaction.

[2151] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule data, means for acquiring user location information, means for acquiring images of clothing and accessories owned by the user and classifying and saving them in a database, means for acquiring weather forecast data, means for analyzing characteristics of meeting and event participants, means for learning the user's past choices and understanding their preferences, means for comprehensively analyzing the acquired data and generating an optimal outfit for a specific event, means for displaying the generated outfit proposal to the user, means for providing an online purchase link, means for acquiring and analyzing emotion data, and means for adjusting the outfit based on the acquired emotion data. This makes it possible to propose outfits that comprehensively consider various factors related to the user.

[2152] "Users" are the end users of this system, who input schedules, upload images, and check and evaluate coordination suggestions.

[2153] "Schedule data" refers to information about appointments and events that the user inputs or synchronizes with a calendar app.

[2154] "Location Information" refers to information about your current geographic location or the location of an event or meeting.

[2155] "Images of clothing and accessories" refers to photos of clothing and accessories owned by the user, which the system uses for analysis.

[2156] "Weather forecast data" refers to forecast information regarding weather conditions for a particular day or location.

[2157] "Participant characteristics" refers to attribute information such as age, gender, job position, and relationship of participants in an event or conference.

[2158] "User's past selections" refers to the selection history of outfits and their evaluation data.

[2159] "Emotional data" refers to information about a user's emotional state, such as data obtained through facial expression recognition or voice analysis.

[2160] "Online purchase link" refers to a link on the Internet where items related to the generated coordination can be purchased.

[2161] "Coordination" refers to the optimal combination of clothing and accessories that takes into account various factors related to the user.

[2162] "Comprehensive analysis" refers to using the various data acquired to perform a series of calculations and analyses to determine the optimal outfit.

[2163] This invention is a system that collects and analyzes a user's schedule, items in their possession, and emotion data, and suggests outfits suitable for specific situations. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[2164] Overall system configuration

[2165] Server: Responsible for receiving requests from users, collecting and analyzing data, and generating and distributing coordination proposals.

[2166] Device: Operated through an application installed by the user on a device (smartphone, tablet, PC, etc.).

[2167] User: The final user of the system, who inputs schedules, uploads images, and checks and evaluates coordination suggestions.

[2168] Emotion engine: Responsible for recognizing user emotions and providing emotional data.

[2169] server

[2170] The server receives schedule registration requests and image upload requests for clothing and accessories sent by users through the app. Specifically, it receives data using a RESTful API. At this time, a calendar synchronization tool such as the Google Calendar API is used to obtain the user's schedule data.

[2171] The server then obtains the user's location via a GPS API to confirm the location of events and meetings. It also uses image recognition software such as OpenCV and TensorFlow to analyze the uploaded images of clothing and accessories, storing the results in a database. During the analysis, specific algorithms are used to identify the type, color, and characteristics of the items.

[2172] Additionally, the server uses weather forecast APIs, such as the OpenWeatherMap API, to retrieve weather forecast data, which provides weather information for a given date, time, and location, and is an important factor in choosing appropriate clothing.

[2173] The server also analyzes information about event and conference attendees to identify attributes such as age, gender, and job position. This allows it to select appropriate outfits for each time, place, and occasion (TPO). It uses machine learning algorithms to learn user preferences based on the user's past outfit choices and evaluation data.

[2174] Emotion data is obtained from the emotion engine through facial expression recognition and voice analysis. Emotion recognition APIs (e.g., Amazon Rekognition and Microsoft Azure's Emotion Recognition API) are used to understand the user's emotional state, and this data is also used as part of the analysis.

[2175] The server integrates multiple data sources and uses a multi-criteria analysis algorithm to comprehensively analyze all data and generate optimal outfit suggestions. The resulting outfits and corresponding online purchase links are then sent to the user's device via push notification or email.

[2176] Terminal

[2177] The user launches the app on their smartphone or PC and authenticates themselves on the login screen. The OAuth 2.0 protocol is used for authentication. The authentication information entered by the user is securely sent to the server, and if authentication is successful, an access token is generated.

[2178] By using the sync function with the calendar app, the user's schedule data is entered, and the app retrieves the latest schedule information and sends it to the server.

[2179] Users take pictures of clothing and accessories and upload them to the server through the app. When uploading, the device displays a preview and allows users to confirm the recognized item information before sending it to the server. To acquire the user's emotional state, the camera and microphone are used to collect facial expressions and voice, which are then analyzed in real time.

[2180] The resulting emotion data is sent to the server and reflected in outfit suggestions. The generated outfit suggestions are displayed in the app, and include detailed descriptions and related online purchase links. Users can rate the suggested outfits using the rating button or feedback form, and the data is sent back to the server.

[2181] Specific examples

[2182] 1. For business meetings

[2183] When a user launches the app and schedules an important business meeting, they upload an image of a navy suit, white shirt, and black shoes. The server uses a weather forecast API to obtain weather data for the next day (rain forecast) and analyzes the characteristics of the participants (including senior executives). It also obtains emotional data and suggests outfits for the navy suit, white shirt, and black shoes. It also provides a link to purchase the brown leather shoes online.

[2184] Example prompt: "User has a business meeting. Upload an image of a navy suit, white shirt, and black shoes. Considering that rain is forecast for the next day, suggest outfit suggestions. Also consider including senior executiv...

Claims

1. A means for obtaining schedule data of a user; A means for obtaining user location information; A means for acquiring images of clothing and accessories owned by the user, and classifying and storing them in a database; a means for obtaining weather forecast data; A means of analyzing the characteristics of participants in meetings and events; A means of learning from your past choices and understanding your preferences; A means to comprehensively analyze the acquired data and generate the best outfits for a specific event; a means for displaying the generated coordination proposal to a user; a means for providing an online purchase link; A system including:

2. 10. The system of claim 1, further comprising means for utilizing image recognition technology to identify item type, color, and characteristics when analyzing images of a user's clothing and accessories.

3. 10. The system of claim 1, further comprising means for using a weather forecast API to obtain weather forecast data.

Images